Service

AI Integration

Consultancy and implementation for LLM copilots, RAG, agents, and production AI workflows in your product.

I help you decide where AI creates leverage, then integrate it into your product — copilots, RAG search, agents, and automation built for production, not demos.

Get started Book a call

Typical timeline

1

AI strategy & scoping

2-3 days
2

Architecture & model choice

1 week
3

Integration & development

2-4 weeks
4

Harden & handoff

1 week

Benefits

Why teams choose this

Strategy through ship

Integration plan, architecture, and hands-on implementation in one engagement — not a slide deck you never use.

Production-ready AI

Streaming, tool use, guardrails, evals, and fallbacks so features hold up with real users.

Grounded in your data

RAG and knowledge systems that answer from your docs, tickets, and internal sources.

AI Integration Consultancy & Implementation

I help teams decide where AI belongs in the product, then ship it — copilots, RAG, agents, and automation that run in production, not just demos.

What we typically integrate

LLM copilots & chat

In-product assistants with streaming, memory, tool use, and guardrails.

RAG & knowledge bases

Answers grounded in your docs, tickets, and internal data.

Agents & automation

Multi-step workflows that take action across your tools and APIs.

AI inside your product

Features shipped with auth, billing, and the rest of your stack.

Consultancy plus implementation

Strategy without delivery stalls. Delivery without strategy wastes budget. Engagements cover both: a clear integration plan, then working software in your codebase.

Map high-leverage AI use cases against your data and constraints
Choose models, retrieval, and evaluation approaches that fit production
Implement in your stack with weekly demos and fixed scope
Hand off docs, runbooks, and a support window

Example work

Recent projects in this space.

AI Content Generator

Web app that generates marketing content using GPT-4 with custom prompts and templates.

React OpenAI API Node.js MongoDB

Smart Analytics Dashboard

AI-powered dashboard that provides intelligent insights and predictions from business data.

Next.js Python TensorFlow PostgreSQL

Tech stack

Tools I use for this type of work.

OpenAI API

Anthropic

Python

React

Node.js

PostgreSQL

How it works

A clear process from kickoff to launch.

1
AI strategy & scoping

Map use cases, data, constraints, and success metrics. Agree on a fixed implementation plan.

2-3 days
2
Architecture & model choice

Select models, retrieval approach, tool APIs, and eval strategy for your stack.

1 week
3
Integration & development

Build copilots, RAG, or agents into your product with robust APIs and UI.

2-4 weeks
4
Harden & handoff

Evals, monitoring, docs, and a support window so your team can own it.

1 week

Common questions

About ai integration.

What does AI integration consultancy include?

Use-case mapping, model and architecture recommendations, data readiness, risk/guardrail planning, and a fixed-scope implementation plan your team can execute — or that I implement with you.

What can you implement?

LLM copilots and chat, RAG over your docs and tickets, multi-step agents with tool use, workflow automation, and AI features inside new or existing products.

Do you work with existing AI services or custom models?

Both. Most production work starts with OpenAI, Anthropic, or similar APIs for speed, with custom retrieval and tooling. Custom models when privacy or domain needs require it.

How do you keep AI reliable in production?

Evals, validation sets, fallbacks, rate limits, monitoring, and clear failure modes — so the feature degrades gracefully instead of hallucinating into user-facing bugs.

Ready to get started?

Let's talk about your ai integration project, fixed scope, weekly demos, no surprises.

Start your project Free consultation

Years building production code

5M+

Daily active users at Blitz.gg

YC W20

Ex-CTO, ToDesktop

4–12 wk

Typical engagement