Multi-step AI workflows with API integration, review steps, and failure handling.
Machine learning models and computer vision pipelines for inference and data extraction.
Fine-tuning, model packaging, deployment, and monitoring for private model environments.
Document ingestion, indexing, retrieval, and evaluation for RAG and analytics.
Evaluation harnesses, regression tests, guardrails, and model comparison.
Dashboards for model runs, human review queues, system health, and usage.
Technical notes from experiments and system work: evaluation harnesses, model-routing tradeoffs, human review paths, and operating patterns for reliable AI systems.
Deterministic test surfaces, regression suites, and provenance for language-model and AI components that need to ship under review.
InfrastructureDecision points for hosted APIs, private deployments, and hybrid routing based on data boundaries, latency, volume, and operating cost.
Review LoopsReview paths where uncertain model outputs pause safely, preserve context, and return to automation with an operational record.