LLM Integration Services
We bring large language models into your existing product. Proper architecture, cost controls, monitoring, and the engineering discipline to keep it running once the novelty wears off.
The honest version of LLM integration
A lot of LLM features fail in production because the team built a prototype that looked great in a demo and then bolted production on top. The result is a system that costs five times what it should, breaks when the model is updated, and has no way to tell when it's quietly returning garbage.
We start from the production version. Prompts are versioned. Outputs are validated. Failures are caught and logged. Costs are tracked per feature. When the vendor ships a new model, you can roll forward with confidence.
Where LLMs are actually a fit
LLMs are excellent at: classifying unstructured input, summarizing long text, generating draft copy a human will edit, extracting structured data from messy documents, and answering questions over a defined knowledge base.
They are not good at: deterministic math, sourcing facts they weren't given, making decisions you can't audit, or replacing structured systems. We will push back if you're trying to use an LLM for a job that a regex would do better.
What we typically build
Document understanding
Extract structured data from PDFs, contracts, invoices, or freeform forms. Output schema-validated JSON. Human review on low-confidence cases. We've shipped this for legal research, EdTech, and operations workflows.
Agentic workflows
Multi-step reasoning with tool calls. The model decides what to do next based on intermediate results. We're cautious here. Most "agents" people demo are actually fragile pipelines. We build them only when the task genuinely requires planning.
Search and retrieval
Hybrid retrieval combining BM25 and vector search. Re-ranking. Citation extraction. We wrote up our BatasDB approach in detail, linked below.
Drafting and editing
Generate first drafts a human refines. Email replies, marketing copy, internal memos, code review comments. The trick is keeping the human productively in the loop rather than replaced.
What you get from the engagement
A working integration, versioned prompts, eval harness, monitoring dashboard, cost tracking, and documentation. Plus an honest cost forecast so you can budget for the year, not just the demo.
We hand off cleanly. If you want to take it in-house, the documentation and code are clear enough that another senior engineer can pick it up. We don't write lock-in into our deliverables.
Related reading
- AI integration: what business owners need to know
- Why your AI chatbot keeps making things up
- How much does an AI chatbot cost in 2026?
Frequently asked questions
What does an LLM integration project usually involve?
Three things. A use case that's actually a fit for an LLM (most aren't, and we'll tell you). An integration layer that handles prompts, context, retries, and cost. And the monitoring to know when the model is drifting, costing too much, or being prompt-injected. We focus on all three; most agencies stop at the first.
Which LLM should we use: GPT-4, Claude, or open-weight?
It depends on what you're doing. GPT-4 family is strong at structured output and tool use. Claude is stronger at long-context reasoning and instruction following. Open-weight (Llama 3, Qwen, Mistral) is the right answer when data residency, cost at high volume, or air-gapped deployment matters. We benchmark on your actual workload, not a generic leaderboard.
How do you control LLM costs?
Prompt caching where the vendor supports it. Smaller models for routine work, larger models only for hard cases. Aggressive context pruning so we're not sending 8K tokens when 800 will do. Output streaming so users see results sooner even when the total response is long. We've cut bills in half on existing integrations by tightening these levers.
Can you integrate LLMs into our existing stack without a rewrite?
Almost always, yes. LLM integration is usually a new service that sits next to your existing app and exposes a clean API. We don't rewrite your Rails monolith to add a summarization feature. We add a FastAPI sidecar, you call it from your existing code, done.
Do you handle compliance and data residency for LLM work?
When it matters. We've deployed self-hosted models for clients in healthcare and legal who can't ship customer data to OpenAI. We also handle SOC 2 friendly setups using vendors with DPAs in place. Specifics depend on your jurisdiction.
Have an LLM project to scope?
Tell us the use case. We'll tell you whether it's a fit and what the production version looks like.
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