The problem we were brought in for, what we built, and the metric that changed, from real client engagements.
| Client | Industry | Engagement | Headline metric | Read time |
|---|---|---|---|---|
| A growth-equity firm | Private capital / technical diligence | Readiness & Diligence, 2 weeks | $1.4M Undisclosed re-platform cost surfaced | 7 min |
| A mid-market healthtech | Healthtech / clinical intake | Custom Development + AI Integrations, 8 weeks | 12% → 3% Field hallucination rate | 7 min |
| A Series B fintech | Fintech / risk & fraud | Strategy & Architecture + AI Integrations, 10 weeks | -41% Cost per resolved request | 7 min |
| A mid-market SaaS support platform | B2B SaaS | AI Integrations, 8 weeks | -38% Ticket escalation rate | 6 min |
The client is anonymised, and these figures are not independently audited.
A growth-equity firm
A growth-equity firm's term sheet on an 'AI-native' SaaS target got repriced after a two-week check found $1.4M of hidden re-platform cost.
Read →A mid-market healthtech
A healthtech's extraction model dazzled in a notebook, then hallucinated on real traffic. An eval harness cut hallucinationsA hallucination is a confident, fluent output that is factually wrong or unsupported. from 12% to 3% in 8 weeks.
Read →A Series B fintech
A Series B fintech's LLM spend outran revenue on one provider. Model-agnostic routing cut unit cost, vendor risk, and closed a data-residency gap.
Read →A mid-market SaaS support platform
A SaaS support platform swapped a keyword search widget for a tenant-isolated retrieval pipeline, cutting escalations and response time with no new headcount.
Read →Source: https://customlabs.io/case-studies/