Three ways an automation project could work
Explore sample scopes for fintech compliance, e-commerce support and healthcare reporting. Each scenario shows a possible approach and the assumptions you would need to test.
Illustrative examples. These are fictional businesses and hypothetical figures, not client results or forecasts.
- Fintech
- 73% in 6 weeks
- E-Commerce
- 64% in 12 days
- Healthcare SaaS
- 96% in 3 weeks
- Client evidence
- illustrative only
Timelines and metrics are hypothetical. A real scope starts with your data and requires validation.
Reduction in review time
Fictional example: Series B fintech · 120 employees · $2.1M daily transaction volume
The compliance team manually reviewed 300+ flagged transactions daily. Average resolution time sat at 18 hours, with a 12% false positive rate creating unnecessary friction for legitimate customers and draining analyst bandwidth.
A possible project would map the compliance pipeline, test transaction risk scoring and route exceptions to analysts. Human review, traceable decisions and validation would be required before any production use.
AI AuditCustom BuildHypothetical figures for this example. These are not measured results, expected returns or guarantees.
Illustrative timeline: 6 weeksPython · LangChain · PostgreSQL · Supabase Edge Functions
Tickets resolved autonomously
Fictional example: DTC brand · $8.4M annual revenue · 15-person operations team
Customer support handled 1,200+ tickets per week manually. Average first response time was 6.2 hours and climbing. CSAT stagnated at 3.4/5 despite hiring two additional agents the previous quarter.
A proposed support agent could connect Shopify, Zendesk and an approved knowledge base. It would draft responses to routine questions and escalate sensitive or unsupported requests, with rollout depending on testing.
AI AgentsHypothetical figures for this example. These are not measured results, expected returns or guarantees.
Illustrative timeline: 12 daysTypeScript · OpenAI API · Zendesk API · Shopify API · Supabase
Reduction in report production time
Fictional example: B2B healthcare platform · 45 employees · 340 clinic clients
The analytics team produced 8 client reports per week manually. Each report required pulling data from 3 separate systems, formatting to client specifications, and quality checks. Average production time: 4.5 hours per report.
A proposed reporting workflow could combine approved data sources, generate draft reports on a schedule and require human approval before delivery. Access controls and data handling would need separate assessment.
Ready SolutionCustom BuildHypothetical figures for this example. These are not measured results, expected returns or guarantees.
Illustrative timeline: 3 weeksNode.js · PostgreSQL · REST APIs · PDF Generation · Supabase
Start by measuring the current process
Estimate the cost of the manual process using your own volumes, staff time and error rates before deciding what to automate.
Record how the process works today. Collect volumes, handling time and exceptions from your actual workflow.
Agree scope, price and acceptance checks in writing before implementation starts.
Test the proposed system against representative inputs and review failures before expanding its use.
Scope a project using your own data.
The $290 operations audit maps your manual work and helps you decide what is worth automating. Its fee is credited toward a subsequent build.
$290 · credited against any build · no retainer