AI Integration Services

Problems we integrate AI into

Most companies don't need an AI product — they need AI inside the product they already have. That reframes the problem from research to engineering, and engineering is our edge: we integrate AI into real production systems, with the auth, data models, and reliability constraints that demos get to ignore.

• Support teams answering the same questions hundreds of times a month
• Products whose search returns keywords instead of answers
• Back offices manually reading, classifying, and re-typing documents
• Sales teams qualifying leads by hand before anyone talks to them

Typical integrations: retrieval-augmented search that answers questions from your documentation and data instead of listing links; document pipelines that extract structured fields from invoices, contracts, and forms into your database; summarization and drafting inside existing workflows; recommendation and classification layers; and support assistants that resolve the repetitive half of your ticket queue while escalating the rest to humans with full context.

What makes integrations succeed is the unglamorous part: evaluation suites that measure accuracy before and after every change, guardrails and human-in-the-loop controls where mistakes are expensive, cost monitoring so token spend doesn't surprise finance, and a swappable model layer across OpenAI, Anthropic, Google, and open-source. Because we build in Laravel, Node.js, React, and mobile stacks daily, the AI lands inside your codebase cleanly — not bolted on through a fragile third-party wrapper.