AI Integration
AI Integration Services
We integrate LLMs into your product the right way: grounded in your data, cost-controlled, evaluated, and reliable in production.
The challenge
Demos are easy; production AI is hard. Hallucinations, runaway token costs, prompt fragility, and no evaluation harness are why most AI features never ship. We build AI systems that hold up with real users and real data.
We add real AI to products — the kind that survives contact with production, not a demo that impresses once and breaks on the tenth query. That means grounding models in your own data with retrieval-augmented generation (RAG), building evaluation harnesses so quality is measured rather than hoped for, and controlling token cost as usage grows.
Most AI features fail for predictable reasons: hallucinations, prompt fragility, runaway costs, and no way to tell whether an answer is right. We design around those failure modes from the start — retrieval that cites sources, guardrails, fallbacks, and observability on spend.
We've shipped a legal-contract analysis RAG pipeline over 500–1000 page documents, AI chatbots, and an AI call-analysis system — so the patterns here come from production, not slideware.
Sound familiar?
If any of this rings true, you're in the right place.
Your AI demo impressed everyone once, then hallucinated the moment real users touched it.
Token costs are climbing and you have no idea what's driving them.
You can't actually tell whether the model's answers are right.
How we solve it
Your problem, and exactly how we remove it.
The problem
Generic LLMs make things up on your domain data.
How we solve it
We ground models in your own content with RAG, so answers are retrieved from and cited against your real sources.
The problem
There's no way to know if quality is improving or regressing.
How we solve it
We build an evaluation harness so answer quality is measured on every change, not guessed.
The problem
Costs scale unpredictably with usage.
How we solve it
We tune retrieval, cache aggressively, and pick the right model per task, with observability on token spend.
What we deliver
Scope of work
Tech stack for this service
- OpenAI
- Anthropic
- Next.js
- Node.js
- PostgreSQL
- Supabase
- AWS
Why CodeBaxh
What sets this work apart.
Production AI, not demos
We've shipped a legal-contract analysis RAG pipeline, AI chatbots, and an AI call-analysis system — real systems, real users.
Grounded and evaluated
We ground LLMs in your data with RAG and build evaluation harnesses so quality is measured, not guessed.
Cost-aware by design
Model selection, caching, and prompt optimization keep token costs predictable as you scale.
How we work
How a AI Integration project runs
A calm, visible rhythm from the first call to launch — short loops, weekly demos, and clear updates throughout.
Discovery & feasibility
We pressure-test the use case, your data, and what 'good enough' actually means.
Data & retrieval design
We shape ingestion, chunking, embeddings, and retrieval before touching a prompt.
Build & evaluate
We build the feature alongside an eval set, so quality is measured from day one.
Ship & monitor
We deploy with guardrails, fallbacks, and cost and latency observability.
Proof
Relevant work.
A real project that shows this service in production.
Fixed-scope, retainer, or staff-augmentation engagements available. See engagement models or book a discovery call.
FAQ
AI Integration — FAQs
Retrieval-Augmented Generation (RAG) grounds an LLM in your own data: relevant documents are retrieved via vector search and passed to the model so answers are accurate and cite your sources instead of hallucinating.
We build with OpenAI (GPT) and Anthropic (Claude), and select the right model per task to balance quality, latency, and cost. We can also work with open-source models when self-hosting matters.
Through model selection, caching, prompt compression, retrieval tuning, and observability on token spend — so cost stays predictable as usage grows.
Yes. Most of our AI work integrates into an existing codebase — adding search, chat, extraction, or agent workflows without a rewrite.