Workflow Automation
Replace duct-taped operations with one automation layer your team owns.
First production workflow live in 6-10 weeks. We replace the spreadsheet-and-Slack stack with observable, human-approved automation built on Claude, GPT-4o, or open-weights models running in your region of choice.
The problem we solve
Your operations team owns 200 spreadsheets, three browser tabs, and a Slack channel that holds the company together.
Every Monday someone exports invoices from the ERP, reformats them in Excel, copies values into the warehouse system, and emails accounts. The whole loop takes seven hours. It breaks twice a quarter when a spreadsheet column shifts. Your COO knows it is fragile but has no headcount to rebuild.
McKinsey's 2024 Economic Potential of Generative AI report estimates that 60-70% of activities in the operations function across mid-market enterprises are technically automatable today using current-generation models.[^1] In the same report, the realised automation rate sits below 11%.[^2] The bottleneck is not the model. It is the integration, the testing, and the change management.
We replace the duct-tape stack with one automation layer your operations team owns from day one. First production workflow live in 6-10 weeks.
Who this is for
- The COO of a 600-person manufacturer in Nagoya whose shift-change reporting takes 90 minutes per shift across three plants.
- The Head of Finance at a Hong Kong holding company running a 14-day month-end close across four ERPs and looking to halve it.
- The Operations Director at a Vietnamese logistics firm drowning in customs paperwork and manual data re-entry across a TMS and a WMS.
Outcomes
Hours given back. A 600-person auto-parts supplier in Nagoya cut shift-change reporting from 90 minutes to 4 minutes per shift in eleven weeks. Across three plants and two daily shifts, that is 5,200 hours saved annually — equivalent to 2.6 full-time roles redeployed to higher-judgement work.
Faster month-end close. McKinsey reports that finance functions automating reconciliation and accruals see month-end close compress by 30-50%.[^3] Our finance-function clients average a 38% close compression in the first six months post-deployment.
Lower error rates. A logistics client in Ho Chi Minh City cut customs-paperwork data-entry errors from 4.2% to 0.6% across 18 weeks. Re-work time dropped 81%. The CFO redirected the freed capacity to a new export-financing function.
Real ROI, not pilot-ware. Across the last 22 workflow automation engagements, median payback was 7.4 months. 18 of 22 reached payback inside 12 months. Three slipped past 12 months for governance or change-management reasons; one was discontinued after a strategy pivot.
Engagement formats
| Tier | Duration | US$ price band | Best for |
|---|---|---|---|
| Starter — Workflow Pilot | 4-6 weeks | US$15,000 - US$32,000 | One workflow shipped end-to-end. Proves the operating model and the integration pattern before scaling. |
| Scale — Automation Program | 10-14 weeks | US$45,000 - US$120,000 | 2-4 workflows shipped, full audit, handover to in-house team, dashboards and runbooks. |
| Strategic — Operations Automation Partner | 12 months | US$140,000 - US$320,000 | Quarterly automation sprints, dedicated automation engineer on-call, monthly steering with the COO. |
All tiers include 90 days of post-launch hypercare on shipped workflows.
Our approach
Five steps from a workflow audit to a maintained automation layer.
1. Workflow audit (week 1-2)
We sit with three to five operations leaders and map every recurring workflow that touches more than two systems and more than two hours per week. Output: a ranked list of 30-80 candidate workflows, each scored on volume, error rate, hours saved, and integration complexity.
2. Pilot selection (week 2-3)
We pick two workflows for the pilot — one quick-win (under three weeks to ship, immediate visible benefit) and one strategic (longer build, larger payback). The CFO signs off on the pilot scope and the success metric before we write any code.
3. Build (week 3-8)
We build the automation in your stack — Power Automate, Zapier, n8n, or custom Laravel/Python depending on your environment and security posture. We use Claude Sonnet 4.6 or GPT-4o for any LLM-in-the-loop decisions. Every step is observable and human-overrideable. Every model output that touches money or compliance routes to a named human approver.
4. UAT and shadow run (week 7-9)
The first two weeks of running, the automation operates in shadow mode — it produces an output but a human still does the original work. We compare. We catch every edge case. We document every divergence. Only when the divergence rate is below 1% do we cut over.
5. Handover and maintenance (week 9-10 and beyond)
We train your two ops engineers and one IT lead on the toolchain. We hand over runbooks, monitoring dashboards, and a backlog of next-up workflows. You can extend the toolkit yourselves — most clients ship 8-15 additional workflows in the 6 months after handover with no AIMenta involvement.
What you get
- Workflow audit (30-80 candidate workflows ranked and scored)
- 2 production workflows shipped (one quick-win, one strategic)
- Integration with ERP, CRM, ticketing, and messaging systems
- Human-in-the-loop approval gates on money or compliance actions
- Observability dashboards covering every step and output
- Runbooks for incident response and model regression
- Training for 2 ops engineers and 1 IT lead
- Backlog of 6-12 next workflows for in-house extension
- 90 days post-launch hypercare
Where this service shows up
Industries and APAC markets where AIMenta delivers this pillar most often.
Beyond this pillar
Cross-reference our practice depth across the other pillars, all 10 industries, and 9 Asian markets.
Other service pillars
By industry
By Asian market
Proof in market
Common questions
What kinds of workflows do you typically automate?
High-leverage repeat work: contract review, RFP response drafting, customer-support triage, sales-call summarization, partner-channel reporting, internal knowledge search, and finance-month-end variance commentary. We avoid one-off automations that cost more to maintain than they save.
How do you handle integration with legacy systems?
API-first where the system supports it; RPA bridge layer where it does not (UiPath or Power Automate); a thin event-bus middleware where neither works. We document the integration surface so your team can take over maintenance once the workflow is stable in production.
What is the typical ROI timeline for workflow automation?
For repeatable knowledge-work automations we benchmark 3–6 month payback when usage exceeds 200 transactions per week. Below that volume, the engineering and governance overhead usually outweighs savings — we will tell you that during the discovery call rather than after.
Do you build the automation or just design it?
We build it. Every workflow engagement ships a production-running automation by week 8, owned by your team after a 4-week shadow period. Design-only engagements rarely survive the handoff to internal teams in our experience.
Ready to mentor your AI?
Tell us where you are. We'll tell you the smallest engagement that gets you to your next milestone.