AI-first product teams,
on demand.

Radiologists and pathologists faced mounting review backlogs. Manual annotation of imaging studies was slow, inconsistent across reviewers, and created single points of failure in triage workflows.

Built a DICOM ingestion pipeline feeding a computer vision inference service, paired with a specialist review dashboard that renders bounding-box overlays on each study. A human-in-the-loop approval gate ensures no clinical flag is committed without explicit clinician sign-off.

• ~40% reduction in average triage time per study
• Full per-study audit trail stored for compliance review
• Zero false-positive-only dismissals since deployment
• Annotation consistency improved across distributed reviewer team

PyTorch with the MONAI medical imaging framework for model training and inference. OpenCV for preprocessing (windowing, normalization, augmentation). FastAPI inference microservice with async job queuing. PostgreSQL audit log with row-level security. End-to-end encryption at rest and in transit with HIPAA-aware storage architecture.

The finance team was manually processing thousands of documents monthly — invoices, contracts, and bank statements — with extraction errors causing downstream reconciliation failures and significant analyst time burned on rework.

Fine-tuned an LLM on domain-specific vocabulary to extract structured fields from varied document layouts. RAG retrieval surfaces relevant policy context during extraction. All output is emitted as validated JSON with per-field confidence scores; low-confidence extractions are automatically routed to a human review queue before any downstream write.

• ~70% reduction in manual document handling time
• Extraction error rate under 2% across all document types
• Full human visibility preserved — no silent automation
• Review queue volume tracked and reportable per analyst

Domain fine-tuned LLM with structured output enforcement. LangChain orchestration layer for retrieval and chain logic. pgvector extension on PostgreSQL for semantic policy lookups. Pydantic-based output validation with schema versioning. Full audit logging of every extraction event and human override decision.

A multi-vendor marketplace was struggling to scale: search relevance was poor (leading to high abandonment), vendor onboarding was a manual back-and-forth process, and the internal ops team was bottlenecked by tooling that couldn't keep up with transaction volume.

Rebuilt the full-stack platform: ML-powered search and personalized recommendation engine on the customer side; a self-serve vendor onboarding flow with automated verification; and an internal ops dashboard giving the team real-time visibility and bulk action tooling without engineering intervention.

• +35% improvement in search-to-conversion rate
• Vendor onboarding time cut by more than half
• Ops team capacity freed for higher-value work
• Platform handles 3× previous peak transaction load

Next.js frontend with SSR for catalogue pages. Node.js API layer with GraphQL for flexible data fetching. Elasticsearch with ML-based learning-to-rank for search relevance. PostgreSQL as the primary store with Redis caching layer for hot product and session data. GitHub Actions CI/CD pipeline with staged deployment.