LLM Applications and Retrieval
Assistants grounded in your own content, so answers cite something real rather than being produced confidently from nothing.
Services
AI That Removes Work, Not AI That Sounds Impressive
Practical AI with a measurable payback: assistants, document pipelines and automations wired into the tools you already run.
Most AI projects fail for the same reason: they start from the technology rather than from a task somebody is currently doing by hand. The useful version starts with a job that is repetitive, expensive and well defined, and asks whether a model can do it reliably enough to trust.
We build AI into products where it pays for itself, and we build the evaluation alongside it, because a feature that is right most of the time is only useful when you know which times those are.
This Is for You If
- A team is spending hours on work that is repetitive and rule-shaped
- You have a corpus of documents nobody can search usefully
- You want an assistant inside your product rather than a chatbot bolted to the side
- An AI feature shipped, and nobody can tell whether it is actually working
What you get
Deliverables
Workflow Automation
The repetitive path between your existing tools automated end to end, with the human kept at the decisions that need one.
Data Pipelines
Getting your content into a shape a model can use: extraction, cleaning, chunking and keeping it current as the source changes.
Evaluation and Guardrails
Test sets, measured quality and explicit limits on what the system may do unsupervised. This is what separates a demo from something you can put in front of customers.
How it runs
The Engagement
Four stages, each with something you can see at the end of it. No phase runs longer than the evidence that it is working.
Start a project- 01
Find the Task
We look for work that is repetitive, measurable and currently manual. If nothing qualifies, we will say so rather than invent a use case.
- 02
Prove It Small
A narrow version against real data, measured against a test set, before anything is wired into the product.
- 03
Build and Instrument
Production integration with evaluation, logging and guardrails from the start rather than added after the first bad output.
- 04
Tune Against Reality
Real usage reveals the cases a test set missed. The system improves against those rather than against assumptions.
Questions
Asked Before Every Project
Not if the integration is set up correctly. Which provider and which tier you use determines this, and it is a decision we make deliberately with you rather than by default.
Grounding answers in retrieved source material, constraining what the system is allowed to do on its own, and measuring output against a test set so you can see quality rather than assume it. Nothing removes the failure mode entirely, which is why the guardrails matter.
Often it is not, and a rule-based automation is cheaper, faster and more predictable. We will tell you when that is the case. A model is worth its cost when the task involves language or judgement, not when it involves arithmetic.
Also from us
Other Services
MVP DevelopmentGo from idea to a product real users can pay for. We scope ruthlessly, build the core, and ship in weeks, not quarters.Websites & Web AppsFast, accessible, search-optimised sites and web applications that make a small team look like a category leader.SaaS PlatformsMulti-tenant products built to survive growth: billing, roles, analytics and an architecture that scales past launch.Mobile ApplicationsNative-feeling iOS and Android products, designed around the few moments that actually keep people coming back.Scale, Support & OptimisationInherit a codebase that is slowing you down? We stabilise it, pay down the risk, and keep shipping alongside your team.
Let's build what's next
Your next product starts with a conversation.
Share your idea, explore what is possible, and get a straight answer on scope, cost and timeline. No pitch deck required.