The work nobody should still be doing by hand.
We build AI automation into real business operations — the approvals, the handoffs, the copy-paste between systems that quietly consumes your team's week. Scoped against hours we've actually measured, shipped into your stack, handed over with the runbook.
# what a scoping assessment actually produces process = "supplier invoice reconciliation" volume = 1_840 # documents / month handling = 6.5 # minutes each, measured # current cost of doing nothing hours_year = volume * 12 * handling / 60 report(hours_year) # 2,392 hrs # what we automate vs. what stays human automate("extract, match, flag variance") escalate("variance > 2% or new supplier") # projected: 78% hands-off, 4-week build
Automation that survives
contact with your actual business.
The problem is rarely the model
Most AI projects don't fail because the model was wrong. They fail because nobody mapped the real process, the data was messier than anyone admitted, and the pilot never connected to the systems where work actually happens.
We start with process and data, not with a demo. If the honest answer is that a rules engine or a fixed integration would serve you better than a model, that's what we'll build — and it'll cost you less.
Measured, not promised
Every engagement opens with a baseline: volume, handling time, error rate, cost per unit of work. That baseline sets the scope, the price, and the definition of done. At handover we measure the same things again.
You should be able to state your return in a sentence a CFO would accept. If we can't get you to that sentence during scoping, we'll tell you the automation isn't worth building yet.
Before
- Three people re-keying the same data between four systems
- Two-day turnaround on requests that should take minutes
- Backlog grows every time volume spikes or someone takes leave
- Nobody can say what the process actually costs
- Errors found downstream, weeks later, by a customer
After
- One reviewed queue, exceptions only, with full audit trail
- Same-hour turnaround on the routine 80%
- Volume spikes absorbed without adding headcount
- Cost per transaction on a dashboard, updated daily
- Validation at the point of entry, flagged before it propagates
Three things we build.
Most engagements are one of these. The larger ones are all three, in this order — because the third is usually what makes the first two possible.
Take the repetitive loop off your people.
We find the highest-volume manual process in your operation, automate the routine majority of it, and route the genuine exceptions to a human with the context already assembled.
001Document processing
Invoices, purchase orders, claims, contracts, onboarding packs. Extraction, validation against your systems of record, and structured output into the tools you already use.
002Triage & routing
Inbound tickets, emails and forms classified, enriched and routed to the right queue with a drafted first response — instead of sitting until someone reads them.
003Approvals & exception handling
Rules-based approval for the clear cases, structured escalation for the rest. Every decision logged with the reasoning and inputs that produced it.
004Reporting & reconciliation
Recurring reports, month-end reconciliation, and variance analysis assembled automatically from source systems, with the exceptions surfaced rather than buried.
Assistants that work inside your systems.
Not a chat window bolted onto the side of your business. Assistants that read your documentation, follow your templates, and take scoped actions — with a human in the loop wherever the cost of being wrong is real.
005Internal copilots
Answering from your own policies, contracts and runbooks, with citations back to the source document — so staff stop asking the one person who knows.
006Customer support deflection
Front-line resolution for the questions that repeat, clean handoff with full transcript and context for the ones that don't.
007Retrieval over your data
RAG pipelines built on your document estate with permissions honoured, freshness handled, and evaluation to prove the answers hold up before anyone relies on them.
008Guardrails & human-in-the-loop
Action scoping, approval gates, rate limits and audit logging. We red team our own agents before handover — the advantage of having an offensive team in the same building.
The unglamorous part that decides everything.
Automation fails on plumbing far more often than on intelligence. We connect the systems, clean the data enough to be trusted, and instrument the whole thing so you can see it working.
009System integration
CRM, ERP, ticketing, finance, HR and the shared drive nobody has audited since 2019 — connected via API where one exists, and pragmatically where one doesn't.
010Data pipelines & quality
Ingestion, normalisation, deduplication and validation. The boring work that determines whether anything downstream can be trusted.
011Legacy & long-tail systems
Green-screen terminals, fixed-width exports, an Access database that runs a department. We've worked with worse, and we won't insist you replace it first.
012Monitoring & observability
Throughput, exception rate, cost per run and drift, on a dashboard your operations lead reads — not one only we know how to interpret.
Four phases from
first call to handover.
Assessment & baseline
We sit with the team doing the work, map the process as it genuinely runs, and measure volume, handling time and error rate. You get a ranked shortlist of candidates with projected return — including any we recommend against.
Pilot on one process
One workflow, running against real data in a controlled environment, measured against the baseline. Cheap enough to walk away from, real enough to prove the case to whoever signs off the budget.
Production build
Hardened, integrated and deployed into your environment with monitoring, guardrails, access control and a rollback path. The same offensive review we would give a client system, before anything touches production data.
Handover & support
Documentation, runbooks and training for your team, plus a support window while it beds in. We build systems you can own — including the option to take the source and run it yourself.
Three ways to start.
Scope and cost are set in the scoping conversation, and we quote fixed fees wherever the scope allows it.
- Process mapping with the operating team
- Measured baseline: volume, time, error rate
- Ranked candidate shortlist with projected return
- Build-vs-buy recommendation per candidate
- Fee credited against a subsequent build
- Everything in Assessment
- Working pilot measured against baseline
- Production build, integrated and monitored
- Offensive security review before handover
- Documentation, runbooks and team training
- 90-day support window
- A standing team across multiple workflows
- Rolling backlog, re-prioritised each quarter
- Ongoing operation, tuning and cost management
- Direct access to engineers, not account managers
- Quarterly business review against measured return
Before you get in touch.
Does our data get sent to a model provider?
Only if you decide it should, and only under terms you've agreed. We design for the deployment model you're comfortable with: a commercial API with a zero-retention agreement, a private deployment in your own cloud tenancy, or open-weight models running entirely inside your network. Data residency and retention are settled during scoping, in writing, before any build starts.
What if AI isn't the right answer for our process?
We'll say so. A meaningful share of what gets pitched as AI automation is better served by a fixed integration, a rules engine, or fixing the form that creates the mess upstream. Those are cheaper to build and far cheaper to run, and we'd rather deliver one of those than sell you a model that underperforms a script.
Will this put our people out of work?
That's your decision, not ours, and it's worth being straight about it. In practice most of our clients redeploy people onto the exception handling and judgement work that was being crowded out by volume. What we'll commit to is honesty about which tasks a system genuinely absorbs, so you're planning against reality rather than a vendor's projection.
How do you handle security for the systems you build?
Every build gets a security review from the same team that runs our AI red teaming engagements — prompt injection, tool abuse, data exfiltration paths and permission boundaries — before it touches production data. Having offensive and build capability under one roof is the main reason our agents ship with tighter action scoping than most.
What happens if we want to take it in-house?
You can. We write handover into the contract by default: source code, infrastructure definitions, documentation and runbooks are yours. We'd rather be re-hired for the next workflow than hold a client hostage on the last one.
How quickly can we see something working?
A scoping assessment takes one to two weeks. A pilot on a single workflow typically runs three to five weeks after that, so most clients see something real against their own data inside two months of the first conversation.
What's eating your team's week?
Tell us the process. We'll tell you whether it's worth automating, roughly what it would cost, and how long before it pays for itself.