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Generative UI: what it is, how it works, and when to use it

Generative UI lets AI build the screen each user needs, in real time. What it is, how it works, the trade-offs, and two working demos we built.

Santiago Chiappa

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Jul 17, 2026

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‍Generative UI is a full-stack architecture that lets AI create, modify, and render user interfaces in real time, based on what each user needs at that exact moment. Instead of static, predefined screens, the interface assembles itself on the fly: a bar chart, a table, a comparison card when you're comparing things.

We've been building proofs of concept with it for the past few weeks. Most of what's written about generative UI is either too abstract or too exciting, so this is our attempt at neither: what it is, how it works, where it helps, where it doesn't, and what we learned from two demos we built.

The short version

  • Generative UI means the AI designs the screen that answers your question, not just the answer.
  • In production, most systems don't let the AI write code. It configures pre-built components. Safer, and good enough.
  • It shines in open-ended workflows like reporting and data exploration, where you can't pre-design every screen someone might need.
  • It complements standard UI. It doesn't replace it. Anyone telling you otherwise is selling something.

What is generative UI?

Generative UI is a full-stack architecture: the backend talks to the LLM, decides what the answer should look like, and picks the components, while the frontend renders them and handles how the user interacts with what’s on screen.

Compare that with how interfaces have always worked. A designer decides what goes on each screen, a developer builds it, and every user sees the same thing. Forever, or until the next redesign.

Generative UI flips that. The interface becomes dynamic and personal instead of static and universal. The AI doesn't just answer your question, it designs the screen that answers your question.

Dashboards and reporting are the most common use cases, but they're far from the only one. The same pattern works for dynamic forms, onboarding flows, and customer support, as it takes input just as easily as it presents output. It can even adjust font size, contrast, or layout for users with low vision, color blindness, or cognitive load.

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The three types of generative UI

There are three levels of generative UI, from most constrained to most open (Google Cloud, 2026):

  1. Static. Everything is pre-built. The AI picks which screen to show you from a fixed library. Low risk, low flexibility.
  2. Declarative. The AI assembles a JSON tree that specifies which UI components to use, in what order, with what properties. It doesn't write code. It configures pre-designed widgets. This balances the AI's flexibility with the system's stability.
  3. Open. The AI generates completely new code from scratch and the frontend renders it. Maximum flexibility, maximum risk.

Most production systems today use the declarative approach, and that's what this post assumes from here on. The AI isn't writing HTML or CSS freestyle. It selects components, fills in pre-designed widgets, and composes them into the right screen.

How does generative UI work?

Generative UI works by turning a user request into structured data that describes an interface, then rendering that data as real components. The flow looks like this:

  1. The user asks for something, explicitly or inferred from context.
  2. An LLM analyzes the request. It invokes tools, pulls data, and makes the design decisions: what to show and how.
  3. The system generates structured data describing both the components and the information they'll display.
  4. That schema travels to the frontend through the AG-UI protocol, a standard for communication between agents and frontends. It defines events that keep the agent's state in the backend synchronized with the frontend framework.
  5. The frontend transforms the schema into actual widgets and renders them.

To the user, the result feels like magic. Behind the scenes, it's structured data flowing through a well-defined pipeline. We prefer the second description. It's the one you can build on.

Pros and cons of generative UI

Generative UI trades real personalization and faster development for added latency, inference costs, and less predictable layouts. That's the honest version. Here are the details.

What you gain

Benefit Why it matters
Real personalization Each user sees the view they need, not the view designed for the average user. When that happens, conversion follows.
Flexibility that scales A small set of components combines into thousands of screens, including views you never explicitly built.
Faster development You build the component library once. The system composes it, instead of your team coding endless specific screens.

What you pay for it

Trade-offs What to watch
Latency There's an LLM in the middle, and that adds response time.
Token costs Every generated screen has an inference cost attached.
Less muscle memory The same request won't always render the same layout. Users can't build habits around pixel positions.
Privacy Sending data through an LLM means thinking carefully about what you send and where it goes.

None of these are dealbreakers. There are known techniques to mitigate each one. 

Generative UI examples: two working demos

We built two demos. One with fictional data, one on top of a tool we use every day.

Aurora Goods: a conversational e-commerce dashboard

Aurora Goods is a fictional consumer e-commerce platform we created for the demo. The interface is simple: chat on the left, canvas on the right. You ask about the business, the LLM figures out what you need, pulls the data, and renders it visually.

Ask about 2025 sales and it shows the numbers on cards, with a short note on anything relevant. Ask it to break that down by region and it extends the same view instead of starting over, because it understands the second question builds on the first. This part took us a while to get right, and it's what makes the whole thing feel like a conversation rather than a search box.

The canvas isn't output-only either. You can click into any element and drill down: revenue by category, then inside electronics, then which products sold most.

You configure the widgets once. The system combines them and adds relevant commentary on the spot.

An internal reporting screen for our time-tracking tool

The second demo is closer to home: a generative reporting layer on top of the time-tracking tool we use every day at Kaizen. The questions in this demo are questions someone here has actually asked.

Instead of building dozens of hyper-specific reports, a small amount of code now handles virtually unlimited queries. How many hours were logged in May? Which anomalies showed up in April? How do billable and non-billable hours compare across two months? Who worked on a given project last month, and for how long? Each answer arrives as the right visualization: cards, lists, bar charts, plus a short summary that's easy to scan.

Two details won us over. The LLM suggests next steps, so exploring the data becomes a conversation. And when it's not sure, it asks instead of assuming. Ask for the hours of someone named Alex and, since we have more than one Alex on the team, it asks which one before answering.

Generative UI complements standard UI. That's the point.

Generative UI is a complement, not a replacement. Standard interfaces still win for stable, repetitive workflows where consistency matters. Nobody wants their checkout button to be creative. Generative UI wins where the workflow is complex and the questions are unpredictable.

It also changes what design systems are for. Beyond designing components and screens, teams will need to define semantic rules: how the AI should react to uncertainty, which interfaces match which intentions, and the guardrails that keep generated screens functional and safe.

That's a new kind of design work. And it's already starting.

Want to see generative UI applied to your own data? 

We build working proofs of concept in two weeks. Your data, your workflows, a real thing you can click.

Start a conversation.

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August 27, 2026

Generative UI: what it is, how it works, and when to use it

Generative UI lets AI build the screen each user needs, in real time. What it is, how it works, the trade-offs, and two working demos we built.

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Nov 7, 2025

TIA Technovations 2025 in Phoenix

TIA Technovations 2025 showed that AI is now the cost of entry in logistics, but custom tech is where real differentiation starts.

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The Transportation Intermediaries Association (TIA) Technovations 2025 Conference in Phoenix, AZ, was a highly focused and valuable event. Held at the JW Marriott Phoenix Desert Ridge Resort & Spa, this gathering, while smaller and intimate with an estimate of 200 attendees, was a powerful showcase for the best of logistics technology.

The central theme was clear: Automation and AI are driving the industry forward, and the sheer volume of high-quality, off-the-shelf software available is impressive.

The Technovations Experience

The conference structure was engineered for engagement, primarily through product-focused interaction:

  • Focused Showcases: The agenda featured multiple "Technovator Showcase" segments, where innovative software companies provided quick demos of their solutions.
  • The Kiosk Ecosystem: The Kiosks, which served as the hub for the Networking Breakfast, Lunch, and Evening Reception, were populated exclusively by Software Product companies. This provided an ideal environment for meaningful, quick exchanges between vendors and attendees.
  • Key Industry Insights: Panels stressed that technology evaluation should center on core business metrics: Differentiation, Revenue Increase, Scalability, and Protection.

The Unified Message: AI is the Cost of Entry

The consensus across the keynotes and educational sessions was clear: AI and automation are the cost of entry for survival and scaling.

  • Automation vs. AI: A critical distinction was made: Automation is often a prerequisite for effective AI adoption. The goal isn't just to automate a task, but to use AI to smooth out high-volume, repetitive processes, like reformatting RFPs or triaging a sales team's worst 10 emails for managerial review, as highlighted by an attendee’s high-value use case.
  • The AI Roadmap Dilemma: Many attendees and panelists are struggling with how to start an AI initiative. The advice was to abandon the idea of a massive, costly roadmap and instead focus on a small, measurable Proof of Concept (POC) that targets a specific pain point with clear KPIs. If you can’t measure the ROI, it’s not worth doing.
  • A Word of Caution on Tech Adoption: Panelists stressed that technology failures are overwhelmingly due to the client specifically, their lack of preparation, poor data, or failure to commit to the change management process. They noted that implementing new tech can often feel like taking on a "second job" due to the integration and rollout time required.

The Customization Opportunity: Solving for the Unique

While the product ecosystem is robust, our conversations with leaders across the industry revealed a clear, unmet need:

  • Top-tier TMS providers and logistics operators acknowledged that for many scaling $50M-$250M 3PLs, a standard product simply cannot address the 1-2 unique, complex operational processes that truly drive their differentiation and growth (e.g., highly specialized niches or proprietary client models).
  • For these unique challenges, the best approach is often a tailored technology solution. This isn't about criticizing products; it's recognizing where the product lifecycle naturally reaches its boundary and a custom approach becomes necessary to avoid bottlenecks.

This is the true next step for growth: building proprietary technology that hard-codes a competitive edge directly into the business model.

How Kaizen Softworks Partners for Differentiation

At Kaizen Softworks, we partner with logistics leaders to:

  • Pilot Custom POCs: We build targeted, measurable Proofs of Concept around your unique challenges, allowing you to quickly test and measure ROI on advanced AI or operational automation without the risk of a massive system overhaul.
  • Engineer Proprietary Systems: We create tailored software that enhances your core differentiator, be it a specific carrier-sourcing model, a unique compliance layer, or a specialized "Cradle to Grave" client experience, ensuring your technology is a true competitive weapon.
  • Ensure Alignment: Our focus on cultural fit, time-zone synchronization, and clear communication ensures that partnering on strategic development is smooth, effective, and less likely to feel like a "second job."

TIA Technovations 2025 confirmed that the logistics industry is healthy and focused on innovation. We look forward to working alongside our peers to help freight leaders build the tailored solutions necessary to thrive in the complex, digital landscape ahead.

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Oct 31, 2025

Design Critique Process: Culture & Methodology

Giving feedback on design isn’t always easy. This is how we approach it as a team.

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At Kaizen, design critiques began as a simple idea for peer learning, but quickly became the most valuable part of our UX culture. Our philosophy is to build by co-building. Feedback isn’t something we fear; it’s something we train for. We prefer to face tough input at the right time rather than too late.

We use design critiques to connect, exchange perspectives, and train our critical eye. It's not about one UXer working alone; it’s about bringing diverse viewpoints together to strengthen ideas early, before they go live. We hold these sessions weekly across all projects, from small features to large redesigns.

What is a Design Critique (and How We Run Ours)

A design critique is a structured, collaborative review where peers evaluate a design, prototype, or concept. Its main objective is to gather constructive feedback to identify strengths and weaknesses, leading to significant product improvements.

Our sessions last one hour. One person moderates while others present their work. Each presentation is about 15 minutes, split evenly between context and feedback.

Every UX design critique follows a three-part structure. The core rule is that feedback must be objective analysis (e.g., based on user needs and UX principles) and never personal judgment:

  1. The Critique Itself: Observations, reactions, and questions about the design.
  2. The Theoretical Grounding: The reasoning behind the comment, often supported by heuristics or UX principles.
  3. The Proposed Solution: A suggestion on how to address or refine the issue discussed.

The Dual Purpose of Our Critique Process

What makes the Kaizen design critique process unique is the dual purpose it serves:

  • Training the Muscle of Critique: Every session is both practice and contribution. We grow as designers while simultaneously improving the work for our clients.
  • A Consistent Quality Check: Having many eyes on one piece of work means issues surface before production, improving quality and reducing design debt. This means a client's project benefits from the entire UX team's perspective.

Our golden rule for giving a design critique remains simple: “Critique, explain, propose.” It’s how we stay grounded, theoretical, and solution-oriented at once.

Integrating Critiques into the Workflow

When the critique ends, the feedback doesn't vanish, it's immediately actionable:

  • For open tickets: The designer integrates the insights into the next iteration of the work.
  • For closed work: We record suggestions for future reference and share them with the client if relevant.

Every session becomes part of our collective learning system, building a repository of UX design best practices.

More Than a Routine: Our Ritual of Learning

For us, design critiques are not mere meetings; they are a ritual of learning. They remind us that good design doesn't happen in isolation, and that growth comes from openness, respect, and shared curiosity.

When asked how Kaizen’s UX team achieves consistent quality, we point to this practice: a warm group of people, united by listening deeply, questioning respectfully, and building better designs together.

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Oct 28, 2025

Windsurf Workflows: From Prompt Chaos to Productive Focus

We tried going from prompt to prototype with Windsurf. Here’s how that actually played out.

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Working with AI in its default state is a manual loop: you prompt, get a result, tweak it, and repeat. The core problem is that this approach has no memory or system. A lesson learned in one prompt is forgotten by the next.

This inevitably ends in frustrating loops, where the AI goes beyond what is asked and gets stuck solving the wrong problem while we waste time and energy trying to prompt it back on track.

Breaking the Loop: Creating a Repeatable AI Process

We learned that the agent's effectiveness skyrocketed when we stopped treating it like a single-use tool and started building a system around it by:

  • Enriching the context with clear documentation.
  • Limiting its actions with explicit rules to prevent it from going off-course.

Initially, each developer solved this in their own way, creating custom prompts and conventions. It worked, but it didn't scale. The solution was to systematize this knowledge and turn it into shared workflows and rules using the Windsurf Editor.

The windsurf-devsuite Repo

Our Innovation Hub created a repository that functions as an additional workspace, providing a set of workflows and rules that standardize how we interact with the AI on development projects.

Workflows: The Process Steps

Building a workflow in Windsurf is like creating a smart recipe for your AI assistant. You're giving it the ingredients, the rules, and the step-by-step instructions separately so it can deliver a perfect result every time.

Location: .windsurf/workflows/ These are executed with slash commands and orchestrate the entire flow of a task:

  • /0-task → Initializes the task and sets up tracking.
  • /1-discovery → Analyzes the current state of the code.
  • /2-design → Proposes design options and documents the chosen one.
  • /3-implement → Incremental implementation with logs and validations.
  • /4-clean → Refactoring and cleanup with approval.
  • /5-test → Plans and executes tests.
  • /6-document → Audits and generates technical and user documentation.
  • /status → Shows progress and next steps.

Using Rules as AI Guardrails for Code Quality

Location: .windsurf/rules/

This is where you enforce quality and consistency. You create "rules" that act as non-negotiable guardrails for the AI's output.

global.md 

Guidelines for architecture, TypeScript conventions, use of Tailwind + shadcn/ui, testing, performance, error handling, and the assistant's expected behavior (e.g., do not assume or invent libraries).

devsuite-workspace-paths.md 

This file tells the AI "where" to work. It defines that process tracking and documentation are centralized in docs/development/current-task.md  within the devsuite. This maintains order and traceability.

You can explore all the DevSuite guidelines and workflows directly on our GitHub repository here.

Benefits of Using Windsurf Workflows

Consistency → The same rules applied across all projects.

Standardization → Clear steps and documented outcomes in each workflow.

Traceability → All decisions recorded in a single location.

Productivity → Less friction; the assistant knows what to do at each stage.

Current Status and Next Steps 

Today, we are in the middle of the adoption phase: each team at Kaizen is incorporating the workflow set into their daily routine while we gather feedback to improve it. The goal is for these workflows to evolve with use:

  • Adjusting rules that prove to be too restrictive.
  • Adding steps where they are needed.
  • Refining the generated documentation to make it truly useful.

We want this to become a common and living foundation that empowers every dev, regardless of their seniority, and facilitates collaboration between teams.

In Summary 

We went from "fighting with the AI to make it understand what we want" to having a structured and reliable process where the assistant works with focus, within a common framework, and without endless prompt loops. 

With Windsurf + devsuite, AI has stopped being an isolated experiment and has become a real pillar of our daily operations.

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Oct 23, 2025

The 5 Stages of Logistics Growth: A Supportive Guide to Your Tech Journey

Find where your logistics operation stands today and what tech steps can help you move from daily firefighting to smarter growth.

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Is your logistics operation running smoothly, or does it sometimes feel like a daily firefight? Do you make decisions based on real-time data, or do you rely on gut feelings and the expertise of a few key employees?

Every company is on a unique journey. For many, the challenges come from patchworks of disconnected systems, manual workarounds, or processes that haven’t yet scaled with growth. This isn’t a failure, it’s simply where you are today. The important thing is recognizing opportunities to grow stronger, more efficient, and better connected.

This framework is designed to help you quickly assess where you are today and what opportunities exist to unlock your next stage of growth.

Stage 1: The Reactive Firefight

You may be here if:

  • Spreadsheets, email, and phone calls are your main tools.
  • Processes are manual, inconsistent, and depend heavily on “tribal knowledge.”
  • Data is limited or unreliable, so decisions are often based on instinct.
  • Most time is spent putting out fires instead of planning ahead.

Opportunities to Grow: Reduce inefficiency, errors, and stress by introducing simple, repeatable processes and basic technology tools.

Stage 2: The Functional Silos

You may be here if:

  • You’ve adopted basic systems (like a TMS or accounting software), but they don’t connect.
  • Processes are documented but live in departmental isolation.
  • Data is being captured but remains trapped in separate systems.
  • Miscommunication or rework happens between teams.

Opportunities to Grow: Focus on connecting systems and sharing data to improve collaboration and visibility across teams.

Stage 3: The Integrated Powerhouse

You may be here if:

  • Core systems (ERP, WMS, TMS) are connected and share information.
  • Internal processes are unified and standardized.
  • Automation is in place (e.g., barcode scanning).
  • Reliable KPIs are available to understand performance trends.

Opportunities to Grow: Build efficiency, scalability, and consistency by leveraging integrated systems and measurable results.

Stage 4: The Collaborative Optimizer

You may be here if:

  • You use modern APIs and BI platforms to connect with partners.
  • You have end-to-end visibility across your supply chain.
  • Data helps you understand not just what happened, but why.

Opportunities to Grow: Use collaboration and data insights to strengthen partnerships, anticipate challenges, and improve customer experience.

Stage 5: The Digitized Leader

You may be here if:

  • You’ve built or adopted advanced proprietary tech (TMS, WMS, CRM).
  • AI and Machine Learning help forecast, optimize, and prevent disruptions.
  • Automation drives predictive and proactive decision-making.

Opportunities to Grow: Turn technology into a true strategic advantage by moving from reactive problem-solving to proactive, optimized decision-making.

The Cost of Standing Still

No matter where you are currently, staying stagnant can hold back your growth. Every disconnected process, data-entry error, and missed insight make it harder to compete.

Your Competitors Are Advancing: Companies at later stages are moving faster and more efficiently.

Inefficiency Eats Profit: Manual processes and disconnected systems act like a hidden tax on your margins.

Blind Spots Limit Strategy: Without integrated data, decisions remain educated guesses.

The question isn’t if you should progress, it’s how and when. And the best time to start is now.

Ready to explore your next stage of growth? 

Let’s work together to assess your current systems and design a roadmap that fits your business goals. 

SET UP A FREE CONSULTATION

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Oct 22, 2025

How Our Salary Policy Works at Kaizen

See how we built a transparent salary policy around fairness, market data, purchasing power, and shared decision-making.

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A transparent, decentralized, and human approach to compensation

Let’s face it, salary decisions in most companies feel like a black box. You work hard, get good feedback… and still wonder: Am I being paid fairly? Who decides that, anyway?

At Kaizen, we decided to do things differently.

We built a transparent and collaborative salary policy, one that doesn't rely on negotiations behind closed doors, or a single person pulling the strings. Instead, it's a decentralized, data-informed system that values fairness, context, and sustainability.

Here’s how our salary policy actually works, and why we believe it reflects the culture we’re building every day at Kaizen.

From a Centralized Bottleneck to a Collaborative System

In the early days, our CEO, Bruno, handled all salary reviews. His intentions were fair and well-meaning, but the process just wasn’t scalable. Reviews took time, data was hard to update, and decisions were largely centralized.

So a few Kaizeners decided to change that.

Martin (our Agile Coach), along with Nacho and Eduardo (two of our tech leads), proposed a change. They started codifying the values and principles Bruno used to make decisions and turned them into a transparent, repeatable process the whole company could see.

They focused on four main goals:

  • Standardize the principles that guide compensation.
  • Reduce how long salary reviews take.
  • Replace case-by-case negotiation with proactive, periodic reviews.
  • Open up the process so more people could contribute, not just leadership.

The Four Pillars of Our Compensation Model

We based our salary policy on four clear, concrete pillars. Each one helps us make decisions that are fair, sustainable, and rooted in reality.

1. Market Competitiveness 

We subscribe to trusted market surveys (CPA Ferrere and Búsquedas IT) and build salary bands by role and experience.

Instead of guessing or benchmarking against vague “industry averages,” we aim to land between the 20th and 80th percentile of real-world salaries. This gives us flexibility to reward growth while staying competitive across roles.

2. Real-World Purchasing Power

A salary isn’t just a number, it’s what it can actually buy.

That’s why we track inflation, currency fluctuations, and cost of living. We don’t just raise salaries because it’s time; we raise them when people’s actual purchasing power is impacted. It’s about making sure people can live well, not just look good on paper.

This principle of real-world stability also applies to when you get paid. We pay salaries on the first day of each month. If that day is a holiday or weekend, you get paid on the last business day of the previous month. 

3. Business Sustainability

We’re transparent about the fact that salaries are a big investment, and we need to manage them responsibly.

We track our Gross Margin (revenue minus direct project costs), and aim to keep it around 50% to stay healthy. Every proposed salary adjustment gets run through simulations to see if it keeps us on track.

It’s a way of saying: yes, we care about people, and we also care about keeping this company strong for the long haul.

4. Internal Equity 

Similar work should mean similar pay across different departments, with room to recognize impact.

We use years of experience as our starting point (it’s a reliable guide about 80% of the time), and then enrich that with input from team leads and principals. This helps us identify people who are growing fast and contributing at a higher level, even if they’re earlier in their careers.

How a Salary Review Actually Works

Anyone at Kaizen can request a salary review, for themselves or a colleague. But we also run company-wide reviews twice a year (around March and September), and on every Kaizener’s yearly anniversary.

Here’s what the process looks like:

  1. Update the Data: We bring in fresh market numbers and adjust our salary bands.
  2. Analyze the Business: We review company metrics like Gross Margin and economic indicators.
  3. Evaluate Individually: We assess each person’s place in their band using experience, feedback, and performance.
  4. Run Simulations: Before making any changes, we simulate the financial impact to ensure company health.
  5. Build the Proposal: All proposed changes are put together in one doc.
  6. Final Review: Bruno steps in here for a final pass; not to make top-down decisions, but to ask thoughtful questions and give input.
  7. Communicate: We sit down with each team member and talk through the outcome and reasoning.

Why This Matters

Our salary policy isn’t perfect. But it’s transparent, thoughtful, and built by the people who live it every day.

It reflects who we are as a company: collaborative, transparent, and constantly evolving. And because we value that long-term commitment, we have other ways of showing it. For example, after three years at Kaizen, your personal laptop bought with an allowance benefit, is 100% yours to keep. 

So if you’re someone who values honesty, ownership, and real context behind how things work, you might feel right at home here.

Curious to learn more about life at Kaizen or how we work?

Check out our open roles or reach out to [email protected], we’re always happy to talk.

·

Oct 14, 2025

How Much is Your "Good Enough" Logistics Tech Really Costing You?

Outdated logistics tech may feel “good enough,” but hidden costs in visibility, scalability, and efficiency can quietly hurt growth.

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Is your technology just another expense, or is it your greatest strategic weapon? For many logistics companies, a patchwork of legacy systems and spreadsheets feels "good enough," but this mindset comes with hidden costs that squeeze margins and hand an advantage to your competition.

This article exposes the five key pains of outdated tech and provides a modern playbook to fix them.

The 5 Pains of Outdated Logistics Technology

If you're running on legacy systems, manual processes, or systems that don’t talk to each other, these challenges probably sound familiar. They start as minor frustrations but quickly snowball into major business liabilities.

1. Operational Bottlenecks

This is the most immediate pain. Manual processes, suboptimal routing, and a complete lack of automation lead to excessive transport costs, human error, and wasted time. The direct result is reduced profit margins and an inability to compete on price.

  • Insight: According to industry analysis from ARC Advisory Group, companies implementing a modern TMS can reduce their total freight costs by an average of 6% to 10% through better route planning, load optimization, and carrier selection1.

2. Lack of Visibility Across Operations

Disconnected systems create data silos, making real-time answers impossible. Internally, a lack of centralized data cripples accountability and makes real-time coaching nearly impossible. This can lead to reduced productivity, costly mistakes, and even opens the door to fraud, a massive problem in the industry today. 

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When a client calls for an update, can you give them a real-time answer? A lack of visibility creates a negative customer experience, loss of trust, and cripples internal decision-making.

  • Insight: A 2024 survey by FreightWaves and Descartes found that a staggering 99% of supply chain professionals rate real-time visibility as 'important' or 'very important,' yet many legacy systems fail to deliver this critical capability2.

3. Limited Scalability and Missed Opportunities

Opportunity knocks, but your systems can't answer the door. Your tech should enable growth, not cap it. 

Generic TMS or legacy software was never designed for modern growth. It struggles to keep up with advancements in shipper technology and changing federal and state regulations, which require constant updates to create synergy between systems (especially for EDI setups).

When systems fail during peak periods or require you to hire more staff just to handle a small increase in volume, you're being held back. More specifically, the hidden costs of managing off-the-shelf software become a major financial drain:

  • Support Costs: You're hit with expensive billable hours for "help" and routine software management for your TMS, load automation, and carrier qualification tools.
  • The "Base Package" Trap: Most TMSs lure you in with a cheap base package, but the critical features you need require expensive upgrades, customizations, and modifications.
  • Shared System Rigidity: Some popular platforms are shared between all clients, meaning you're stuck with changes everyone else agrees to, limiting your ability to tailor the tech to your unique business needs.

4. Mounting Competitive Pressure

This pain comes from the outside. You watch as rivals pull ahead with slicker operations, more transparent service, and better pricing. They aren't smarter; they're just better equipped.

Your rivals are gaining an edge by investing in the logistics automation, AI, and proprietary technology that you are not.

5. Security and Compliance Risks

This is the threat that should keep you up at night. Outdated systems lack the modern security protocols required in today’s digital landscape, making them prime targets for data breaches and often fail to meet modern compliance standards.

The real cost is the potential for costly fines, devastating reputational damage, and severe operational disruptions.

The Modern Playbook: Building Your Custom Tech

The solution is a unified logistics platform that serves as a single nerve center for your business. Whether built from the ground up or as a custom hub integrated with existing tools, the path involves four key steps:

  • The Core (Custom TMS): The brain of your operation, centralizing quoting, dispatch, routing, and financials.
  • Integration: Connect your TMS with CRM and finance tools to break down data silos and create a single source of truth.
  • Automation: Automate workflows to eliminate manual tasks and use client portals to provide real-time tracking.
  • The Result: Become a proactive, data-driven operation that makes smarter decisions and delights customers.

Lead or Fall Behind? The Choice is Yours

Investing in modern logistics tech isn't just an upgrade; it's a fundamental business transformation. In today's market, standing still is falling behind as the race for digital leadership accelerates.

That leaves one critical question: Will you invest to lead the pack, or will you risk getting left behind?

Book a free consultation with our team, and let’s diagnose the best path forward for your logistics tech strategy.

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1 ARC Advisory Group, "Transportation Management Systems Market Research Study" (recurring report).

2Descartes Systems Group, "2024 State of the Supply Chain: Taming the Bullwhip Effect" survey, conducted in partnership with FreightWaves.

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Oct 10, 2025

Designing UX Proposals That Survive, Advance, and Deliver

UX Vitals is a simple way to test whether a design proposal has enough user, business, technical, and strategic value to move forward.

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After more than twenty years in design and product, I’ve seen countless proposals fail. Some were brilliant, but didn’t survive the first meeting. Others moved a little further, only to stall in a committee or lose their place in the roadmap.

At some point we realized the problem wasn’t the quality of the design itself, but how the proposal held up across different dimensions. We started looking for patterns in the ones that did succeed. That reflection eventually shaped what we now call UX Vitals.

Mega-fail history: the design nobody asked for

Once we replaced a floating action button with a bottom bar. From a usability standpoint, it looked perfect, more accessible, more visible, more in line with mobile patterns.

Stylized laptop illustration displaying the 'Mega Bottom Bar' concept, featured as a visual case study for designing UX proposals that survive and deliver.

But the new bar came packed with functionality no one had asked for. It solved problems that mattered to us as UXers, not to anyone else. We spent time in debates and presentations, selling the “big change,” while ignoring what we were actually prepared for: aligning different needs and perspectives.

In the end, it was costly to build, added debt, and from a business perspective it moved a few metrics, but nowhere near enough to justify the investment. A proposal polished in usability but weak across the dimensions, and a failure we still cite as a reminder to check UX Vitals before moving forward.

Borrowed principles, reshaped into our system: UX Vitals

UX Vitals borrows from methods like HEART, Lean UX, and the Kano Model, but it’s also shaped by our own practice, what has worked for us and what hasn’t.

What makes it ours is simple: every proposal should stand on at least three of five dimensions, experience, feasibility, impact, cost, scalability, and be framed in the right language for the audience.

That’s why UX Vitals isn't a theory for us. It’s the filter we rely on to check substance before we commit energy.

The 5 dimensions of a solid UX proposal

1. User Experience

Does this change actually improve how people use the product?

It should reduce friction, improve accessibility, and build trust. When this is missing, you may still ship something functional, but the gaps in experience surface quickly, turning usability into a real point of failure.

2. Business Impact

Will it make a measurable difference for the business?

A clearer flow that reduces support tickets, or a smoother checkout that lifts conversion, is what gives a proposal real weight. Without this, even a well-designed solution risks being sidelined, not because it lacks value, but because it fails to connect with what the business is actually driving toward.

3. Technical Feasibility

Can it realistically be built with the stack and the team we have?

For us in UX, this means aligning early with engineering instead of designing castles in the air. If feasibility is ignored, a proposal can look promising at first but soon turns into fragile workarounds or long delays that erode confidence in the design itself.

4. Cost and Effort

Is the value worth the time and energy required?

Part of our job is keeping proposals realistic. That often means shaping them into versions, an MVP first, improvements later. When effort is overlooked, teams can end up chasing over-designed solutions that look impressive on paper but drain resources and push out more impactful work.

5. Scalability

Will this solution still make sense as the product grows?

A pattern that works today should extend across contexts, align with the current design system, and stay open to what the product may need tomorrow. If scalability is overlooked, the design can look elegant in the moment but quickly turns into tomorrow’s bottleneck, creating debt that slows down future progress.

A proposal that balances at least three of these five dimensions has the substance to move forward. But substance alone isn’t enough. How you frame and communicate that proposal often decides whether it gains traction or stalls.

Beyond the dimensions: speaking the right language

You can’t talk technical details with a product owner, just like you can’t talk about long-term vision in a handoff with developers. Each role listens through its own filter, and a good proposal adapts to that reality.

Illustration of a stakeholder requesting to 'Make this button bigger' during a design review for a Mega Bottom Bar, depicting common client feedback challenges in UX proposals.
  • With a PM: “If we introduce this new flow, we can reduce support tickets and keep the release aligned with x goals.”
  • With a Tech Lead: “This flow only requires a minor x adjustment and avoids rework in the checkout logic.”
  • With a Developer in handoff: “Here’s how the new component fits the current design system, so it can be reused without adding custom styles.”
  • With a Designer peer: “The new layout increases clarity in navigation and keeps accessibility contrast ratios consistent.”

The same change framed in four different ways shows how intention and substance only work if they’re spoken in the language of the listener. That’s what turns a proposal from an isolated idea into something the team is ready to move on.

Our Cheatsheet

Here’s the way we review our proposals. A simple checklist we use every time.

User Experience (friction, accessibility, clarity, design)

  • Does it solve a real friction point for the user?
  • Does it make the interaction clearer and more reliable?

Technical Feasibility (stack, dependencies, risks)

  • Can it be built with the current stack and architecture?
  • Does it avoid blocking dependencies or introducing critical risks?

Business Impact (conversion, efficiency, timing, success metrics)

  • Is this the right moment to introduce the change?
  • Does it move or redefine the success metric we actually care about?

Cost and Effort (resources, time, trade-offs)

  • Is it expensive or does it pay off the investment?
  • Is there a simpler version that gives us almost the same benefit?

Scalability (design system, adaptability, long-term debt)

  • Will it hold up over time without creating technical or UX debt?
  • Can it adapt if the product grows or changes context?

Language Fit (audience, framing, clarity)

  • Is the proposal framed in a way that speaks to the right audience?
  • Does it highlight what matters most to them (impact, feasibility, design, etc.)? </aside>

The Lesson Behind UX Vitals

Over the years, I’ve learned that the difference doesn’t come from how clever an idea looks on paper, but from how it holds up across multiple dimensions and how it’s framed for the people who make decisions.

Kaizen’s UX Vitals was born out of that realization: to stop pouring energy into beautiful but fragile ideas, and to focus instead on successful proposals that survive, advance, and deliver real impact.

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Oct 6, 2025

NotebookLM: This AI Is Grounded in Your Documents, Not the Whole Internet

NotebookLM helps you turn your own documents into grounded answers, summaries, and insights without searching the whole internet.

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Whether you're a student juggling research papers, a professional preparing a major presentation, or a creative mind organizing a new project, the challenge is the same: how do you synthesize countless articles, videos, reports, and notes into coherent insights?

What if you had an AI assistant that only read your stuff? An expert that knows your project inside and out and can instantly answer any question you have about it.

That's NotebookLM. Built from the latest Gemini models, it’s a research and thinking partner designed to turn your digital clutter into clarity.

And before you ask: your data is private. NotebookLM does not use your documents, your questions, or its answers to train any AI models.

What is NotebookLM? 

NotebookLM is an AI research assistant that works exclusively with the documents you provide.

Here’s the key difference: unlike general-purpose AI tools that pull answers from the entire internet, NotebookLM becomes an expert on your information and nothing else.

The secret sauce is a technique called Retrieval-Augmented Generation (RAG). In simple terms, this means before NotebookLM answers your question, it first finds the relevant facts directly from your sources. This grounding technique is why its answers are so reliable and it doesn't "hallucinate" or make things up. 

What Can You Do With NotebookLM? A Feature Breakdown

NotebookLM is more than just a chatbot for your files. It’s a full suite of tools designed to help you understand, synthesize, and create new insights from your own material.

1. Upload (Almost) Anything

It all starts with your sources. In NotebookLM, you can upload up to 50 documents per notebook, creating a focused, private "mini-internet" for the AI to work with. By limiting the AI to only your sources, you control the context completely. 

You can upload a wide variety of formats, including:

  • PDFs and Google Docs
  • Website links
  • YouTube video transcripts
  • Google Slides
  • Copied text

2. Get Answers You Can Trust (with Proof)

Once your sources are uploaded, you can start asking questions. The magic of NotebookLM is that every single answer comes with citations, linking you directly back to the exact passage in your source documents.

This is the ultimate fix for AI hallucinations. You never have to guess where an answer came from. You can instantly verify every fact, making it an incredibly reliable tool for researchers, students, and professionals who need accuracy above all else.

3. Instantly Generate Reports

NotebookLM can synthesize your source material into professionally formatted documents, saving you hours of tedious writing. With a single click, you can generate:

  • Briefing Doc: A comprehensive overview of your sources, complete with key insights and summaries. Perfect for getting up to speed on a new project.
  • Study Guide: An invaluable tool for learners that creates quizzes, essay topics, and a glossary of key terms from your notes.
  • Blog Post: A ready-to-publish article that distills the most insightful takeaways from your research into a highly readable format.

4. Visualize Connections with a Mind Map

For visual thinkers, this feature is a game-changer. NotebookLM can automatically generate an interactive mind map that shows the relationships between key topics, people, and ideas across all your source documents.

A mind map helps you see the bigger picture. It can reveal hidden connections and spark new lines of inquiry that you might have missed by just reading the text, making it perfect for brainstorming and strategic thinking.

5. Audio & Video Explainers

Need to review your notes but can't be tied to a screen? NotebookLM can generate a custom podcast-style audio conversation or even a short, narrated video explainer based on your documents.

It can create an audio Q&A, a discussion, or a short video that walks you through the key points, complete with a voiceover. You can listen to a deep-dive on your research or watch a quick summary of your meeting notes, absorbing key information without being glued to the page.

Conclusion

NotebookLM isn't trying to be an all-knowing oracle that has an opinion on everything.

It’s a focused, private tool designed for a single purpose: to help you think better and work smarter with the information that matters most to you. By giving you reliable answers, powerful summaries, and new ways to see your own data, it acts as a true partner for your brain.

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Oct 3, 2025

Nerdearla 2025 Highlights: Lessons from Latin America’s Top Tech Event

Nerdearla 2025 showed how AI, research, and data are reshaping tech, and why the best ideas start with real problems.

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In September, we joined Nerdearla 2025, the largest free science and technology event in Latin America. For five days—September 23–24 online and September 25–27 at Ciudad Cultural Konex in Buenos Aires—over 10,000 in-person attendees and 40,000 online participants explored the future of technology, from artificial intelligence to user experience.

What is Nerdearla and Why It Matters for the Tech Community?

Nerdearla was born with the mission of promoting learning and collaboration within the global tech and open-source community. The event mixes talks, workshops, and networking spaces, with free streaming worldwide and in-person activities.

The 2025 edition featured multiple tracks: Development, Data Science, Product, Infrastructure, Security, AI, Testing, UX, and Soft Skills. This makes it an important event for professionals looking for emerging  trends, inspiration, and community connections.

How We Got There: A Team Initiative

Our participation in Nerdearla 2025 started as an internal proposal from the UX team. After reviewing the idea with company leaders, the delegation was shaped into a group of two designers and one developer, a mix designed to maximize value and capture insights from different perspectives.

This ensured the experience was valuable for each of us and for the team as a whole, aligning with both our company values and our professional growth goals.

Top Nerdearla 2025 Highlights

Here’s a breakdown of the key conversations that dominated the sessions we attended.

Development: The Role of Open Source and AI

  • Many talks focused on the role of open source  (software with publicly available code that anyone can use, modify, and share) and how to integrate it into real projects.
  • Emerging technologies, especially AI, were discussed as enablers for future solutions.

Recommended talk: "Programming is dead. Long live programming!" – Miguel Ángel Durán García

Design: UX Research and Data-Driven Decisions

Product: Applying AI with Purpose 

  • Sessions stressed the importance of evolving practices in the era of AI.
  • Risks of adopting AI without a clear goal: using it as a buzzword or add-on can be worse than not using it at all.
  • Value comes when AI is tied to real problems, generating improvements in productivity, decision-making, and user experience.

Recommended talk: "Build the right product (before writing a single line of code). Validation, purpose, and focus."

One common takeaway was that AI should not be added just for the sake of it but rather used as a tool to solve a real, defined problem.

A Few Talks That Stayed With Us

We’d love to share a few of the sessions we kept talking about long after leaving the room:

Key Takeaways for Our Team and Clients

Some of what we heard confirmed our core beliefs: making decisions without data is like working in the dark; AI is here to stay as a tool to speed up processes and solve problems; and research should be present in every stage to build a successful product.

At the same time, we discovered new perspectives that broadened our view. What stood out most is that our company supports both individual and team growth, and Nerdearla gave us the space to grow together. That growth now flows back into the projects we deliver.

The Future of Tech: Final Thoughts from Nerdearla

Nerdearla 2025 made it clear that combining research, data, and new technologies—especially AI—will continue to shape the industry. For us, it was a moment to validate our approaches, learn from others, and bring fresh ideas back to our work.

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