UI/UX design & full-stack development for AI-first startups
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AI Product UI/UX
Interfaces for AI-first products — chat, agents, and copilots that feel usable, not experimental
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Agentic Tool Interfaces
Multi-step agent flows with visible state, control, and a way to intervene when it goes wrong
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ML Dashboard Design
Model output turned into charts and decisions instead of raw scores and confidence numbers
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Data & Analytics UX
Complex datasets structured so the insight is visible before the user has to dig
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Prompt & Config Interfaces
Settings, prompts, and parameters designed for people who are not prompt engineers
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AI Features in Existing Products
Adding an AI layer to a live product without breaking the interface people already know
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Trust & Explainability UX
Showing why the model said what it said — sources, confidence, and limits made visible
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AI Landing & Launch Pages
Product sites for AI startups that explain the thing in one scroll, not one demo call
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Full-stack AI Development
React and Next.js builds with model APIs, streaming responses, and real-time state
Projects
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RUST skins & raids — the complete database. Everything the Rust economy needs in one place — skin values, raid data, item tracking
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Match history, team rosters, and player stats for competitive CS2 — built for fans who live in the data
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A case-battle platform wrapped in RuneScape-inspired visuals and mechanics.
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Player and team comparison for CS2, Valorant, Dota 2, and Call of Duty — one dashboard for all stats
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WordPress ACF build for one of South America’s oldest breweries, part of the ANSA McAL Group
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Fractional racehorse ownership, built end-to-end. Custom authorization system, buying shares in each horse, VIP offers, horse catalog
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The affiliate hub for competitive gaming. Case-opening simulator, upcoming-match predictions, live odds and more
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First-generation GGSkins: site design, platform architecture and a library of 20+ custom case packs for a CS skins case-opening platform.
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Financial Education, Simplified. Credit products pulled live via API, ranked by a custom rating system, built on React/Next
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Landing page plus mint experience for a Web3 product, with a distinct animated design per block
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Built from scratch on React/Next — full 3D animations and full-screen scroll storytelling for a DAO product
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- Logo & Visual Identity
- AI Product Identity
- Brand Strategy & Positioning
- Brand Guidelines
- AI Visual Systems
- Model & Feature Naming
- Pitch Deck Design
- Rebranding
Identity that turns an abstract model into a product someone can picture buying, without the same blue gradient as every other AI landing page.
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- AI Product UX
- Agentic Tool Interfaces
- Prompt & Config UI
- ML & Analytics Dashboards
- Design System Development
- UX Audit & CRO
- 3D / Motion Design
- Prototyping & Wireframing
UX for the part users actually judge: what the model produced, how sure it is, and what to do when it is wrong.
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- React / Next.js Development
- Model Endpoint Front-ends
- Streaming & Queue UX
- LLM & API Integrations
- Landing Page & Promo Sites
- Platform & Portal Development
- CRO & Performance Optimization
- Website Maintenance
Front-ends over model endpoints — streaming responses, queues, long-running jobs and every state in between.
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- Dedicated Design Team
- Fixed Monthly Hours Plans
- UI/UX & Full-Stack Dev
- NDA-First Workflow
- Slack & Async Ready
- White-label Delivery
- AI/ML Specialist Pool
- Scale Up / Down Anytime
Your behind-the-scenes design & dev team. We plug into your agency under your brand — NDA signed, Slack-ready, no onboarding drag. Fixed monthly plans: 60 / 120 / 240 hrs.
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AI & ML FAQ
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How much does an AI product project cost?
Fixed-scope work runs from $4,500 for a launch page up to $27,000+ for a full product interface. Ongoing work runs on monthly plans from $2,490. Number of model surfaces and states moves the price, page count does not.
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How long does it take?
A launch or waitlist site is 2 to 3 weeks. A product interface over your model is 2 to 4 months from first wireframe to launch. If you are shipping to a demo date, we build the surfaces you will demo first.
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Do you train models?
No. We design and build everything around the model: the interface, the states, the integration layer. Training, evaluation and infrastructure stay with your ML team — we design against what your endpoint actually returns.
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How do you design for a model that is sometimes wrong?
By treating wrong as a normal state instead of an exception. Confidence, sources, partial answers, retries, edits and an obvious way to correct or reject the output. A product that only designs the correct answer stops being trusted the first week.
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Can you add AI features to a product we already have?
That is a common ask and usually the safer one. We work inside your existing design system, add the model surfaces where they earn their place, and keep the rest of the product recognisable to the people already using it.
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What about latency and streaming?
Streaming, skeletons and progressive output are design decisions, not just engineering ones. Long jobs get a queue and a place to come back to. Nothing that takes eight seconds should look like a button that failed.
































