Core Capabilities
Model Architecture
Selection & shaping (classification, retrieval, generation) optimized for cost & latency.
Feature & Data Pipeline
Curated datasets, versioning, drift monitoring, and repeatable transformations.
Evaluation & Metrics
Offline/online evals, A/B tests, custom metrics (coverage, relevance, toxicity).
Retrieval & RAG
Vector stores, chunking, ranking, multi-hop enrichment & caching strategies.
Responsible AI
Guardrails, PII handling, prompt risk filters, audit trails & fallback patterns.
MLOps & Lifecycle
CI for models, deployment rollouts, canaries, model registry & observability.
Engagement Examples
Retrieval Upgrade Sprint
Improve relevance & latency; evaluate embeddings, re-rankers & memory strategies.
Evaluation Harness Build
Implement automated metrics pipeline to inform tuning & safe releases.
Responsible AI Hardening
Threat modeling, policy definitions, risk filters & monitoring dashboards.
Feature Store & Reuse
Unify feature definitions, versioning, and governance for multi-model adoption.
Plan an ML initiative
Describe objectives - we'll outline a lean path to measurable value.
Response time: usually within 24 hours.