Reliable AI applications, analytics products, ML APIs and production-oriented ML systems.
Engagements can cover a focused prototype, an existing-system review, or an implementation path that includes testing, deployment, monitoring and documentation. The related work below is portfolio evidence, not a claim of previous client delivery.
Service
AI & RAG Applications
Build source-aware AI tools that retrieve useful context before generating an answer.
Who This Is For
Teams with documents, research, policies, or internal knowledge that is difficult to search and reuse.
Problem It Addresses
Generic chat experiences can produce unsupported answers and make it difficult to inspect the evidence behind a response.
Representative Capabilities
Document Q&A and knowledge assistants
Semantic search and retrieval pipelines
Grounded LLM responses
AI research and internal knowledge tools
Typical Deliverables
Document ingestion and preprocessing
Embeddings, retrieval and RAG orchestration
Source-aware LLM integration
API, focused frontend, Docker, tests and documentation
Move a model beyond a notebook into a validated API with release, monitoring and rollback controls.
Who This Is For
Teams with a trained model or prototype that needs a safer, reviewable path to deployment and operation.
Problem It Addresses
Models can fail through data drift, inconsistent scoring, corrupt artifacts, weak validation, or unsafe releases even when headline accuracy looks acceptable.
Representative Capabilities
FastAPI inference services
Data and artifact validation
Drift and model monitoring
CI/CD, staged release and rollback
Typical Deliverables
Serving contracts and inference APIs
Docker and artifact handling
Quality, drift, integrity and monitoring checks
Release controls, observability, tests and documentation