AI & LLM Integration
Embed AI into your products and workflows — measured in weeks, not quarters.
What this looks like in production
Most AI projects fail not because the model is wrong, but because no one set up the eval loop, observability, and release gates that let you iterate confidently. We treat AI integration as a software-engineering problem first.
A typical engagement includes:
- A retrieval layer tuned for your corpus — chunking strategy, embedding model, reranking, hybrid search where it helps.
- An inference pipeline that handles prompt versioning, model routing, tool calls, and structured output.
- A review interface for the cases the model isn’t confident on — humans-in-the-loop where the cost of a wrong answer matters.
- Evals as code, run on every prompt change, model upgrade, or corpus update. Without these, you’re flying blind.
- Cost + latency observability so you catch a 10× regression before your CFO does.
When this fits
You have a use case where a smart-enough generator over your context could replace a slow manual workflow — and you need it to work reliably, not just demo well. Common shapes: support copilots, internal knowledge search, document extraction, agentic workflows over your tools.
Questions about AI & LLM Integration.
Calling an API is the easy part. The hard part is retrieval quality, evals that catch regressions, latency under load, cost control as you scale, and a human review loop for cases the model gets wrong. We build the system around the API call.
Yes. We've shipped systems on GPT-4o, Claude, Llama 3, Mistral, and Mixtral. We pick based on quality requirements, latency budget, and data residency — not vendor preference.
A focused RAG implementation over a defined corpus is typically 4–8 weeks from kickoff to production. The shape: 1 week discovery, 2–4 weeks build + eval iteration, 1–2 weeks deployment and handover.
We start with a strong prompt + retrieval baseline because that solves most use cases. If evals show fine-tuning is the right call, we run that as a separate workstream — synthetic data generation, training run, eval gates, and deployment.
Ready to talk about AI & LLM Integration?
Tell us about your project. We respond within 24 hours.
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