LLM integration
LLM integration in your product
We embed LLMs — OpenAI, Anthropic, or open source via Bedrock and Azure — in your existing products. With structured prompting, evaluation and guardrails that keep the model on track in production.
Built for production
What separates a demo from a product
- Structured output via tool use
- Schema
- Pipeline measuring quality over time
- Eval
- PII redaction and prompt-injection defences
- Guardrails
- Azure OpenAI or Bedrock in Frankfurt
- EU
How we think about LLMs in product
We treat LLMs as software.
Most LLM integrations fall apart in the same place: an impressive demo, an opinion that "we should have AI too", and then a project that dies in the transition to production because the model hallucinates, costs too much, or can't be measured. We build LLM features that survive that transition — because we treat them as software, not magic.
We always start with the use case: what is this a problem a language model actually solves better than a classical solution? Sometimes the answer is "nothing", and we say so. When there's a good use case (summarisation, classification, structured extraction, conversational interface), we build an architecture with structured output, retry and fallback logic, evaluation of quality over time, and management of model costs.
We also take guardrails seriously: prompt-injection protection, PII redaction before the model sees data, output validation and audit logging of what the model said to which user. That's the kind of thing that separates an AI feature you can put in front of your customers from one that should stay an internal experiment.
What we deliver
AI features built to survive the transition to production.
Structured prompting, evaluation, guardrails and spend control — not just a demo.
Use-case assessment and prototype
We start with a week of discovery: what's the problem, which models are candidates, and what's a realistic evaluation metric? You get a working prototype and an honest assessment of whether it's worth building further.
Structured prompting and output
We design prompts, system messages and few-shot examples. Output is validated against a JSON schema (OpenAI structured outputs / tool use) so your backend can trust what comes back.
Retrieval (RAG) when it makes sense
If the model needs to answer based on your own data, we build a RAG layer with embeddings (Voyage, OpenAI, or open source), a vector store (pgvector, Pinecone, Vectorize) and a retriever that can be measured and improved.
Guardrails and security
Prompt injection detection, PII redaction before the model sees data, output moderation (OpenAI Moderation or Lakera), tool-use sandboxing and audit logging on every single model call.
Evaluation and A/B testing
We set up an evaluation pipeline (LangSmith, Braintrust or our own) that scores output on the metrics that matter for your use case. Models, prompts and parameters can be A/B tested in production without going offline.
Cost and latency management
We cache where it makes sense, choose the cheapest model that solves the task well enough, batch requests and monitor spend per feature and per customer — so you know exactly what AI features cost you.
Things to know
Important trade-offs before you start.
Model choice and vendor lock-in
Frontier models (GPT-5, Claude Opus 4.5) deliver quality for demanding tasks, but they're expensive and change more often than you'd like. For production features we typically choose a mix: a frontier model for the hard logic, a smaller/cheaper model for classification and pre-processing. We design the abstraction so you can switch models without rewriting half the code.
Data confidentiality and where data lands
Using OpenAI or Anthropic via their APIs, they promise not to train on your data — but data still passes through their infrastructure. If you have strict data-residency requirements, we run via Azure OpenAI in the EU, AWS Bedrock in Frankfurt, or self-hosted models. We review this thoroughly in discovery.
Hallucinations and evaluation
Language models confidently fabricate things. That's not a bug — it's how they work. We always design under the assumption that output may be wrong: validation, citation requirements (RAG), user feedback loops, and an evaluation pipeline that measures quality over time. Without evaluation you'll never know if a prompt change or model swap made things better or worse.
Operational costs
AI features have variable costs that scale with usage. That means you need to see spend per feature, per customer and over time — otherwise you'll be staring at a surprise bill in three months. We build spend monitoring in from the start and discuss any pricing adjustments if features cost more than they contribute.
FAQ
What people usually ask.
Which models do you typically use?
It depends on the task. For classification, structured extraction and lightweight conversational flows we often use smaller models (GPT-5 Mini, Claude Haiku, Llama on Bedrock) — they're cheap, fast and plenty smart. For complex reasoning, code generation and demanding writing tasks we use frontier models (GPT-5, Claude Opus). We design the abstraction so model choice is a config value, not a rewrite.
Can we use open source models instead?
Yes, where it makes sense. Llama, Mistral and Qwen can run on Bedrock, Together, Groq or your own GPU infrastructure. For many use cases the quality is good enough, and you get predictable cost and full control over data. For others — especially complex reasoning — frontier models are still noticeably better. We measure it concretely for your use case instead of guessing.
What about GDPR and personal data?
We treat it as a requirement from day one. We redact PII before it passes the model where avoidable, choose vendors with EU data residency (Azure OpenAI EU, AWS Bedrock Frankfurt), sign data-processing agreements and log what the model saw on which user. If you have particularly sensitive data (health, finance), we can run self-hosted models on your own infrastructure.
How long does an LLM integration take?
A targeted feature (smart search, summarisation, classification) is typically 4–8 weeks from discovery to production. A larger integration with multiple features, RAG over your own data, evaluation and cost management is typically 2–4 months. We always go live early with a focused version and expand from there — it's the fastest way to find out what actually works for your users.
How do we measure if the AI feature is worth having?
We define success criteria in discovery — that might be conversion rate, support ticket reduction, average task time, or quality score via human review. We set up the measurement from day one and report continuously. If the feature doesn't move the numbers we agreed on, we remove it or redesign — that's better than letting it linger because "it doesn't cost much".
Ready to get started?
Let's have a no-pressure conversation.
We'll get back within one business day with concrete input — not a stock proposal.