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ARANEUX

AI services

LLM & ChatGPT integration

GPT, Claude or Gemini inside your product, answering from your own data, with guardrails you can explain to legal.

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In short

LLM integration puts a model like GPT, Claude or Gemini inside your product. It answers from your own documents through retrieval-augmented generation (RAG) and can only take the actions you approve. We go from a one-week content audit to a live assistant or feature in four to six weeks, with an eval set every release has to pass.

Put a language model in your product without the surprises.

Calling an LLM API takes an afternoon. Making it answer from your data, refuse what it should refuse, stay within budget and behave the same next month takes real engineering.

We've built ChatGPT-based assistants for clients since the API became usable for business. The pattern that works: ground answers in your own content with retrieval-augmented generation (RAG), give the model a few well-defined actions, and test every change against questions your users actually ask.

That covers customer-facing assistants, AI features inside SaaS products, internal search over documents and tickets, and back-office jobs like extraction, classification and summarisation that run without anyone opening a chat window.

Is this for you?

You'll get the most out of this if

  • You want a support or sales assistant that actually knows your product
  • Your team searches through docs, tickets or contracts all day
  • You need an AI feature in your SaaS with a clear cost per user
  • You've had an LLM make something up in front of a customer
  • You need to extract structured data from PDFs, emails or forms

Deliverables

What you walk away with

  1. 01

    Search over your own content

    Docs, tickets, contracts and records indexed with embeddings and hybrid search, so answers come with sources and stay current when the content changes.

  2. 02

    An assistant or feature in your product

    Chat, a sidebar, an API or a background job, whatever fits how your users already work, in Next.js, Laravel, Python or your existing stack.

  3. 03

    Guardrails and privacy

    Permission checks on the server, PII redaction where needed, and clear rules for what the model may say and do. Documented, so security and legal can sign off.

  4. 04

    Cost and quality tracking

    Token spend per user and answer quality over time, visible in a dashboard instead of arriving as a surprise invoice.

Timeline

How the project runs

Typical timings for a first live version. We confirm yours in writing after discovery.

  1. Week 1

    Audit

    What content exists, who should see what, and the twenty questions users ask most.

  2. Weeks 2 to 3

    Prototype on real data

    A working version on your actual content, tested with a few internal users and scored against the eval set.

  3. Weeks 4 to 6

    Go live

    Auth, rate limits, monitoring, fallbacks and the release itself. Then we watch real usage closely.

  4. Ongoing

    Tune

    Prompt, retrieval and model changes, each tested against the eval set before it ships.

Usual stackOpenAI API, Claude API, Gemini, Azure OpenAI, AWS Bedrock, LlamaIndex, LangChain, pgvector, Pinecone, Qdrant, Rerankers, Langfuse, Next.js, Python

Why a small studio

The person who scopes your project is the one who builds it, and the one who picks up when it breaks at 2am.

The founder is on your project
Hussain leads every build himself. You won't brief an account manager who briefs a developer who briefs someone else.
Three people, the output of ten
We build with Claude, Codex and our own agent tooling every day. A small senior team now covers what used to need a department.
We stay after launch
Evals before every release, a log of each AI decision, cost limits and someone on call. Month six matters more to us than demo day.
Your repo, your cloud
The code sits in your GitHub and runs in your AWS, GCP or Azure account from the first commit, on tools your next hire already knows.
Projects shipped
115
since 2014
Clients served
53
US, UK, EU, Middle East, APAC
Average rating
5.0
113 of 113 rated jobs
Years shipping software
18
the founder, since 2008

Case studies

Where we've done this before

FAQ

Questions people ask

Is our data used to train the model?

Not through the business APIs we use. OpenAI, Anthropic, Google, Azure and Bedrock don't train on API data by default. We document exactly what leaves your systems and where it's processed.

Can it run on our own servers?

Yes, when privacy rules require it. Open-weight models like Llama, Qwen or Mistral can run in your cloud account with vLLM or Ollama. They cost more to operate, so we decide based on what the data really needs.

How do you stop it making things up?

It answers only from retrieved sources, cites them and says when it doesn't know. We test for hallucination specifically, with questions designed to tempt it, before launch and after every change.

What is RAG and do we need it?

Retrieval-augmented generation means fetching the relevant parts of your own content and giving them to the model with each question. If the assistant needs to know your products, policies or documents, you need it.

What does it cost to run?

It depends on volume and model choice. During the audit you get an estimate per thousand conversations, and we design to a budget, using smaller models and caching where quality allows.

Keep exploring

Start here

Let's scope yours.

Send a few lines about the workflow or product. You'll hear back from Hussain within a working day with questions, a rough shape and a ballpark.

Book a free callhello@araneux.com