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AI Integration

AI inside the software you already run.

The wins aren't in another chatbot on a landing page — they're in your product answering from your data, your documents processing themselves, your team's tools drafting the busywork. We wire AI into what you already have.

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In short

AI integration is adding AI capability to software you already have — a support assistant that knows your documentation (RAG), AI features inside your product, document processing, or AI-assisted workflows in your internal tools. FIMM integrates Claude, GPT and open models into production systems, and we build with Claude Code daily — including the RAG-powered assistant running on this very website. You get working AI in your product, not a demo that dies in a notebook.

Claude GPT RAG Claude Code Document AI Guardrails
What we build

What we build.

A

RAG on your documents

Assistants that answer from your manuals, policies and product data — with citations — instead of hallucinating. The assistant on this site runs on exactly this architecture.

B

AI features in your product

Summarization, drafting, search, classification and extraction built into your SaaS or app — designed around your users, priced around your margins.

C

Claude Code development

We build and ship with Claude Code every day — using it to deliver production features faster, and helping teams adopt it into their own engineering workflow.

D

Document processing

Invoices, applications, contracts and reports read, extracted and filed automatically — with confidence thresholds and human review where errors cost money.

E

Internal AI tools

The unglamorous wins: AI drafting in your admin panel, ticket triage, report generation — small features your team uses fifty times a day.

F

Model strategy & guardrails

Which model, what it costs at your volume, what data it may touch, and the evaluation and fallbacks that keep quality from drifting after launch.

How it ships

Scoped, guardrailed, owned.

Automation only earns trust if it's reliable. Here's how we keep it that way.

01

Identify

We find the two or three places in your software where AI pays for itself — and the places it doesn't.

02

Prototype

A working slice against your real data in days, so you judge results — accuracy, speed, cost — not slideware.

03

Integrate

Production build inside your codebase: error handling, rate limits, guardrails, monitoring and cost controls.

04

Evaluate

We measure quality and cost against real usage and tune — models change fast; your integration keeps up.

At a glance

What you can count on.

Claude · GPT
plus open models where they fit
RAG
this site's own assistant runs on it
Daily
we ship with Claude Code ourselves
Frequently asked

Common questions.

What is AI integration?
Adding AI capability to software you already run — an assistant that knows your documents, AI features inside your product, automated document processing, or AI steps in internal tools. It's engineering work: APIs, data pipelines, guardrails and monitoring, not just prompts.
What is RAG and why does it matter?
Retrieval-augmented generation: the AI looks up your actual documents before answering, so it responds from your content — with sources — instead of inventing things. It's the difference between a chatbot that guesses and an assistant you can put in front of customers. This website's own support assistant runs on RAG.
Can you add AI to our existing product or codebase?
Yes — that's most of what we do. We work inside your existing codebase and stack rather than demanding a rebuild, and we've integrated AI into Laravel, React and mobile products, including our own.
What is Claude Code and do you use it?
Claude Code is Anthropic's AI coding agent that works across a real codebase. We use it daily to ship production work — it's part of how a senior team stays fast — and we help other teams adopt it well: where it shines, where it needs review, and how to keep quality high.
Which model should we use — Claude, GPT or open source?
It depends on the task, your data constraints and your volume economics. We benchmark on your actual use case and pick per feature — often a strong model for hard tasks and a cheaper one for high-volume simple ones. You get the math, not a religion.
How do you stop the AI from making things up?
Grounding (RAG with citations), constrained outputs, confidence thresholds, human review on consequential actions, and evaluation sets that catch drift. Hallucination is managed with engineering, not hope — and where the risk is too high, we'll tell you AI isn't the right tool.
What does an AI integration project cost?
A focused first integration — one assistant or one feature, production-ready — is typically a few weeks of senior work; we scope it fixed-price after a short call. The prototype stage comes first precisely so you never bet the full budget on an unproven idea.
Proof

See it in a real build.

Case study Fresh Box FIMM became Fresh Box's end-to-end product and automation partner: web, app, CRM, and the AI agents that run the repetitive work. Read the case →
More services

Explore what else we build.

01

AI Automation

Support agents, lead qualification, and back-office workflows that run themselves — guardrailed, with human escalation.

Explore →
02

Web & SaaS

Customer portals, dashboards, and internal tools built to ship in weeks and scale cleanly.

Explore →
03

Mobile Apps

Native-feeling iOS and Android products, from prototype to store — backend included.

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Let's build
something.

// A 30-minute call · one problem worth solving · a straight answer on fit

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