Outcomes, in specific numbers.

Rune's legacy lending engine took 11 minutes to decision a loan, with manual review on 38% of applications. Throughput capped before they could enter the Tier-2 city market.

We re-architected the decisioning service around an event-driven core in Go, replaced the monolithic rules engine with a feature store + gradient-boosted scorecards, and introduced shadow-mode model validation before any production flip.

Decision time fell to 4.1 seconds. Approval rate rose 37% within two quarters with default rates unchanged. Rune launched in 6 new states with the same risk team.

Veda's outpatient clinics were drowning in repetitive intake. Clinicians spent 14 minutes per visit on documentation; patients waited an average of 47 minutes.

We built an AI triage assistant trained on de-identified intake transcripts, with strict guardrails and a human-in-the-loop review queue. Deployed across 22 clinics with rolling regional rollouts and on-prem inference for sensitive workloads.

Patient wait time dropped 42%. Clinician adoption hit 92% by month three. Veda passed an external HIPAA audit with zero findings.

Northwind's prototype LLM platform was a single-tenant Python service that fell over above ~120 concurrent users. Inference costs were 31% of MRR.

We split inference, retrieval and orchestration into independently scaled services, introduced semantic caching, switched to fp8 quantized models on inference-optimized hardware, and added a usage-aware autoscaler.

Throughput rose 11× on the same infra footprint. Inference cost per request dropped 68%. Northwind closed Series B at $4M ARR.