What qualifies as "AI integration and development" versus regular automation?

Regular automation connects tools you already have using off-the-shelf logic. AI integration and development is when you need custom intelligence: a RAG system that answers questions from your private knowledge base, an AI agent that handles a multi-step customer workflow, a voice AI that replaces a phone tree, or a custom model fine-tuned on your data. Timelines are 4–8 weeks; typical range is $15,000–$60,000.

Do I need my own data to do RAG?

Yes — that is the point. RAG (retrieval-augmented generation) pulls from your documents, tickets, product docs, policy manuals, or whatever corpus you have. We handle data ingestion, chunking, embedding, vector storage (usually Pinecone or pgvector), and the retrieval pipeline. Typical corpus sizes are 100–50,000 documents.

Is this production-ready code or a prototype?

Production-ready, with proper error handling, logging, monitoring, rate-limit handling, fallback paths, and deployment infrastructure (usually Cloudflare Workers, Vercel, or your existing cloud). Prototypes are faster and cheaper but we will tell you up front if that is what fits the budget. We do not ship prototype code pretending to be production.

Build · 4–8 weeks

AI Integration & Development

Custom AI agents. RAG systems. API integrations.

AI Integration & Development is PerezCarreno & Coindreau's engineering service for businesses that have outgrown off-the-shelf automation and need custom AI software — agents that understand their business context, RAG systems over their own documents, and API integrations connecting their full stack. Engagements typically run 4–8 weeks and $15,000–$60,000, delivered by Armando and a small team with 18+ years of production software experience. Everything we build is version-controlled, documented, and handed over with full ownership.

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Starting at

$15,000

Timeline

4–8 weeks

Best for

Businesses ready for AI-powered products and advanced automation

What's included

Everything you get, in the box.

✓ Technical discovery and architecture design
✓ Custom software build (TypeScript / Python / Node)
✓ LLM integration (OpenAI, Anthropic, or self-hosted)
✓ Vector database setup for RAG use cases
✓ Auth, logging, observability, and cost monitoring
✓ Documentation and runbook for your engineering team
✓ 90 days of post-launch support and bug fixes

Who it's for

Is this the right engagement for you?

Companies with internal data or domain knowledge that off-the-shelf tools cannot reason about

Teams building an AI-powered product feature

Businesses that need an AI agent tied to proprietary APIs or databases

Founders who need a trustworthy senior engineer, not a junior contractor

How it works

4 steps. No surprises.

Architecture

We sketch data flow, model choice, and cost envelope before committing to a build.

Prototype

A working prototype in 1–2 weeks to de-risk the hardest part of the system early.

Production

Hardened build with auth, observability, retries, and cost controls. Deployed to your infrastructure.

Handoff

Docs, runbook, and optional ongoing retainer for improvements and new features.

What you walk away with

Outcomes, not deliverables.

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Custom AI systems that speak your domain

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Production-grade code — not notebooks or demos

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Full IP and ownership — deployed to your cloud

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Predictable LLM costs through caching and routing

Pricing

Starting at $15,000

Typical range $15,000–$60,000 depending on scope. Ongoing retainer available post-launch.

Book a free discovery call See full pricing

FAQ

AI Integration & Development: Questions we hear

What qualifies as "AI integration and development" versus regular automation?: Regular automation connects tools you already have using off-the-shelf logic. AI integration and development is when you need custom intelligence: a RAG system that answers questions from your private knowledge base, an AI agent that handles a multi-step customer workflow, a voice AI that replaces a phone tree, or a custom model fine-tuned on your data. Timelines are 4–8 weeks; typical range is $15,000–$60,000.
Do I need my own data to do RAG?: Yes — that is the point. RAG (retrieval-augmented generation) pulls from your documents, tickets, product docs, policy manuals, or whatever corpus you have. We handle data ingestion, chunking, embedding, vector storage (usually Pinecone or pgvector), and the retrieval pipeline. Typical corpus sizes are 100–50,000 documents.
Is this production-ready code or a prototype?: Production-ready, with proper error handling, logging, monitoring, rate-limit handling, fallback paths, and deployment infrastructure (usually Cloudflare Workers, Vercel, or your existing cloud). Prototypes are faster and cheaper but we will tell you up front if that is what fits the budget. We do not ship prototype code pretending to be production.

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Not a one-time workshop where everyone forgets everything by Friday. We do hands-on training with your actual tools, your actual workflows. Then we stick around for ongoing support.

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Last updated April 2026