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Explore this serviceAI Automation & Internal Tools
Automate the work nobody should be doing
Somewhere in your company, a capable person spends two hours a day moving data between systems, classifying tickets, or re-typing a PDF into a form. That is where automation pays for itself — not in a chatbot on the homepage. We build the tooling that removes those hours, and we are honest about where a language model helps and where it does not.
- Grounded in your data
- Human in the loop
- Evaluated, not vibes
The value of AI in most businesses is unglamorous: extraction, classification, summarisation, and drafting — each wired into a workflow that already exists and already matters. The failure mode is equally consistent: a demo that impresses, followed by a system nobody trusts because it is wrong four percent of the time and nobody knows which four percent.
How we make it trustworthy
Every automated step gets an evaluation set drawn from your real data before it goes anywhere near production. We measure accuracy against it, and we design the workflow so that low-confidence cases route to a human instead of proceeding quietly. The system knows what it does not know.
Outputs are grounded in your own documents and records, with the source cited, so a reviewer can verify a result in seconds rather than re-doing the work.
Where it usually starts
- Document intake: invoices, contracts, forms — extracted into structured, validated records
- Triage: routing and prioritising inbound tickets, applications, or leads
- Internal search: answering questions from your own documentation, with citations
- Back-office tooling: the admin screens that make the whole thing operable
Problems we solve
What this work is for
The situations teams are usually in when they bring us this problem.
Skilled people doing clerical work
The most expensive automation is the one you never build. We start where the hours are, not where the hype is.
Data trapped in documents
PDFs, emails and scans that must become records. Extraction with validation, so a bad read is caught, not filed.
AI output nobody trusts
Without evaluation and citations, every result must be checked by hand — which is the work you were trying to remove.
No tooling to run any of it
An automation without a review queue, an audit trail, and an override is a liability. We build those first.
Our approach
How the work runs
The same sequence on every engagement, so there are no surprises in week three.
Find the hours
We sit with the team doing the work and measure where the time actually goes. The candidates that survive this step are usually not the ones people expected.
Build the evaluation
Before any model is chosen, we assemble a labelled set from your real cases. That set is what we optimise against and what tells us when we are done.
Ship with a human in the loop
The first release routes everything through review. As measured accuracy earns it, confident cases are allowed through automatically.
Monitor
Accuracy drifts as your data and your models change. Ongoing evaluation and alerting mean you find out before your customers do.
What's included
What you get
Concrete deliverables — all of it yours to keep, run, and hand to another team.
Workflow analysis
A written, quantified view of where the manual hours are and which are worth automating.
Evaluation harness
A labelled dataset and a repeatable score, so 'is it good enough' is answered with a number.
The automation itself
Grounded, cited output wired into your existing systems, with retries and idempotency.
Review tooling
The queue, the audit trail, and the override — the parts that make an automation safe to operate.
Cost and accuracy monitoring
Dashboards and alerts for both quality drift and token spend, because both surprise people.
Technology
What we build it with
Chosen for the problem, not for novelty.
We are not tied to any of these. If your team already runs something that works, we build with it.
- Models
Model choice is a trade-off between accuracy, latency and cost — decided per task with evidence, not loyalty.
- Claude
- OpenAI
- Open-source models
- Structured output
- Retrieval
Grounding in your own data is what turns a plausible answer into a verifiable one.
- PostgreSQL
- pgvector
- Embeddings
- Citations
- Tooling
Automations are background jobs, and background jobs need queues, retries, and somewhere to fail safely.
- Node.js
- TypeScript
- Redis
- Next.js
Questions
Before you get in touch
The questions that come up in almost every first conversation.
Will this replace our team?
No, and we would be sceptical of anyone promising that. It removes the clerical layer around skilled work so the same people handle more of what they were hired for. The engagements that succeed are the ones the affected team helped design.
How accurate is it, really?
We do not answer that in a pitch; we answer it with your data. The evaluation set is built in the first weeks, and the go/no-go decision is made against a measured score you agree to up front.
Where does our data go?
Wherever you decide. We can run entirely within your cloud with a self-hosted model, or use a hosted API with a zero-retention agreement. The architecture is chosen after your data-handling constraints are on the table, not before.
What does it cost to run?
Inference cost is a line item we monitor from the start, and it usually shrinks as we route easy cases to cheaper models. You get a per-transaction cost figure, not a surprise invoice.
Tell us what you're building.
Bring the problem, not a spec. We will tell you honestly whether we are the right team and outline a concrete first step — no obligation, no sales theatre.