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How We Deployed 3 Automations in 2 Weeks
A case study: how a financial services firm automated compliance reporting, meeting notes, and client onboarding in just two weeks with OpenClaw.
A case study: how a financial services firm automated compliance reporting, meeting notes, and client onboarding in just two weeks with OpenClaw.
A mid-size financial services firm came to us with a familiar problem: their operations team was drowning in manual work, but every automation attempt had stalled. They'd tried Zapier (too limited for their compliance needs), considered building custom scripts (no bandwidth), and briefly explored an enterprise automation platform (six-month implementation timeline).
They needed results in weeks, not months. Here's how we delivered three production automations in 14 days.
A wealth management firm with 120 employees. Their compliance team (4 people) spent roughly 40% of their time on manual reporting. Their operations team managed client onboarding with spreadsheets and email chains. Meeting notes from client advisory sessions disappeared into shared drives, never to be acted on.
We'll call them Meridian Financial (not their real name, but the details are accurate).
We started with our standard workflow audit. Two sessions, 90 minutes each, with the compliance lead, the ops manager, and the head of advisory.
What we found:
Compliance reporting consumed 15+ hours per week. The team manually pulled transaction data, cross-referenced it against regulatory thresholds, compiled reports, and submitted them. Most of this was data gathering and formatting—not actual analysis.
Client onboarding took 5-7 business days, involved 23 manual steps across 6 systems, and frequently stalled because someone forgot to complete a step. New clients noticed. It wasn't a great first impression.
Meeting notes from advisory sessions (3-5 per day) were transcribed but never processed. Action items—follow-up calls, portfolio adjustments, document requests—were manually extracted (or forgotten).
We scoped three automations, defined success metrics for each, and identified champions:
| Automation | Success metric | Champion | |-----------|---------------|----------| | Compliance reporting | Reduce from 15 hrs/week to 2 hrs/week | Sarah (Compliance Lead) | | Client onboarding | Reduce from 5-7 days to 1-2 days | Marcus (Ops Manager) | | Meeting notes → actions | 100% of action items captured and ticketed | Priya (Senior Advisor) |
This was the highest-value target, so we started here.
The old process: Every Monday, the compliance team downloaded transaction logs from three systems, loaded them into Excel, ran manual checks against a list of 40+ regulatory thresholds, flagged exceptions, compiled a report, had it reviewed by the compliance officer, and submitted it to the regulator's portal.
The OpenClaw automation:
The key challenge: Regulatory rules aren't simple if/then checks. Some thresholds depend on client type, account age, and transaction history. The AI agent needed to reason about context—exactly what OpenClaw is built for.
Day 5 result: First automated compliance report generated. Sarah compared it against her manual version. The automated report caught two exceptions she'd missed (one was a edge case involving a joint account). She approved the workflow.
The old process: When a new client signed, Marcus's team manually created entries in the CRM, portfolio management system, and document management system. They sent welcome emails, scheduled intro calls, requested KYC documents, set up account structures, assigned advisors, and tracked everything in a shared spreadsheet.
The OpenClaw automation:
The key challenge: Different client types (individual, joint, trust, corporate) have different onboarding paths. The old spreadsheet had notes like "if trust, also request trust deed and check state requirements." OpenClaw handles this through natural language rules—no flowchart required.
Day 8 result: First automated onboarding completed. What used to take 5-7 days of back-and-forth finished in 36 hours, with the bottleneck being the client providing documents—not the ops team processing them.
The old process: Advisors used a transcription tool for client meetings. Transcripts were saved to a shared drive. Occasionally, someone would read through them and extract action items. More often, action items were remembered (or forgotten) by the advisor.
The OpenClaw automation:
The key challenge: Meeting transcripts are messy. People talk over each other, go on tangents, and express action items indirectly ("we should probably think about rebalancing the fixed income allocation"). The AI agent needs to distinguish real commitments from idle discussion—this is exactly the kind of judgment that rule-based tools can't handle.
Day 11 result: Priya reviewed the agent's output from five meetings. It correctly identified 23 out of 24 action items (the missed one was extremely implicit). She added a clarifying note to the agent's instructions, and accuracy hit 100% on the next batch.
The final three days focused on:
| Metric | Before | After | |--------|--------|-------| | Compliance reporting time | 15 hrs/week | 1.5 hrs/week (review only) | | Onboarding time | 5-7 business days | 1-2 business days | | Meeting action items captured | ~60% (estimated) | 100% | | Total team hours saved | — | 22 hrs/week |
At their blended team cost, that's roughly $85,000 per year in recovered productivity. The engagement cost paid for itself in under two months.
More importantly, the compliance team stopped dreading Mondays. Marcus's team stopped getting complaints about slow onboarding. And advisors started trusting that nothing would fall through the cracks after a client meeting.
Three things made this engagement successful:
Every company has three automations that would deliver outsized impact. The question is identifying which three—and deploying them before momentum fades.
Book a workflow audit and we'll help you find yours.
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