Artificial Intelligence
LLM Integration Services
Put the capabilities of Claude, GPT, and Gemini inside your product — with the prompt engineering, evaluation, and cost controls production demands.
Adding an LLM feature to a product looks easy — an API call and a text box. Shipping one that's reliable, safe, fast, and affordable at scale is a different discipline. Prompt design, structured outputs, model selection, fallback chains, caching, rate limiting, abuse prevention, and evaluation pipelines are where real LLM engineering happens.
We've integrated language models into products for summarisation, drafting, extraction, classification, translation, and conversation. We're model-agnostic: Claude, GPT, Gemini, and open-weight models like Llama each have strengths, and the right choice depends on your task, latency budget, data constraints, and unit economics.
Just as important: we design for the model landscape changing under you. Clean abstraction layers mean that when a better or cheaper model ships next quarter, you switch with a config change — not a rewrite.
What We Build
LLM Integration: Our Offerings
Feature Integration
Summarisation, drafting, rewriting, classification, and extraction features built into your existing product.
Structured Output Pipelines
LLM outputs your systems can rely on — validated JSON, typed schemas, and retry logic for malformed responses.
Model Selection & Routing
Benchmarks across models on your real tasks, with routing that uses expensive models only where they're needed.
Prompt Engineering & Evals
Versioned, tested prompts with regression suites — so improving one case doesn't silently break another.
Cost & Latency Optimisation
Caching, batching, streaming, and model-size tuning that routinely cuts inference spend significantly.
Safety & Abuse Controls
Input filtering, output moderation, rate limiting, and prompt-injection defence for user-facing AI features.
What You Get
Delivered with Discipline
- LLM features with measured accuracy on your real data, not demo examples
- A model-agnostic architecture you can re-point as the market moves
- Predictable inference costs with monitoring and alerts
- Evaluation suites that run on every prompt or model change
- Documentation your team can build on
Technology
Tools We Work With
Technology choices are made per project — these are the tools we reach for most in llm integration work, and we'll explain the reasoning behind every recommendation.
FAQ
Common Questions About LLM Integration
Which model should we use?
It depends on the task, and the honest answer comes from benchmarking on your data. As a rule of thumb: frontier models for complex reasoning and customer-facing quality, smaller or open-weight models for high-volume classification and extraction. Most mature products end up with a mix, routed by task.
What about our data privacy?
Commercial API providers (Anthropic, OpenAI, Google) offer terms where your data isn't used for training. For stricter requirements we deploy through cloud-provider endpoints (AWS Bedrock, Azure OpenAI, Vertex AI) inside your own cloud account, or self-hosted open-weight models for the most sensitive workloads.
How do you keep costs under control?
Design-stage volume modelling, then engineering: prompt caching, response caching, smaller models for simpler steps, batching, and output-length controls. We set up cost monitoring per feature so spend is never a surprise.
Can you work with our existing engineering team?
Yes — many LLM integration engagements are collaborative. We bring the AI-specific expertise (prompting, evals, model behaviour), your team brings product context, and we hand over a system they fully own.
Discuss Your LLM Integration Project
Tell us what you're trying to achieve and a specialist will get back to you within one business day.
- Free 30-minute consultation
- Quote within 48 hours
- Your idea stays confidential
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