Workflow audit
Map where information is manually moved between systems, and rank those tasks by time cost and reliability risk so effort goes where it pays.
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n8n · Make · Zapier · CRM · Email · Sales operations
AI Automation That Clears the Repetitive Work Out of Your Week
Most business software does not talk to each other, so people become the integration layer — retyping a lead from a form into a CRM, chasing a quote, manually updating a spreadsheet. This engagement connects those tools into workflows that run on their own, with AI handling the parts that need judgement such as classifying, extracting, drafting, and summarising. The goal is not automation for its own sake. It is removing the hours your team spends moving information between systems.
Problems this engagement is built to solve
The scope starts from the product problem—not a prepackaged list of technologies.
- Enquiries sit in a form or inbox until someone happens to notice
- The same information is retyped into a CRM, a sheet, and an inbox
- Follow-up depends on individual discipline rather than a system
- Quotes and proposals are rebuilt from scratch for every request
- Nobody knows the true response time or where leads are lost
- Reporting is assembled by hand at the end of every week
What you should leave with
- Every enquiry captured, categorised, and routed without manual entry
- Follow-up that happens immediately and consistently, not when someone remembers
- Quotes, briefs, and replies drafted from your own templates and data
- Reporting that assembles itself instead of being rebuilt each week
- A visible record of what is automated, what failed, and what needs a person
What the work includes
A focused engagement covers the decisions, implementation, and handoff needed to solve the problem cleanly.
Automation build
Implement the workflows in n8n, Make, or Zapier, connecting your forms, inbox, CRM, calendar, and databases with proper error handling and retries.
AI steps where judgement is needed
Add AI only where a rule is not enough — classifying an enquiry, extracting details from a document or message, drafting a reply, or summarising a thread for review.
Visibility and handover
Add alerting and logging so a failed run is noticed rather than silently missed, and document each workflow so your team can maintain it.
A clear path from diagnosis to delivery
You see what is being changed, why it matters, and how the result is verified.
- 01
Find the manual work
Start from how work actually happens today, including the copy-paste nobody admits to. Rank candidates by hours lost and by how often they cause errors.
- 02
Design the flow
Decide what should be fully automatic, what should be drafted for a human to approve, and where the workflow should stop and ask for a person.
- 03
Build and test
Implement with retries, failure alerts, and duplicate protection, then test against real historical examples before it touches live data.
- 04
Monitor and improve
Watch the early runs, fix the edge cases that only appear with real data, and extend coverage where the evidence supports it.
What I can build
The engagements I take on for this service, described so you can tell whether any of them match what you need.
Instant lead capture and routing
A form, ad, or directory submission is enriched, categorised, pushed to your CRM, and acknowledged before a human even sees it.
AI lead enrichment and scoring
Incoming enquiries are read, summarised, enriched from your own data, and scored so sales works the right leads first.
Quote and proposal drafting
Reusable quotes and proposals generated from your templates and pricing, then sent for approval before anything reaches a customer.
Invoice, payment, and follow-up sequences
Payment and invoice events trigger the right follow-up, reminders, and internal notifications without checking whether someone replied.
Document and inbox extraction
Details pulled from received emails, PDFs, and spreadsheets and written into your systems instead of being copied by hand.
Automated reporting and alerts
Scheduled summaries of pipeline, response times, and anomalies, delivered to your team or tools instead of assembled manually.
Frequently asked questions
Practical answers before we define the project scope.
- Is this just connecting Zapier to my CRM?
- Partly, but the hard part is usually deciding what should happen at each step and handling what goes wrong. Reliable automation needs duplicate protection, retries, alerting when a run fails, and a clear rule for when a human should approve instead. Those decisions matter more than the connection itself.
- Where does AI actually fit in an automated workflow?
- Where a fixed rule is not enough — reading an unstructured enquiry and deciding which category it belongs to, extracting details from a PDF, drafting a reply in your tone, or summarising a long thread. Straightforward copying and routing is better done with deterministic logic, which is cheaper and easier to trust.
- Which platform do you build on — n8n, Make, or Zapier?
- It depends on your situation. n8n suits self-hosted, cost-conscious setups with complex logic. Make is strong for multi-app visual workflows. Zapier is the fastest to start when the apps involved are already supported. I recommend the platform that fits your team and budget rather than forcing one.
- What happens when a workflow breaks at 2am?
- That case is designed in. Workflows include retries for transient failures, idempotency so a repeated run cannot double-send or double-create records, and alerting to a channel your team actually watches. Silent failure is the thing worth avoiding, because it is how a broken workflow quietly damages data.
- Can you work with the tools we already use?
- Yes. The starting point is your existing stack — form provider, CRM, inbox, calendar, spreadsheets, payment gateway, and whatever internal system holds the data. Automation works best when it fits the tools already in use rather than requiring a migration first.
- How do you make sure AI steps do not embarrass us in front of customers?
- Anything customer-facing is drafted for approval rather than sent autonomously, at least until the quality is proven. Internally, the AI steps operate inside narrow, defined scope with the retrieved context supplied, and every run is logged so you can review what actually happened.
Related specialist services
How this engagement is scoped
- Pricing
- Custom scope — quoted after a short discovery call
- Typical timeline
- A first workflow is typically live within one to two weeks
- Ownership
- Built on n8n, Make, or Zapier so you own the workflows and can edit them.
Bring me the problem—not a finished technical brief.
Share the product, the current constraint, and what a better outcome looks like. I'll help define the right scope.