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LLM Integration
You have a working product. You want to add AI features, a summarisation button, smarter search, automatic classification, or a chat assistant, without rebuilding from scratch. That is what this service covers.
We scope the integration, benchmark the right model for your use case, design and test the prompts, integrate with your codebase, and set up cost monitoring before launch. You get a feature that works and a cost model that won't surprise you.
Tell us which feature you want to add.
Seven components in every LLM integration engagement. The ones that get skipped most often (cost monitoring, fallback strategy, caching) are the ones that cause problems six months after launch.
We map the exact feature you want to add (what the model receives, what it returns, where it fits in your existing data flow) before touching a line of code.
We benchmark two to four candidate models against your actual prompts and data before committing. We optimise for accuracy, latency, and cost, not vendor preference.
We write, version, and test prompts systematically. Every prompt gets a test suite covering expected outputs, edge cases, and failure modes.
Full integration into your existing codebase: auth, error handling, retry logic, streaming where appropriate. We follow your existing code conventions.
Token usage dashboards and spend alerts so a prompt engineering change or traffic spike doesn't result in a surprise invoice. We set thresholds before launch.
What happens when the API is down, slow, or returns an error? We design fallback behaviour (cached responses, degraded functionality, or graceful error messages) so your product doesn't break.
Semantic caching for repeated or near-identical queries reduces API costs and improves response time for common prompts. We implement and tune this based on your query patterns.
These are the feature types we integrate most frequently. If yours isn't here, describe it, most text-in, text-out problems fit this service.
Long documents, support tickets, or meeting transcripts condensed into structured summaries. Useful in support, legal, and operations tools.
Tag, route, or categorise text inputs: support tickets by topic, leads by intent, documents by type. More accurate than rule-based approaches on messy real-world text.
Pull structured data from unstructured text: names, dates, contract terms, product details. Replaces brittle regex pipelines with something that handles variation.
Draft emails, product descriptions, reports, or other templated content. Works best when the output has clear structure and the inputs are consistent.
A scoped chat interface embedded in your product, for onboarding, documentation lookup, or guided workflows. Not a general-purpose chatbot.
Replace keyword search with a model that understands what the user means rather than matching exact terms. Especially useful on product catalogues, knowledge bases, and support content.
Wiring a model to an API and getting a good result on the first try is an afternoon's work. The difference between that and a feature you can ship is four things that never show up in the demo and always show up in production.
An evaluation set, so changes are measured. A prompt that works on three test inputs can break on the fourth, and without a test set you find out from users. We build a labeled set from real inputs and run it on every prompt or model change, so you are improving the feature on evidence instead of changing it on a hunch.
Structured output that your code can trust. A feature that returns free-form text the rest of your app has to parse is fragile. We constrain the model to a typed schema, validate every response, and define what happens when validation fails, so the integration produces data your code can rely on rather than prose it has to guess at.
Fallbacks for the bad day. The model API will time out, rate-limit, or return an error during your busiest hour. We design what the feature does when that happens: a cached response, a degraded mode, or a clear message, so a provider incident does not become an outage in your product.
Cost you can see before the invoice. Token costs scale with usage, and a feature that is cheap in testing can be expensive at real volume. We instrument spend per feature from the first deploy, add caching for repeated work, and set alerts, so finance sees the curve coming rather than discovering it at month end.
Greenfield AI products
If you are building a new AI-native product from scratch, with no existing codebase, this service isn't the right fit. You need a full build, not an integration layer. Look at our SaaS MVP service instead.
Teams that want to DIY with zero guidance
If you want to do the integration yourselves and just need someone to review your prompts, that's not what we do. We do the work. If DIY is the goal, we can point you to documentation, but we won't charge you to give feedback on your existing setup.
Projects needing real-time voice
If your integration requirement is a real-time voice AI (something a caller speaks to), that is a different service with different latency requirements and infrastructure. See AI Voice Agent Development.
Describe the feature you want to add and the tech stack you're working with. We'll reply within one business day with a rough scope and price range.