AI integrated into your systems
Language models connected to your own data and your own tools, with permissions that follow the ones the user already has.
AI and automation where it changes something measurable.
AI integrations, agents and automated workflows built into your systems — not demonstrated alongside them.
Most AI projects stall in the same place. The demo works, everyone is impressed, and then it turns out it has no access to the company’s own data, cannot write back to any system, and cannot be measured against a result. It becomes a presentation rather than a tool.
We start from the other end: which work takes time today, how much of it, and what shortening it would be worth. If that cannot be stated, we do not build AI into it, and we say so. It is a short answer that saves more money than most long ones.
Where the answer does exist, we build it into the systems the work already happens in — not as a separate chat window staff have to remember to open. An automated flow that sits outside the tool does not get used, and one that is not used saves nothing.
What is included
Scope is set per project. These are the parts we work on.
Document and text processing
Invoices, contracts, applications and email read, categorised and summarised automatically — with a human left on the decisions that need one.
Support and internal assistants
Answers grounded in your own documents and your own history, citing the source, with a clear route to a person when the answer is not enough.
Agents and workflows
Multi-step processes that do real work in your systems, logging what was done and allowing it to be stopped and corrected.
Automation without AI
Often the answer is a scheduled job, an integration or a rule. It is cheaper, faster and breaks less — and we propose it when it is enough.
Measurement and control
Quality, cost per run and failure modes tracked continuously. AI that is not measured is AI nobody knows the state of.
How it runs
- CandidatesWe list the flows that take the most time and judge which can be automated safely.
- BaselineHow long does it take today, and how often does it go wrong? Without this there is nothing to compare against.
- PilotOne flow, in real use, with a person reviewing the output.
- EvaluationEffect measured against the baseline. If it is not enough, we do not build further.
- EmbeddingThe solution moves into the tool where the work already happens.
- MaintenanceModels, costs and quality tracked — all three move over time.
What we build with
Models
- Claude
- GPT
- Open models
- Self-hosted where required
Data
- Vector databases
- PostgreSQL
- RAG
- Document processing
Integration
- APIs
- Webhooks
- Queues
- Scheduled jobs
Control
- Evaluations
- Logging
- Cost ceilings
- Human review
A fit if you
- have a manual flow with high volume and low variation
- sit on a lot of text — contracts, cases, email — nobody has time to read
- have tried AI tools that cannot reach the company’s own data
- want to know what AI would actually be worth to you before investing
- need control: audit trails, permissions and a human left in the decision
AI & automation
What can AI actually do for our company?
It performs best on work with high volume, low variation and a clearly correct answer: reading and categorising incoming documents, summarising cases, drafting for a person to review, finding the right information in your own documents, preparing decision support. It performs worst on work requiring accountability, negotiation or judgement. The fastest way to a concrete answer for your case is to go through where the time actually goes today — which is what we do on the first call.
What does it cost to build AI into our business?
There are two costs and they behave differently. The build is an ordinary development project and is priced as one. Running it is variable and follows how much is processed — so we set a baseline and a cost ceiling before anything goes live, letting the effect be weighed against what it costs per month rather than becoming a surprise afterwards.
Where does our data go?
That is decided in the design and written down before the build. We choose the provider and hosting model according to how sensitive the data is — including EU hosting or models run in your own environment when requirements demand it — and limit what is sent to the model to what the task actually needs. You should be able to answer "where does our data go" in one sentence.
How do we know the answers are right?
By measuring rather than trusting impressions. We build a test set of real cases with known correct answers, run it on every change, and track the outcome in production. On flows where an error costs something, a person stays in the decision — AI prepares, the person approves. What cannot be measured does not go live.
Will AI replace our staff?
The projects that work remove steps, not roles: the preparation, the compiling, the searching. What stays with the person is the judgement and the customer relationship. We say that plainly, because expecting otherwise is the most common reason an AI project is judged a failure while doing exactly what it was built to do.
Can you connect AI to systems we already have?
Yes, and that is usually where the value is. If the system has an API we integrate against it. If it does not, we can often work against the database or against exports. What we will not do is pretend an integration exists — if the system cannot be reached we say so, and propose what can be done instead.
What we build — and what you have once it is built.
E-commerceCommerce built for conversion, for scale, and for a customer experience that still holds up a year in.MVP & product developmentFrom idea to MVP, launch and everything after — with a clear view of what gets built first and what can wait.Web platformsScalable platforms and SaaS products built around real users and real business processes.
Want to know what AI would be worth to you?
Book a free consultation. We go through your flows and point out where the effect would be largest — and where there would be none.