( AI development )

AI wired into the work you already do.

Assistants that answer from your own material, and automation that removes the queries nobody wants to answer twice.

Plenty of AI projects fail for the same reason. A model is bolted onto the side of a business, given no access to anything real, and asked to be impressive. It answers confidently, it answers wrongly, and people stop trusting it within a fortnight.

The useful version is narrower and duller. A model connected to your actual documents, your actual database and your actual rules, doing a specific job with limits you can see. That is what we build.

( What this covers )

AI & Chatbot Development, in practice

Chatbots that answer from your documents

Retrieval over your handbooks, policies, catalogue or knowledge base, so replies come from your material and can be traced back to the source.

Customer support assistants

Handling the repetitive questions, passing anything unusual to a person, with the handover built in from the start rather than added after complaints.

Document processing

Pulling structured data out of invoices, forms, contracts and scans, and putting it where your systems expect it.

Language model integration

Adding drafting, summarising, classifying or extraction inside the software you already run, rather than in a separate tool nobody opens.

Internal knowledge assistants

Letting your own team ask questions across scattered internal material instead of interrupting the person who happens to know.

Guardrails and evaluation

Tests against real questions, limits on what the system will attempt, and a defined answer for when it does not know.

( Worth talking to us if )

Any of this sounds familiar.

Your support team answers the same questions every day
Information exists but nobody can find it quickly
Data arrives as documents and someone retypes it into a system
You have been sold an AI product that impresses in a demo and fails in practice
You want to use AI but need to know where it should not be trusted

( How we work )

The shape of a typical build

01
Pick a job worth doing
We start from a specific task with a measurable outcome, not from the technology. Some briefs are better solved by ordinary code, and we will say so.
02
Get the data in order
Quality of answers follows quality of source material. We work out what the system can draw on before choosing a model.
03
Build with limits
Retrieval so answers are grounded, guardrails on scope, and a clear route to a human when confidence is low.
04
Test against real questions
We evaluate on questions your business actually receives, not on examples chosen to make the demo look good.

( What you end up owning )

Everything, and no strings.

The repository, including prompts, retrieval logic and evaluation tests
API keys held on your own provider accounts, so usage bills go to you
Documentation of what the system will and will not attempt
An architecture written so a provider can be swapped later

( Built with )

Python
TypeScript
OpenAI
Anthropic
Vector databases
FastAPI
PostgreSQL

Mainstream tools with deep talent pools, chosen so you are never dependent on us to maintain what we built.

Questions about ai development

Will it make things up?: Language models are probabilistic and can produce output that is wrong. Nothing removes that entirely, and anyone promising otherwise is overselling. What we do is ground answers in your own documents, constrain what the system will attempt, and design a clear path to a person when it is unsure. We will be direct with you about where the limits sit.
Does our data get used to train models?: Not by us. We use provider configurations that exclude your data from training, and we do not use your material to build anything for anyone else. This is written into our terms rather than just promised here.
What does it cost to run?: Model usage is billed by the provider, on your own account, so you keep control of it. We size the likely monthly cost during scoping and design to keep it predictable rather than letting it drift.
What if a provider changes or shuts down a model?: It happens regularly. We build so that swapping providers is possible wherever it is practical, rather than wiring your business into one vendor's roadmap.

Tell us what you need building.

Send a brief and you get a written scope, a timeline and a fixed price back within 48 hours. It costs nothing and commits you to nothing.

Get a quote

( Often paired with )

AI & Chatbot Development, in practice

Chatbots that answer from your documents

Retrieval over your handbooks, policies, catalogue or knowledge base, so replies come from your material and can be traced back to the source.

Customer support assistants

Handling the repetitive questions, passing anything unusual to a person, with the handover built in from the start rather than added after complaints.

Document processing

Pulling structured data out of invoices, forms, contracts and scans, and putting it where your systems expect it.

Language model integration

Adding drafting, summarising, classifying or extraction inside the software you already run, rather than in a separate tool nobody opens.

Internal knowledge assistants

Letting your own team ask questions across scattered internal material instead of interrupting the person who happens to know.

Guardrails and evaluation

Tests against real questions, limits on what the system will attempt, and a defined answer for when it does not know.

The shape of a typical build

Pick a job worth doing

We start from a specific task with a measurable outcome, not from the technology. Some briefs are better solved by ordinary code, and we will say so.

Get the data in order

Quality of answers follows quality of source material. We work out what the system can draw on before choosing a model.

Build with limits

Retrieval so answers are grounded, guardrails on scope, and a clear route to a human when confidence is low.

Test against real questions

We evaluate on questions your business actually receives, not on examples chosen to make the demo look good.

Questions about ai development

Will it make things up?

Language models are probabilistic and can produce output that is wrong. Nothing removes that entirely, and anyone promising otherwise is overselling. What we do is ground answers in your own documents, constrain what the system will attempt, and design a clear path to a person when it is unsure. We will be direct with you about where the limits sit.

Does our data get used to train models?

Not by us. We use provider configurations that exclude your data from training, and we do not use your material to build anything for anyone else. This is written into our terms rather than just promised here.

What does it cost to run?

Model usage is billed by the provider, on your own account, so you keep control of it. We size the likely monthly cost during scoping and design to keep it predictable rather than letting it drift.

What if a provider changes or shuts down a model?

It happens regularly. We build so that swapping providers is possible wherever it is practical, rather than wiring your business into one vendor's roadmap.