AI Features & LLM Integration

AI features your product can afford to run

I add language-model features to existing products: classification, summarization, search over your own data, assistants that call your APIs, and MCP servers that let AI clients work with your product. Every model call is metered, logged and sits behind a provider you can swap.

One workflow first, with its cost per request known before launch

Scope an AI feature How engagements work

What you get

What I deliver

Answers grounded in your data

Retrieval over your documents and records with embeddings and vector search, so the model answers from your data and the sources can be checked.

Cost you can predict

Usage metered by what each request actually costs, checked before the model call and charged only when it succeeds, so a feature cannot quietly outspend its plan.

Agents with limits

Tool-calling agents and MCP servers where every action is logged with its before and after state, reversible where it can be, and held to scoped keys and rate limits.

How it works

Pick

Choose one workflow and how to measure it

Prove

Test quality on your real data before building the interface

Harden

Add metering, logging, evaluation and fallbacks

Ship

Release to real users and watch cost and quality

What's included

LLM features in existing products

Retrieval-augmented generation (RAG)

Embeddings and vector search

Tool-calling agents and MCP servers

Classification and summarization pipelines

Usage metering and cost limits

Evaluation against your real data

Multi-provider model routing

Scope an AI feature

Book a free 30-minute call. I'll ask about the problem and follow up with a written proposal.

Tell me what's stuck Book a Call