01Service
AI & Automation
Put a model where the repetition is — and keep a person where the judgement is.
- Service
- 01 / 06
- Outcome
- Agents and workflows that do the repetitive work
- Core stack
- LLMs · AI Agents · RAG
Most businesses meet AI as a chat window bolted onto the side of the company. It answers questions, impresses everyone for a fortnight, and changes nothing, because it cannot see your data and cannot act on your systems.
We build the other kind. An agent that reads the enquiry, checks the CRM, applies your qualification rules, writes the record, sends the reply and escalates the ones it should not have touched. The model is one component in a system that has permissions, logging, retries and a defined failure path.
We are equally direct about where AI does not belong. If a database query, a form or a scheduled job solves the problem more cheaply and more reliably, we will tell you — and then build that instead.
Signs you need this
- Someone spends hours a week moving data between two systems
- Enquiries go cold because nobody saw them in time
- The same customer questions are answered from scratch every day
- Documents arrive as PDFs and leave as manual data entry
- A process is documented, followed exactly, and still done by hand
- Problem
- Work that follows a rule still gets done by a person — triaging enquiries, chasing follow-ups, re-keying data between systems.
- Solution
- AI agents and automated workflows wired into the tools a team already uses, with human review kept at the points where judgement actually matters.
- Outcome
- Repetitive operational work runs without anyone starting it, and the team spends its hours on the decisions instead.
/What the work includes
How the work actually runs.
Scope is agreed in writing before the build starts, and every stage produces something you can inspect.
- 01
Process audit
We map the workflow as it actually runs and mark every step by whether it needs judgement. That map decides what gets automated and what deliberately does not.
- 02
Agent and workflow design
Tools, permissions and escalation paths defined before any prompt is written — including what the system must never do without a human.
- 03
Grounding in your data
Retrieval over your own documents, records and history, with answers that cite their source so they can be checked rather than trusted blindly.
- 04
Evaluation and guardrails
A test set drawn from your real cases, run before every change, so quality is measured rather than assumed.
- 05
Deployment and monitoring
Shipped into the channels your team and customers already use, with logging, cost tracking and alerts on anything that starts drifting.
Typical stack
- LLMs
- AI Agents
- RAG
- Vector DB
- Python
- Node.js
- Webhooks
What you receive.
- AI agents with tool access
- Retrieval over your own documents
- Workflow + trigger automation
- Human-in-the-loop review steps
- Evaluation and guardrails
Straight answers.
- Will it make things up?
- Ungrounded models do. We ground answers in your own data, require citations, and set a confidence threshold below which the system escalates to a person instead of guessing. The escalation path is part of the build, not an afterthought.
- Does our data go into a public model?
- Not for training. We use providers under terms that exclude API data from training, and where data cannot leave your environment at all, we architect for that from the start.
- What does it cost to run?
- Model usage is metered, so we design for it — smaller models on the easy paths, caching, and retrieval instead of stuffing context. Running cost is estimated during architecture and tracked in production, not discovered on the first invoice.
Other services
Need AI & Automation? Tell us the problem.
The first conversation is about your operation, not our stack. If a smaller fix would do the job, we will say so before quoting a larger one.