# Agentic AI Product Build · Surinder Thakur, Dubai > Agentic AI product design and development in Dubai, UAE. I design and build AI products that decide and show their working. ProUX is the proof. From $30,000, proposal within 48 hours. Page: https://www.surinder.design/agentic-ai-product-build ## The problem AI makes designs fast. Not decisions. I design and build AI products that decide and show their working. ProUX is the proof, and yours gets built the same way. - 10 versions in 1 minute. Which one ships? AI made screens cheap. Choosing between them still runs on taste. - A confident answer. No number, no source. A chatbot review reads well and proves nothing. Ask twice, get a different list. - Powerful tools that make you the operator. Pick the specialist, the model, the settings. I built ProUX this way first. People stalled. - 1 platform for every step of the design process. ProUX: research, audits and design decisions in 1 place. Every finding cites a verified guideline and measures what it claims. ## Proof - Live: ProUX: AI that decides from set guidelines, built alone - +67%: YoY revenue at a $1B+ DTC brand - 19 yrs: designing how people decide ## The offer: AI Product Build - From $30,000, per product · fixed scope after the call · 3 to 4 months. - A working product, like ProUX. Not a deck. - 1 senior designs and builds it. No agency layers. - Decision logic: what it decides, when it checks with you - Trust layer: sources, measurements, limits - Working product: built, deployed, live - User research: with real users, before launch - 1 product at a time. 30 minutes. No pitch. Your product, then your scope. Proposal within 48 hours of the call. ## Inside ProUX - STEP 01 · ROUTE: Set the task in plain words. It picks the expert and the model. Every choice named on screen. One click to change it. - STEP 02 · GROUND: Findings from sources, not from memory. A guideline on every claim, 1 tap away. - STEP 03 · MEASURE: It measures the screen. It does not guess. Contrast, tap targets and text size, from the pixels. - STEP 04 · RANK: One score, with its math. Fixes in order. You know what to fix on Monday, and why. - STEP 05 · VERIFY: Paste the fix. It checks your work. A finding it cannot see again is never quietly closed. ## ProUX vs general LLMs - Approach: A general LLM, You prompt (You pick the words and the model.) / ProUX, It picks (The right expert and model, named.) - Numbers: A general LLM, “Looks light” (An adjective. Nothing anyone can check.) / ProUX, 2.43:1 (Measured from pixels, against 4.5:1.) - Sources: A general LLM, “Best practice” (No link. No way to verify it.) / ProUX, Guideline #411 (A source on every claim, 1 tap away.) - Priority: A general LLM, 8 equal tips (No order. You guess what matters.) / ProUX, Ranked fixes (By severity, with points and effort on each.) - Consistency: A general LLM, A new answer (Ask twice, get 2 different lists.) / ProUX, Same rules (One scoring method on every page.) - After the fix: A general LLM, Start over (A new chat, and hope it remembers.) / ProUX, Re-measured (Paste the new screen: “Fixed. Now 5.20:1.”) ## Built twice I designed and built version 1 myself. Then I ran a usability test with 10 people. They liked the answers. They found the product complex, closer to traditional software than to the AI tools they use every day. So I rebuilt ProUX from the ground up, for speed. The engine now makes the choices testers struggled with: you set the task, it picks the expert and the model, runs the method and scores every fix by impact and effort. That is what you hire. I test early, and I rebuild when users say the shape is wrong. ## Your product, next - What does it decide? The decision. In ProUX: Which fix comes first, and what it costs. In your product: The 1 call your users need made. - What does it know? The evidence. In ProUX: Measured pixels and cited guidelines. In your product: Your data, documents and rules. Nothing else. - What must it never do? The guards. In ProUX: It never calls a guess critical. In your product: Limits, approvals and undo, set in code. - Why would people trust it? The interface. In ProUX: A score with its math. A source on every claim. In your product: Screens that show why, and hand over to a person. ## Questions ### Is this a chatbot? No. A chatbot replies to a prompt. ProUX runs a task: it picks the expert, with its curated skills, and the model, applies the ProUX method and verified guidelines, scores each fix by impact and effort, and shows its sources. In your product, the same engine acts within limits set in code, with approval and undo. ### Do you only build design tools? No. ProUX is the one you can try yourself. I build AI products where the AI has to decide and people have to trust it. ### Do you write the code too? Yes. I designed and built ProUX alone, from the screens to the API and the database. Your engineers can take it over, or I keep building. ### How do you stop it making things up? The way ProUX does. It works only from what it was given and labels its guesses. Code sets the limits, not the prompt. ### Which models do you use? The one that fits the job. ProUX runs Claude Sonnet 5, with Voyage for search. Your proposal names the model and the reason. ### What would you not build first? Open chat, and full autonomy. The first version does 1 narrow task well, inside limits set in code, with approval before it acts and undo after. Autonomy grows only where the data shows it is safe. ### What do you need from us? 30 minutes for the call. Then your data, documents and rules for the evidence layer, and 1 person who can say yes. ### Can we see it working? Yes. The call starts with a live ProUX audit on a page you choose. ### Why from $30,000? That is where 1 product starts. Your price is fixed in the proposal, after the call and before any work. ### Is our data safe? Private by default. The proposal names where each piece of data is stored and who can see it. ## Next step Book a 30-minute call on the page, WhatsApp +91 98789 77771, or email hey@surinder.design.