What "it worked" actually looks like.
Five anonymized case studies across accounting, wealth management, law, publishing, and healthcare. Real architectures, real metrics, real workflows. Client names redacted out of respect for ongoing relationships and procurement preferences.
Why anonymized
Most of our clients prefer that the specific competitive advantage they've built with us stay quiet, at least for a few quarters. We respect that. These case studies are real engagements with real architectures, real metrics, and real lessons; the names are off, that's all. When clients are ready to share their own story publicly, those land here too.
Accounting
Near-zero-touch bank-feed categorization for a QuickBooks-driven accounting firm.
A 4-stage vendor matching pipeline, regex sanitization, exact match, fuzzy match, and Gemini 2.5 Flash LLM inference, collapses hours of manual transaction categorization into seconds, with shadow-column auditability and a clean QuickBooks Online OAuth integration.
Wealth Management
Seven automation agents replace hours of weekly busywork at a wealth management firm.
From SMS-triggered cashflow reports (text the AI a command, get a PDF emailed back) to mass bank-statement export across 121 client accounts and a 4-hour reconciliation cron, seven specialized agents now run what a team used to do manually.
Legal
"Practice Made Perfect": a full agent platform pilot for a mid-market law firm.
A branded agent platform with seven integrated modules, Assistant, Vault, Knowledge, Workflow Agents, ROI Analytics, Security & Governance, and a learning Academy, gave a partner audience a tangible vision before a single line of production code was committed.
Publishing
Global publisher cuts rights-clearance time 70% with a contract-aware agent.
Royalty inquiries that used to bounce between rights, finance, and editorial, and take 5–10 business days, now resolve inside 24 hours through an agent that reads contract clauses, statements, and payment records, and cites them inline.
Healthcare
Top-10 health system halves prior-authorization backlog with HIPAA-aligned agent.
A clinician-in-the-loop agent assembles prior-auth packets from Epic, checks payer rules, drafts submissions, and pauses for the clinician to sign. Net effect: median PA turnaround drops from 4 days to under 2, with zero PHI leaving the customer's HIPAA-aligned environment.
Patterns across all five
What every successful deployment has in common.
A defined workflow, not "use AI somewhere"
Every win started with one painful workflow and one executive who owned it. Engagements that started with "we want AI somewhere" went sideways. Pick the work first.
A real eval harness, not vibes
Every agent ships with an eval harness drawn from real cases. Quality is measured against that on every change. Without it, agents drift and trust evaporates.
Human approval at the right gates
Every deployment has a clear "the agent drafts, a person approves" pattern at decision points that matter. Speed gains come from automating everything else.
A team that runs it after launch
Half the value is in Operations: tuning policies from real conversations, expanding the eval set, upgrading models. The deployments that stalled were the ones where no one owned this.
Integrations sized honestly
Every project blew through its first integration estimate, then settled. Now we add a 30% buffer up front. Legacy systems and authentication remain the slowest part.
Privacy by deployment, not policy
For every regulated client, "the data never leaves your environment" was a technical decision, not a marketing one. Open-weight in VPC or hyperscaler-account hosting, picked early.