AI & Automation services
Automation built, tested and handed over - with AI where it earns its place.
This is the delivery page: what we actually build. Workflow automation across your systems, integration, document and language automation, monitoring, and documentation so your team can own it. Strategy and prioritisation sit on the AI consulting page.
Conceptual state
Document queued for human review
At a glance
- Typical first targets
- Document intake, quoting, status chasing, approvals and cross-system data entry.
- Non-negotiables
- Human review paths, scoped data access, logged decisions and visible failure handling.
- How we sequence
- Deterministic automation first, AI where it is the only sensible way to handle the input.
How a governed workflow runs
AI inside a process, not bolted beside it.
An AI step is only useful when it sits between a real trigger and a real business rule. This is the shape every automation we build follows.
01
Trigger in a system you already use
An email arrives, a form is submitted, a record changes state. The workflow starts from real business activity, not a separate tool someone has to remember to open.
02
Only approved context is assembled
The step receives the records and documents it needs and nothing else, so the data boundary is explicit and reviewable.
03
The AI step interprets the unstructured part
Reading a document, classifying an enquiry, drafting a response - the part where language or layout varies too much for rules alone.
04
A deterministic rule decides
The result is validated against your own records and tolerances. The rule decides; the model does not.
05
Confirmed work is written back; the rest stops for a person
Anything low-confidence or outside tolerance routes to a review queue instead of proceeding, and every run is logged so outcomes can be measured against the manual baseline.
Automation patterns
What we automate most often.
These recur across manufacturing, construction, professional services and distribution because the underlying friction is the same: information moving by hand.
Document and email intake
Supplier invoices, purchase orders, remittances and enquiry emails read once, classified, validated against existing records and routed - with a human review queue for low-confidence items.
Quote and order preparation
Assemble quotes and orders from templates, price lists and prior history, so staff review and approve rather than rebuild each one from scratch.
Status and follow-up automation
Automatic updates to customers and internal owners when a stage changes, replacing the daily round of chasing calls and 'just checking in' emails.
Approval and exception routing
Rules decide what proceeds automatically and what needs a person, so approvals stop living in inboxes and become visible, timed steps.
Data hygiene and matching
Deduplication, entity matching and field normalisation across CRM, ERP and marketing systems - the unglamorous work that makes reporting trustworthy.
Assistive summarisation
Meeting notes, service history and long threads condensed into the record, so the next person picking up the job has context without reading everything.
Choosing the right tool
Not every problem is an AI problem.
Using a model where a rule would do adds cost, latency and review burden. Using rules where language varies produces brittle logic nobody maintains.
Automate first
Rule-based, high volume, low ambiguity
- Steps with a clear right answer every time
- High frequency, low variation transactions
- Movement of data between two systems
- Scheduled reporting and reconciliation
- Deterministic approvals and thresholds
Consider AI
Language, documents, classification
- Unstructured input: PDFs, emails, notes, calls
- Judgement-shaped tasks with reviewable output
- Drafting where a person still approves
- Search and retrieval across scattered knowledge
- Classification where rules become unmanageable
Non-negotiables
AI is added where it earns its place, inside a process that already works.
Human review paths, scoped data access, logged decisions and visible failure handling are part of the build, not a later hardening exercise.
Governance
How we keep automated work accountable.
Automation increases throughput and it increases the blast radius of a bad rule. These controls apply to every build.
01
Define the decision boundary
Document what the system may decide alone, what it may draft, and what always requires a person. This is agreed before build, not discovered after.
02
Keep a human review path
Low-confidence outputs route to a queue with the source document attached, so review takes seconds rather than reopening the whole task.
03
Control the data
Scope which records and documents a model can access, keep customer data out of anything not contractually appropriate, and log what was sent.
04
Measure against the manual baseline
Capture current handling time and error rate before go-live so improvement is evidence rather than impression.
05
Fail visibly
Errors raise alerts and land in a queue. Silent failure is the most expensive outcome in any automated process.
Getting started
A short, contained first engagement.
01Process capture
Two to three sessions tracing a candidate process end to end, including volumes, exceptions and current handling time.
02Opportunity shortlist
A ranked list of automation candidates with the pattern, systems involved, and the type of benefit expected - effort, error rate or cycle time.
03One proof build
Build the highest-confidence candidate in your real environment with real data, and measure it against the baseline.
04Decide from evidence
Expand, adjust or stop. You keep the documentation either way.
Continue exploring
Bring us the process everyone complains about.
We will tell you whether it should be automated, redesigned or left alone - and what the first build would involve.