AI transformation · business process automation

Most companies don't need more AI. They need AI that's allowed to act.

We come into companies with no AI team and no appetite for a science project, and we automate the work that is actually eating the payroll — support, phones, data entry, the developer process itself. Every action the AI takes lands in a review queue first. Autonomy is earned, one action type at a time, on measured accuracy.

Fixed-scope pilots · 30 days to first automation in production · C2C / 1099 / W2 · MSA, SOW, NDA, COI on file

approval-queue — support — live
AUTO Refund $84.00 → order #48812 policy: under $100, delivered late, first request ✓ 0.97
REVIEW Change billing address on account 7741 write action · awaiting Dana R. · queued 40s 0.91
AUTO Reply: "where is my order" → tracking + ETA grounded in 3 sources · sent 12s ago 0.99
HUMAN Cancel annual contract, customer angry abstained · sentiment + churn risk → routed to Priya 0.42
REVIEW Update shipping qty 12 → 4 on SO-9932 ERP write · diff attached · reversible ✓ 0.88
4Automation practices
30dTo first prod automation
5Levels of earned autonomy
100%Actions written to a ledger
The problem with every AI pilot you've seen

It could talk. It was never allowed to do anything.

A chatbot that answers questions and then says "please contact support" has moved zero work. The value is in the write — issuing the refund, changing the address, rescheduling the appointment, updating the ERP. Nobody will grant that access to a model they can't audit. So the pilot stalls at month three and gets quietly switched off.

01

Read-only pilots die

If the AI can only draft, a human still does 100% of the clicking. You've added a step, not removed one. The ROI never appears and the budget gets pulled.

02

Full autonomy is unsellable

No operations director signs off on a model with unsupervised access to billing. Nor should they. "Trust me, it's 94% accurate" is not a control.

03

So we build the middle

The AI proposes real, executable actions. A human approves or rejects in one click. Every decision is logged. Accuracy is measured per action type — and when it clears your bar, that action type graduates to automatic.

What we do

Four places where the work is, and the automation is boring enough to be safe.

PRACTICE 01

Support automation with an audit ledger

LLM agents that answer customer email and chat and perform the account changes behind them — refunds, address updates, plan changes, order edits — queued for human approval until they earn their way out of the queue.

  • Grounded answers, cited to your own docs
  • Write actions as reversible, diffable proposals
  • Immutable action ledger, exportable for audit
PRACTICE 02

AI call answering & scheduling

A voice agent that picks up on the first ring at 7pm on a Saturday, qualifies the caller, books them into your real calendar, and drops a structured summary into your CRM.

  • Sub-second barge-in, natural turn-taking
  • Live calendar writes with conflict handling
  • Warm transfer to a human on any doubt
PRACTICE 03

Back-office process automation

The spreadsheet-and-copy-paste layer: invoice and document intake, onboarding packets, renewals, reconciliations, CRM hygiene. Built in n8n, Temporal or plain code — whichever your team can actually maintain after we leave.

  • Document extraction with confidence gates
  • Idempotent, replayable workflows
  • Runbooks written for your staff, not for us
PRACTICE 04

Engineering process transformation

For teams whose "AI strategy" is a Copilot licence. We rebuild the development loop: spec-to-PR agents, review bots that catch the bugs your reviewers miss, test backfill on legacy code, and comprehension tooling for the codebase nobody understands anymore.

  • Measured on cycle time and escaped defects
  • Agents run in CI, not on someone's laptop
  • Guardrails first: no agent merges unreviewed
The method

The Trust Ladder

The one thing we do differently. Autonomy is not a switch you flip on launch day — it is a level each individual action type climbs, on evidence, with your sign-off at every rung.

LEVEL0

Shadow

The AI processes live traffic and writes what it would have done to the ledger. It touches nothing. You compare its proposals against what your team actually did.

Gate to advance: 2 weeks of traffic, disagreements reviewed with the team
LEVEL1

Draft

Proposals appear in your agents' existing tools as pre-written replies and pre-filled forms. A human edits and sends. First real time saved.

Gate to advance: ≥80% of drafts sent with minor or no edit
LEVEL2

Queue

The AI composes complete actions — including writes to your systems — and holds them in the approval queue. A reviewer approves or rejects in one click, with the diff and the reasoning in front of them. This is where most work lives, and it is fine to stay here.

Gate to advance: per action type, ≥98% approval over ≥200 decisions
LEVEL3

Auto with recall

That action type now executes immediately, but stays reversible and visible on a recall window — typically 15 minutes to 24 hours. Any human can pull it back with one click, and every pull-back is a training signal.

Gate to advance: <0.5% recall rate over a full month
LEVEL4

Autonomous

Fully automatic, sampled for quality, still fully logged. Reserved for high-volume, low-blast-radius actions. Most companies run a permanent mix of levels 2, 3 and 4 and that is exactly right.

Standing control: weekly sampled audit, instant fleet-wide kill switch
Read the full method →
Evidence

Reference builds

Complete, documented systems — the architecture, the guardrails, the failure modes and the economics — for the four shapes of work we automate most often.

Try it, don't take our word

Two things you can click right now.

If the work is repetitive, it is automatable. The question is what you'll allow.

Bring us the process that eats the most hours. We will tell you in one call whether it is a 30-day automation, a 90-day one, or a bad idea.