AI automation, built for one process at a time.
AI automation is using a language model to carry out work that previously needed a person to read something and decide. In practice that means an agent that runs a multi-step process, a retrieval system that answers questions from your own documents, or a pipeline that moves data between systems without manual re-entry.
We build all three. We will also tell you when your problem does not need a model at all, which is more often than the category suggests.
- Agents act through defined tools, not open access
- Each tool carries guards on what it may do
- Actions are built so they can be rolled back
- Retrieved answers carry citations to their source
- Private hosting where data cannot leave your estate
Three kinds of work
Most requests that arrive as “can you add AI” turn out to be one of these, or a combination of two.
AI Agents & Process Automation
Language models given a defined set of tools and a process to complete — multi-step workflows, enquiry triage, internal operations tasks and data entry.
RAG & Knowledge Systems
Retrieval over your own documents, SOPs, ERP data and contracts, with answers generated from retrieved passages and cited back to their source.
System Integrations
Event-driven pipelines connecting legacy databases, custom APIs, payment rails and SaaS tools, so data moves without anyone exporting a file.
When a model is the right tool, and when it is not
A language model is expensive, non-deterministic and hard to test. That is worth accepting for the right problem and not worth it for the wrong one.
Where a model earns its place
- The input is unstructured — free text, documents, inconsistent formats
- The step being replaced is a person reading something and deciding
- The rules are real but too numerous or fuzzy to enumerate
- Volume is high enough that the reading is the bottleneck
Where it does not
- The process already has clear rules and structured inputs
- A pipeline or a database constraint would do the same job deterministically
- The cost of a wrong answer is high and cannot be checked or reversed
- Nobody can say what "correct" looks like for the task
How the work runs
- 01
One process, not a programme
We start from a single process you can name, with a definition of what a correct outcome looks like. Scope is agreed on the architecture call before work is committed.
- 02
Decide what the model is actually for
Some of what gets called AI work is a pipeline. We separate the part that needs judgement from the part that needs plumbing, and only the first becomes a model problem.
- 03
Build against guarded tools
Where an agent acts on your systems it does so through defined operations with guards on each, and with rollback, rather than being handed general access.
- 04
Ship one thing end to end
Typically two to four weeks to an initial production deployment, or around three weeks to index a knowledge base. One process working end to end beats several half-built.
Frequently asked questions
What is AI automation?
AI automation is using a language model to carry out work that previously needed a person to read something and decide. In practice that means an agent that runs a multi-step process, a retrieval system that answers questions from your own documents, or a pipeline that moves data between systems without manual re-entry.
Do we actually need AI, or just automation?
Often just automation. If a process has clear rules and structured inputs, a pipeline is cheaper, faster and easier to reason about than a model. AI earns its place where the input is unstructured — free text, documents, inconsistent formats — and judgement is required. We will tell you which one your problem is.
Which models do you build on?
It depends on what the data allows. Work can run on an enterprise language model layer, or on privately hosted models where documents cannot leave your own infrastructure. The choice is made against your data sensitivity rather than defaulted.
How do you stop an agent doing something it should not?
Agents act through defined tools rather than open access, each with guards on what it may do, and actions are built so they can be rolled back. An agent is given the narrowest capability that completes the task, not general permission to operate your systems.
How long does a first deployment take?
Typically two to four weeks to an initial production deployment for agent work and integration connectors, and around three weeks to index a corporate knowledge base for retrieval. Scope is confirmed on the architecture call before anything is committed.
Bring us the process, not the technology
Twenty minutes, with an engineer rather than a salesperson. Describe the task somebody is doing by hand and we will tell you whether it is an agent, a retrieval system, a pipeline — or something that does not need us at all.