AI Integration & LLM Development

AI features and LLM applications, built into real products

LLM integrations, AI agents, voice and document intelligence — built into your product by engineers who shipped an AI news platform to web, iOS and Android.

We've built AI into production: Gnomi, an AI news agent that monitors 180+ countries, summarizes SEC filings and earnings calls, switches between Claude, GPT and Gemini, and runs on web, iOS and Android. And a HIPAA-compliant medical-bill review tool that compares bill formats with an AI engine. That experience is the difference between a demo and a product.

We work with Anthropic's Claude, OpenAI and Google models, plus voice (ElevenLabs) and retrieval systems, and we design for the unglamorous parts: cost control, latency, evaluation, privacy and fallbacks.

Who this is for

Product companies adding assistants, summarization, search or automation to an existing app
Businesses with document-heavy workflows (bills, contracts, applications) that want reliable extraction and review
Startups building an AI-first product that needs a real engineering team, not just prompts
Teams that prototyped something with an LLM and need it made production-grade

What's included

Use-case discovery and feasibility: what the model can reliably do, what it can't, and what it costs
LLM integration (Claude, GPT, Gemini) with prompt design, tool use and structured outputs
Retrieval-augmented generation over your documents and data
Agents and workflow automation with human-in-the-loop controls
Voice and audio features (ElevenLabs, speech-to-text)
Evaluation suites, monitoring and cost/latency optimization
Privacy and compliance design (HIPAA-aware data handling, PII redaction)
Full product build around the AI feature: web, mobile, backend

How it works

Discovery
We identify the highest-value use case and run a quick feasibility spike with real data.
Prototype
A working prototype in 2–3 weeks you can put in front of users.
Productionize
Evaluation, guardrails, cost controls, UI and integration into your product.
Iterate
Monitor quality and cost in production; improve prompts, retrieval and models over time.

Frequently asked questions

Which AI model should we use?

It depends on the task, cost and data sensitivity. We build model-agnostic integrations so you can switch — Gnomi lets users choose between Claude, GPT and Gemini — and we benchmark on your actual data before recommending one.

Can AI features be HIPAA or privacy compliant?

Yes, with the right architecture: data minimization, redaction, vendor agreements and audit logging. We built a HIPAA-compliant medical bill analysis tool following exactly this approach.

How much does an AI integration cost?

A focused feature (summarization, assistant, document extraction) is usually a few weeks of work; AI-first products are scoped like any other product build. Projects typically range from $5,000 to $250,000.

How do you keep AI answers accurate?

Retrieval over your own data, structured outputs, evaluation test sets, and human review where the stakes are high. We measure quality before and after every change.