Language Models, Wired Into Your Product Properly
LLM Integration With GPT, Claude, Gemini & Open-Source Models
We add language models to your product the way production needs it done. We test the candidates on your own data, build a layer that can switch providers and fall back when one fails, and put spend limits and logging around every call, so the feature stays dependable after the demo.
Overview
The right model is the one that passes your tests
Chosen by testing
We score candidate models on your real cases, so the choice rests on results from your data and not on a leaderboard.
Swappable by design
Your product talks to our layer rather than to one vendor, so changing or adding a model does not mean rewriting the feature.
Costs you can predict
Caching, routing and spend limits keep the monthly bill something you can forecast before a feature goes live.
Nothing is a black box
Every call is logged with its prompt, output, speed and cost, so a bad answer can be traced instead of guessed at.
Adding a language model to a product takes an afternoon. Making it dependable takes everything after that: the wrong answer on an unusual input, a provider outage on launch day, a bill that doubles the week a feature becomes popular.
The model is the easy part.
We integrate GPT, Claude, Gemini or open-source models into your product, and we choose between them by running each one on your own examples, not by reputation. Around the model we build what production needs: a layer that can swap providers, fall back when one fails, cap spending, and record what happened. If a simpler approach or no model at all does the job, we will say so.
Chosen by testing
We score candidate models on your real cases, so the choice rests on results from your data and not on a leaderboard.
Swappable by design
Your product talks to our layer rather than to one vendor, so changing or adding a model does not mean rewriting the feature.
Costs you can predict
Caching, routing and spend limits keep the monthly bill something you can forecast before a feature goes live.
Nothing is a black box
Every call is logged with its prompt, output, speed and cost, so a bad answer can be traced instead of guessed at.
LLM Integration
What We Build Around The Model
Start with one feature. The layer we build for it carries over to the next one.
Model Selection & Benchmarking
We run GPT, Claude, Gemini and open-source models on your own cases and give you a ranked recommendation, including self-hosted options when data must stay on your servers.
API Integration Into Your Product
Summarising, classifying, extracting and generating, wired into your app or backend with streaming, retries and timeouts handled properly.
Prompt & Output Management
Prompts are versioned and tested like code, and model output is checked against a schema before your product ever relies on it.
Cost, Speed & Fallbacks
Caching, routing easy requests to cheaper models, spend caps, and an automatic switch to a backup provider when the first is slow or down.
Evaluation & Monitoring
A test set that runs before every change, plus live logs and alerts when quality, speed or cost moves in the wrong direction.
The Architecture
What Sits Between Your Product And The Model
Calling a model directly works in a demo. In production we put a thin layer in the middle, so the model can change without your product noticing.
Your product
Web app, mobile app or backend service
Our integration layer
Prompts & versions
Prompts live in one place, versioned and tested, not scattered through the code.
Routing
Simple requests go to a faster, cheaper model and hard ones to a stronger one.
Fallback & retries
If a provider is slow or down, the request moves to a backup and the user never sees an error.
Checks & logging
Output is validated against your rules, and every call is recorded with its cost.
Any model provider
GPT
Claude
Gemini
Open-source & self-hosted
When data must stay on your servers
Swap or add a provider without changing a line of your product.
Evaluation
Four Things We Measure Before Anything Ships
A model that looks good in a demo can still fail your users. These are the four checks that tell the two apart.
Quality
How we measure it
We run every candidate on a test set built from your real cases and score each answer against what a good one looks like.
What you decide
How accurate is accurate enough?
Speed
How we measure it
We time responses under realistic load, including the slowest ones, not just the average.
What you decide
What wait feels acceptable to your users?
Cost
How we measure it
We work out the price of a request and of a month at the volume you expect, before you commit.
What you decide
Where should the budget cap sit?
Safety
How we measure it
We test with awkward, off-topic and adversarial inputs and check what the model does with them.
What you decide
What must it refuse or hand to a person?
The same test set runs again before every model or prompt change, so an upgrade cannot quietly make things worse.
How We Work
From Prototype To Dependable Feature
Plenty of teams already have a working prototype. These are the steps that turn it into something you can leave running.
Already have a prototype or a model chosen? We can pick up from any of these steps.
- 01
Define The Job
We pin down what the model is actually being asked to do, what a good answer looks like, and what a bad one costs you.
- 02
Build The Test Set
We collect real examples, including the awkward ones, and agree how each answer will be scored.
- 03
Compare The Models
We run the candidates on the test set and report quality, speed and cost side by side, with a clear recommendation.
- 04
Integrate With Guardrails
We wire the chosen model into your product with output checks, fallbacks, spend limits and logging in place.
- 05
Launch Behind A Switch
It goes to a small share of users first, with a way to turn it off immediately if something looks wrong.
- 06
Monitor & Review
We review quality and cost with you, and re-run the test set when a provider releases a new model, so you can decide whether to move.
The Stack Behind Our LLM Integrations
Production-grade tooling, with the model layer kept separate so the provider can change without touching your product.
Why us?
Why Choose Us for LLM Integration?
No Favourite Vendor
We have nothing to gain from steering you to one provider, so the recommendation follows your test results.
Built To Be Swapped
Models improve and prices change every few months. Our layer means moving to a better one is a configuration change, not a rebuild.
Production, Not Demo
Fallbacks, spend caps, output checks and logging are part of every build, not extras you find you needed after launch.
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Tell us the feature you want a model to power. We'll tell you which models are worth testing, what it should cost to run, and whether you need one at all.