The process

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The process

Three steps. That's it.

01

We listen

30-minute call. You describe the bottleneck – no tech talk needed. We tell you honestly whether automation solves it, and how.

02

We build it

We handle the technical work and report in plain language. Quality is measured against agreed checks before anything goes live – not assumed.

03

It runs daily

The system goes live with monitoring and escalation paths. You see results in your dashboard; we keep it sharp over time.

What we automate

Six things AI can take off your plate

Click any card to see the difference

Before

An inbox that sorts itself

Shared inboxes overflow. Requests get missed, forwarded twice, or answered late. Someone spends an hour a day just routing messages.

After →
After →

Agents triage every message, draft replies, and route exceptions to the right person – around the clock.

Triage off your desk
Before

Calls handled around the clock

Missed calls after hours. Staff repeating the same confirmations dozens of times a day. Multilingual coverage too expensive to staff.

After →
After →

A voice agent confirms appointments, qualifies leads, and books meetings – in real time, in multiple languages.

Real-time, multilingual
Before

Reports that write themselves

Month-end means days of copy-paste between systems, spreadsheets, and email threads.

After →
After →

Pipelines pull the data, reconcile it, and deliver the report on schedule – every time.

Manual reporting cycles eliminated
Before

Documents classified and synced

Vendor emails, PDFs, invoices, and statements processed by hand into the ERP.

After →
After →

Documents are classified, matched, and synced automatically; only exceptions reach a human.

2–3 hrs/day freed (case study)
Before

Your tools, finally talking

CRM, email, sheets, and databases all separate – people are the glue between systems.

After →
After →

n8n/Make pipelines move data between systems reliably, with logging, retries, and alerts.

~10 hrs/week saved per client
Before

AI that knows your business

Generic chatbots give generic answers. Your documentation sits unread in folders.

After →
After →

Knowledge-base search over your own documents – answers grounded in your data, with sources.

Answers from your data
Pilot playbook

How we run a first pilot

A pilot exists to answer one question cheaply: does automating this specific process save enough time or money, reliably enough, to justify building it out. Keep the first pilot narrow, measurable and reversible. The steps below take you from a shortlisted idea to a decision you can defend, usually within two to six weeks.

1

Pick one process and write down how it works today

Choose a single, well-bounded process rather than a whole department. Document the current steps end to end, who does them, how often, and how long each takes. This baseline is what you will measure against later, so record it honestly before anything changes.

2

Set a clear success metric and a threshold

Decide upfront what good looks like: hours saved per week, error rate, response time or cost per transaction. Name the number the pilot must beat to be worth continuing, and agree it with whoever controls the budget so the result is not argued over afterwards.

3

Scope the smallest version that proves the point

Cut the pilot to the narrowest slice that still tests the core assumption: one team, one region, one document type or one customer segment. A tight scope keeps build cost and risk low and gives you a clean read on whether the approach works.

4

Confirm data, access and a human checkpoint

Check that the inputs the automation needs actually exist, are reachable and are clean enough to use. Decide where a person reviews or approves output before it reaches a customer or the ledger, so a mistake is caught rather than shipped.

5

Build, then run in parallel with the manual process

For the pilot period, let the automation run alongside the existing manual work rather than replacing it. Compare the two outputs side by side. This exposes edge cases and errors while the old process is still there as a safety net.

6

Measure against the baseline and the threshold

At the end of the pilot, compare the recorded results to the baseline from step one and the threshold from step two. Include the time your team spent supervising and correcting the automation, not just the raw processing time, so the saving is real.

7

Decide: scale, adjust or stop

Make an explicit call. Scale it if it cleared the threshold and held up; adjust and re-run if it was close but flawed in a fixable way; stop if the saving is not there. A pilot that ends in a clean no is a success, because it cost little and saved a larger mistake.

How to choose the first pilot

  • Pick a process that is high volume and repetitive, where small per-task savings add up to something material.
  • Favour work that follows clear rules and produces consistent output over judgement-heavy tasks that resist automation.
  • Choose something whose inputs are already digital and reachable, so the pilot is not blocked waiting on data.
  • Prefer a process with a measurable, visible cost today, so the before-and-after comparison is obvious.
  • Avoid, for a first pilot, anything that touches money movement, compliance or customer trust without a human check.
  • Choose a process whose owner wants the change; an unwilling team can stall even a sound automation.

Estimating whether it pays off

  • Estimate current cost: multiply the time the process takes by how often it runs by the loaded hourly cost of the people doing it.
  • Estimate the realistic reduction, not the perfect one: assume the automation handles the routine cases and people still handle exceptions.
  • Subtract the ongoing running cost (software, model usage, licences) and the supervision time the automation still needs.
  • Weigh the one-off build cost against the annual saving to get a rough payback period; under twelve months is usually a comfortable yes.
  • Count avoided-cost benefits too where they are real and measurable: fewer errors, faster turnaround, penalties or lost sales avoided.
  • If the numbers are close, size the downside: a pilot that saves little but costs little is still a cheap way to buy certainty.

Common pitfalls

  • Automating a broken process instead of fixing or removing it first, which just makes the mess run faster.
  • Scoping too broadly, so the pilot is slow, expensive and gives no clean read on what actually worked.
  • Skipping the baseline, which leaves you unable to prove the saving and open to challenge later.
  • Ignoring the supervision and correction time, and overstating the net saving as a result.
  • Removing the manual process before the automation has proven it holds up under real conditions.
  • Chasing the hardest, most impressive process first rather than the dull, high-volume one that pays back fastest.
  • Treating a pilot as a commitment to scale, rather than as a cheap test that is allowed to end in no.
Questions

Things people usually ask

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