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Representative engagements — composite use-cases built from the patterns we run with regulated clients. Industry figures are cited to public sources; engagement-level outcomes are labelled illustrative.
A Tier-1 North American bank wanted to scale LLM and agent use across the front and back office, but its risk and model-risk teams had become the bottleneck — every AI request stalled in committee. We stood up an AI governance program before the scaling, not after: model-risk integration, ISO 42001 alignment, EU AI Act readiness, and prompt-injection and DLP controls wired into a single approval gate. The result was a risk function that sponsors AI instead of blocking it, with a documented control posture a regulator can read.
A national P&C insurer was drowning in first-notice-of-loss intake: manual data entry, slow routing, and complex-claim backlogs. We scoped the work in a paid two-week Discovery Assessment that modeled payback before anyone committed to a build, then delivered a production-grade triage agent on a fixed fee — harness, eval suite, human-in-the-loop, and a tested rollback path. The agent classifies, extracts, and routes incoming claims; a senior adjuster owns every consequential decision. This is a representative composite engagement: the architecture and controls are real and reusable; the named outcomes are modeled and labeled illustrative, anchored to cited industry benchmarks.
A multi-site health system had a working clinical-documentation assistant in pilot and no way to run it in production. Maverin took over the AI surface under a Managed AI Stack retainer: drift and accuracy monitoring, output evaluation with human-in-the-loop verification, cost observability, 24×5 on-call, and a monthly cadence of new governed workflows — all under an SLA. This is a representative engagement; the outcomes are modeled and labeled, the industry figures are cited.
A North American energy operator faced two open senior OT-security seats and a SIEM that was loud but blind. Maverin embedded senior, AI-literate security engineers under the client's lead within weeks, and ran a parallel fixed-fee engagement to re-engineer detection on the OT/IT boundary. The result: continuous coverage during the hiring gap, NERC-CIP-aligned segmentation controls, and a detection stack that surfaced OT-relevant events instead of drowning analysts in noise.