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pixagon

AI Product Development

Design and build AI-powered product features, chat experiences, internal dashboards, and automation tools — engineered like real products, not demos.

What you get
→AI feature design & spec
→Working integrated build
→Evaluation & monitoring setup
→Iteration & handover plan
Who it's for
Founders building AI-enabled products and MVPs
Product teams adding AI features to an existing platform
Companies that need custom internal AI tools
Problems we solve
/A promising AI prototype that cannot become a real product
/AI features that are unreliable or hard to maintain
/No engineering partner who understands both AI and product
What Pixagon does

How we deliver this, end to end.

Product & UX design

We shape the AI feature around a real user need, with clear flows and guardrails.

Model & API integration

Custom integration with the right models and APIs for your use case and budget.

Production engineering

Evaluation, caching, fallbacks, and monitoring so the feature is reliable in the wild.

Iteration

We measure quality and ship improvements as usage and data grow.

Process

How we work — from discovery to support.

01

Discover

Goals, constraints, audience, and success metrics.

02

Design

Architecture, UX flows, and a clear plan.

03

Build

Secure, high-performance, maintainable delivery.

04

Optimize

Speed, SEO, conversion, and analytics.

05

Support

Ongoing improvements and reliable maintenance.

Deliverables
→AI feature design & spec
→Working integrated build
→Evaluation & monitoring setup
→Iteration & handover plan
Technologies
OpenAI Anthropic React Node / Python RAG Postgres
FAQ

Common questions.

How much does it cost to build an AI product or feature?+

Cost depends on the use case, model and infrastructure choices, data and integration work, and the reliability bar for production. We scope a build in phases so you can validate value early before committing to the full feature.

Can you take our AI prototype to production?+

Yes — turning a promising prototype into a reliable, maintainable product with evaluation, monitoring, and fallbacks is exactly what we do, rather than leaving you with a demo that breaks under real usage.

Which AI models do you use — OpenAI, Anthropic, or Google?+

We are model-agnostic and pick per use case across OpenAI, Anthropic, Google, and open models, balancing quality, latency, cost, and data requirements — and we design so you can switch as the landscape changes.

How do you make AI features reliable and prevent hallucinations?+

With retrieval grounding, evaluation suites, guardrails, caching, and fallbacks — plus monitoring in production — so the feature behaves predictably instead of depending on a single prompt.

How long does it take to build an AI feature?+

A focused, well-defined feature can reach a working integrated build in a few weeks; production hardening, evaluation, and iteration follow. We ship incrementally so you see progress early.

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Let's scope your AI product.

Tell us what you're trying to build or improve, and we'll come back with a practical next step.

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