Your tools already talk. Someone just has to stop translating.
We design, build and run the automation layer between your apps — triggers, approvals, handoffs and data sync that execute themselves, with AI handling the judgement calls that used to need a human in the middle. Built on your stack, governed by your rules.
The work between the work is eating your week.
Every business runs on handoffs — and every manual handoff leaks time, accuracy and momentum. If your operations depend on someone remembering to move data between tools, this is the page for you.
Swivel-chair data entry
The same order, client or ticket gets re-keyed into three systems.
Cost · Hours a week lost to duplication — and every re-key is a chance to introduce an error.
Approval bottlenecks
Requests wait in inboxes for a sign-off that takes ten seconds to give.
Cost · Work stalls for days on decisions that have a documented rule behind them.
Dropped handoffs
Sales closes the deal; ops finds out on Friday. The kickoff email never went.
Cost · Customers feel the seams in your process before you do.
Spreadsheet glue
A critical process lives in a spreadsheet one person maintains by hand.
Cost · One holiday, one typo, one broken formula — and the process stops.
Zapier sprawl
Dozens of half-documented zaps and scripts nobody dares touch.
Cost · Automation you can't trust is just another system to babysit.
No audit trail
Nobody can say who changed what, when, or why the numbers differ.
Cost · Every discrepancy becomes an investigation instead of a lookup.
From trigger to synced systems — without a human relay.
This is the anatomy of every workflow we ship. Select a stage to see exactly what happens while your team gets on with real work.
Trigger fires
An event starts the workflow — instantly.
What happens here
- Webhooks, form submissions, inbox events, CRM stage changes, file drops, schedules
- Deduplication and validation at the door — bad or duplicate events never enter the flow
- One workflow, many entry points: the same process fires however the work arrives
- Every trigger logged with payload and timestamp for the audit trail
In practice
A deal moves to Closed-Won in the CRM at 16:03. The onboarding workflow starts before the sales rep has closed the tab.
Strategy first. Then architecture. Then the build.
Automation that sticks is a designed system, not a stack of zaps. We run the same disciplined path on every engagement.
- 01PHASE 1 / STRATEGY
Map the workflow reality
We sit with the people who do the work and map how it actually flows — including the workarounds. You get a prioritised automation map: which workflows to automate first, what each is costing you manually, and the number each should move.
- 02PHASE 2 / ARCHITECTURE
Design the automation layer
Triggers, data contracts between systems, decision rules, AI boundaries, exception paths and approval points — designed on paper and signed off by you before anything is built. Where AI decides, its limits are part of the design.
- 03PHASE 3 / BUILD
Build and battle-test
We build on production-grade orchestration, wire it to your apps, and test against real historical cases — including the messy ones — until the workflow handles your actual edge cases, not the demo path.
- 04PHASE 4 / LAUNCH
Launch with a safety net
New workflows run in shadow mode first — executing alongside the manual process so you can compare outputs. Then we cut over one workflow at a time. Nothing is ripped out on day one.
- 05PHASE 5 / OPERATE
Monitor and extend
We watch the exception rates, tune the rules, and extend the layer to the next workflow on the map. You get a monthly report in hours saved and errors prevented — not vanity metrics.
Automation you can hand an auditor.
The point of automating your operations is trusting them more, not less. Every workflow is governed, observable and reversible.
You approve every rule
Decision logic, thresholds and AI boundaries are written with you and signed off before launch. The system never improvises policy.
Humans own the exceptions
Anything below the confidence bar routes to a named person with full context. Escalation is a designed path, not a failure mode.
Every run auditable
Trigger, data, decision, action and outcome logged for every single run. Any result can be traced in one lookup.
Your data stays yours
Workflows run against your systems with scoped credentials. No training on your data, and data processing agreements as standard.
Production orchestration, not a chain of zaps.
The difference between automation you babysit and automation you trust is engineering: idempotent runs, retries with backoff, dead-letter queues, versioned workflow definitions, and hard guardrails on what AI may decide alone. That's the layer we build.
- Built on production-grade orchestration — observable, versioned, testable
- Idempotent by design: replaying an event never duplicates an invoice or email
- AI decisions bounded by confidence thresholds and explicit allow-lists
- Failures retry, then alert a human — workflows never die silently
- Runs documented and searchable: every 'why did this happen?' is one query away
A running automation layer — not a diagram.
The engagement ends with your workflows executing in production and your team trained to own them.
Live automated workflows
Your prioritised workflows running in production on your stack — triggered, deciding, acting and syncing without manual relays.
Integration layer
Robust connections to your CRM, PM, finance and communication tools, with scoped credentials and documented data contracts.
Decision & escalation rulebook
The documented logic: what runs on rules, where AI decides, what its boundaries are, and who owns each exception path.
Run analytics & audit trail
A monthly view of runs, hours saved, exception rates and cycle times — with every individual run traceable end to end.
What operations look like afterwards.
The point isn't fewer clicks — it's a business that executes the same way every time.
Handoffs in seconds
Work moves between teams and tools the moment it's ready — not when someone checks an inbox.
Re-keying eliminated
Data entered once, synced everywhere. The swivel chair retires.
Decisions that don't queue
Rule-covered approvals clear themselves; only genuine judgement calls reach a human.
Answers in one lookup
Who did what, when and why — the audit trail replaces the investigation.
Ops that scale without headcount
Double the volume without doubling the admin. The layer absorbs the growth.
A team doing real work
The hours that went to copy-paste and chasing go back to customers and improvement.
Common questions about workflow automation.
Those are excellent trigger-action tools, and we sometimes build on them. The difference is the layer around the automation: designed decision rules, AI judgement with boundaries, exception routing, idempotency, monitoring and an audit trail. A zap moves data; an automation layer runs a process you can trust — and someone is accountable for keeping it running.
No — that's the point. We build the layer between the tools you already run: CRM, project management, accounting, inboxes, spreadsheets. If a tool has an API (almost all do), we can usually integrate it. Rip-and-replace is explicitly what this approach avoids.
AI handles classification and judgement inside explicit boundaries: categorising requests, extracting data from documents, drafting content, scoring priority. Every AI decision has a confidence threshold — below it, the case routes to a human. And every decision is logged with its reasoning. Deterministic rules handle everything that has a documented answer.
Failures are a designed path, not a surprise. Steps retry with backoff; if a run still can't complete, it lands in an exception queue and alerts a named owner with the full run context. Runs are idempotent, so resuming never duplicates an action — no double invoices, no repeated emails.
Typically 3–6 weeks from strategy call to the first workflow running in production, depending on integrations. We deliberately ship one high-value workflow first — visible proof on real volume — then extend the layer workflow by workflow.
There's a build engagement, then a monthly run-and-improve cost. In the strategy phase we cost your current manual process — hours, error rates, delay — so the business case is explicit before you commit to the build. If the maths doesn't clear, we'll tell you.
Find out what your manual handoffs are costing.
A 30-minute call: we map your highest-friction workflow, show you what the automated version looks like on your stack, and give you a straight answer on whether the maths works. No obligation.