Case Studies
Not demos. Not hypotheticals. These are AI systems that are live and running today — built for mid-market businesses.
A regional CPA firm was drowning in manual client onboarding — collecting documents, chasing signatures, entering data across multiple systems. Every new client cost 8 hours of staff time before a single billable hour could begin. The firm was turning away clients not because of capacity, but because of operational drag.
We rebuilt the onboarding workflow end-to-end. Document collection was automated through a secure client portal. E-signature requests triggered automatically. Data entered in one system populated across all others — no re-keying. Staff were only pulled in for the exceptions.
Client onboarding dropped from 8 hours to 45 minutes. The firm onboarded 40% more clients in Q1 without adding a single staff member — and staff reported significantly lower administrative stress.
A real estate investment group was losing deals to slow response times. Leads came in from multiple sources, sat in a CRM, and waited for an agent — sometimes hours later. By then, the prospect had moved on. The team was working hard; the process was losing the deals for them.
We built an AI lead pipeline that qualifies, scores, and routes every inbound lead in under 2 minutes. High-intent leads trigger immediate outreach — personalized, context-aware, and routed to the right agent. The AI handles every touch point until a human needs to step in.
127% increase in lead-to-meeting conversion. The team closes more deals without working more hours — and no longer loses opportunities to response lag. The pipeline runs 24/7 without supervision.
A mid-size insurance provider was reviewing every single claim manually. 60% of the team's time went to routine, low-risk claims — leaving less bandwidth for complex cases that actually needed human judgment. Hiring more reviewers wasn't financially viable; the economics didn't work.
We deployed an AI triage layer that classifies each incoming claim by risk level, completeness, and type. Routine, low-risk claims are processed automatically. Complex or flagged claims escalate to human reviewers — with a full context summary already prepared.
The team now focuses exclusively on exceptions. Processing volume tripled with the same headcount. Reviewers spend their time on cases that actually require judgment — not on paperwork that AI handles faster and more consistently.
A real estate investment firm was spending 3–5 hours per deal manually pulling comps, researching neighborhoods, qualifying inbound leads, and writing deal memos. Their analysts were researchers, not decision-makers. The bottleneck wasn't intelligence — it was information assembly.
We built a multi-agent system that handles the entire research and qualification workflow inside their CRM. Agents pull comps, research neighborhood data, qualify leads, and assemble deal memos — automatically, before an analyst ever opens a record.
Analysts now review AI-prepared deal briefs instead of building them. Deal analysis time dropped from 3–5 hours to 18 minutes. The same team now evaluates 10× more opportunities — and makes better decisions because they spend their time analyzing, not assembling.
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