01
AI agents & assistants
People re-type the same decision twenty times a day, and a chatbot that cannot act is no help.
Agents that read, decide and act inside your systems — with limits.
AI workflow automation studio
We build the systems that
do the work.
Agents, document intelligence and integrations that take repetitive work off your operations team — with evaluation, audit trails and human review designed in from the first week, not bolted on after the first incident.
One business day — and the reply comes from an engineer, not a sales sequence
reference pipeline · accounts payable
illustrative
- 01 Inputs Email, supplier portal, scanned paper
- 02 Extract Per-field confidence, not a single guess
- 03 Decide PO ↔ receipt ↔ invoice matching
- 04 Route Above threshold posts to the ledger automatically
- 05 Review Below threshold goes to a human, with evidence attached
- 06 Learn Corrections return to the evaluation set
- 2–4 weeks
- From kickoff to a working pilot
- 5 stages
- Discovery to operated system
- 1 engineer
- Owns your build end to end
- US/EU
- Data residency, your choice
The problem we are hired for
Work that should not
need a person.
Nobody buys “AI”. Companies come to us because a process is eating their team alive, an earlier pilot died in a slide deck, or an auditor asked a question nobody could answer. Here is what we hear most often — and what we build in response.
- 01
The pain
Skilled people spend their week keying data from PDFs, emails and portals into systems that cannot talk to each other.
Hundreds of hours a month, and a close that slips whenever someone takes leave.
What we do
Document intelligence with per-field confidence, matching against your own records, and posting through the accounting API.
What changes
The keying stops. Exceptions — not every item — reach a human, in one queue with the mismatch shown.
- 02
The pain
Knowledge lives in three wikis and two people’s heads, so every repeat question is answered from scratch.
Slow first response, inconsistent answers, and support cost that grows with every new customer.
What we do
Retrieval grounded in your current documentation, with citations and an explicit refusal when the answer is not in the corpus.
What changes
Drafts arrive sourced and reviewable, so agents spend their time on judgement instead of searching.
- 03
The pain
The RPA bot that ran the process for two years broke when the vendor changed a button, and nobody noticed for a week.
Silent failures, manual catch-up, and a business that quietly depends on one fragile script.
What we do
Migration of brittle screen-scraping jobs to APIs and documented interfaces, in risk order, with alerts that fire on drift.
What changes
Automation that survives a UI change — and tells you when something upstream moved.
- 04
The pain
A pilot was bought last year. It demoed beautifully and never reached production, so nobody trusts automation now.
Budget spent, credibility lost, and a team that will resist the next initiative.
What we do
A fixed-scope pilot on your real data with an evaluation set, measured cost per item and an explicit go / no-go — including “no”.
What changes
Production is a decision with evidence behind it, not a leap of faith.
See the full picture — pain, solution and the metric we move →
Capabilities
Eight things we build, and the pain each one ends.
We do not sell a platform. We build the specific system your process needs, integrate it with what you already run, and hand you the keys.
02
Document intelligence
Invoices, contracts and claims have to be read and keyed by hand — slowly and inconsistently.
Invoices, contracts, claims and forms turned into structured data.
03
Workflow orchestration
Multi-step processes live in inboxes, so a half-finished job disappears until someone chases it.
Multi-step processes that survive retries, outages and half-finished work.
04
System integrations
Two systems that both “work” still need a person to move data between them every morning.
ERP, CRM, HRIS, ticketing, legacy databases — connected properly.
05
Review consoles
Automation without a review screen turns into blind trust — or into nobody using it.
The human-in-the-loop screens your operators will actually use.
06
Data plumbing & retrieval
The system answers confidently from stale or irrelevant sources, and you cannot tell which.
Clean inputs, entity resolution and grounded retrieval.
The arithmetic
Eleven steps in.
Three steps out.
This is where the value sits: not in a clever model, but in the number of times a person touches an item before it is finished.
Manual steps removed, exceptions kept human. Nothing with a financial consequence posts without either a match or a signature.
before · manual
11 steps- 01 Invoice arrives in a shared inbox
- 02 Someone opens the PDF and reads it
- 03 Keys 14 fields into the ERP
- 04 Checks the PO in a second system
- 05 Emails the warehouse about a mismatch
- 06 Waits for a reply
- 07 Notes the exception in a spreadsheet
- 08 Re-keys the correction
- 09 Files the PDF in a folder
- 10 Month-end: rebuilds the list by hand
- 11 Closes late again
after · automated
3 steps- 01 Invoice arrives — parsed, matched and posted
- 02 Only exceptions reach a human, with the mismatch shown
- 03 Every action logged, reconciled and reportable
Same controls, same audit trail, same month-end — with the keying removed and the exceptions collected in one queue instead of eleven inboxes.
How we work
Five stages from “this is broken” to operated.
Same sequence every time. Nothing runs on your production data until the pilot has a measured accuracy figure and a named reviewer from your team.
- 01
Discovery
3–5 days
We sit with the people doing the work today, map the process as it actually runs — not as the SOP claims — and find where the hours and the errors are.
- Process map with volumes and cycle times
- Baseline metrics to beat
- Risk and data-access review
- 02
Blueprint
1 week
A written plan you can argue with: architecture, model choice, integration points, human-review design, evaluation criteria and a cost model per transaction.
- Solution blueprint
- Evaluation plan and target accuracy
- Fixed-scope pilot proposal
- 03
Pilot
2–4 weeks
A working system on your real data behind a human review queue. Small blast radius, real numbers, no slideware.
- Working pilot on production-shaped data
- Measured accuracy and cost per item
- Go / no-go recommendation with evidence
- 04
Production
4–10 weeks
Hardening: throughput, failure handling, permissions, monitoring, audit trail, runbooks and training for the team that will own it.
- Production deployment in your cloud
- Monitoring and alerting
- Runbook and operator training
- 05
Operate
Ongoing
We keep it working: evaluation runs on every model or prompt change, drift review, cost tuning, and a monthly session on what to automate next.
- Continuous evaluation and regression tests
- Monthly quality and cost report
- Quarterly roadmap of new automations
What we need from you
Four things, and the project moves fast.
- One process owner who can make decisions without a committee
- Read access to the systems in scope (sandbox is fine to start)
- A sample of real documents or tickets — a few hundred beats a few thousand
- A named reviewer from the team that does the work today
- Encrypted in transit and at rest
- Your data is never training data
- US or EU residency, in writing
- Incident notification within 72 hours
By function
Where it hurts, and what we automate.
Six places this work usually starts. Each one names the metric we are trying to move — if we cannot measure it, we do not promise it.
Finance & accounts payable
Invoices arrive by email and portal, get keyed in by hand, and the month-end close depends on three people remembering the exceptions.
- Invoice capture from email, portal and scan
- Three-way match against PO and receipt
- Exception queue with the reason in plain language
- Posting to the ledger with a full audit trail
Days-to-close and touchless invoice rate
Operations & back office
Order intake, scheduling and status updates live in spreadsheets and inboxes, and nobody can say where a case actually is.
- Intake from email, forms and partner feeds
- Routing and prioritisation rules
- Status propagation to customers and systems
- Escalation when an SLA is about to slip
Cycle time per case and on-time rate
Revenue operations
Leads sit in a queue for hours, CRM records are half-filled, and quoting is a copy-paste exercise across four tools.
- Lead enrichment, scoring and routing
- CRM hygiene and duplicate resolution
- Quote and proposal assembly from approved content
- Handover packs for account teams
Speed-to-first-response and data completeness
How the routing actually works
Humans where it matters,
by arithmetic.
Every step has a threshold, tuned on your own evaluation set. Above it the action executes; below it the item goes to a person with the evidence attached. That is the whole safety story — not a disclaimer, a number.
routing rule
route(x) = HUMAN if p̂(x) < θ
AGENT otherwise - p̂(x)
- calibrated model confidence for step x, not the raw softmax score
- θ
- threshold tuned on your evaluation set — typically 0.82–0.94
- HUMAN
- the item lands in the review console with its evidence attached
- AGENT
- the action executes and is written to the audit log
Thresholds are per step, not per system: a wrong supplier name is cheap to fix, a wrong payment is not.
Straight answers
Questions we get on the first call
No “contact us for details”. If the answer is “it depends”, we say what it depends on.
How quickly can we see something working?
Pilot in two to four weeks from kickoff, including discovery and blueprint. That assumes we get read access to a sandbox and a sample of real documents or tickets in the first week — that sample is the usual cause of delay.
What does it cost?
Discovery is a fixed fee, the pilot is a fixed price, and production is a monthly retainer with a defined scope. We quote after discovery because the honest number depends on your process, not on a price list. If a process is not worth automating, we say so and charge only for discovery.
Do we need to replace our systems?
No. We integrate with what you run — ERP, CRM, ticketing, data warehouse, and the legacy application with no API. Replacing your core systems is a different project and usually a worse idea than automating around them.
Which models do you use?
Whatever the task and your constraints justify: frontier APIs where quality matters most, open-weight models you can self-host where data cannot leave your environment, and classical methods where a model is the wrong tool. We benchmark on your data during the pilot and document why we chose what we chose.
What happens when the AI is wrong?
That is designed for, not discovered later. Every step has a confidence threshold; below it the item goes to a human with the evidence attached. Nothing with legal, financial or safety consequences is decided autonomously — see our Responsible AI Policy.
Will this work with our existing RPA?
Often it replaces part of it. We inventory what you have, keep the parts that work, and migrate the brittle screen-scraping jobs to APIs and documented interfaces in risk order. No big-bang cutover.
Next step
Bring us the process that is eating your week.
Forty-five minutes, one process, no pitch deck. You will leave with a view on what is worth automating, what it would take, and what it would cost.
One business day — and the reply comes from an engineer, not a sales sequence