Projects designed around real engineering problems.

Problem: How can users obtain verifiable answers from changing reference material?

Architecture: Document ingestion → indexing → retrieval → grounded generation.

Features planned: Source-aware retrieval · Traceable citations · Configurable ingestion

Evaluation: Planned: retrieval relevance, answer faithfulness, latency and cost checks.

Problem: How should retrieval and prompting be composed and tested independently?

Architecture: Loader → chunker → embedding store → retriever → model.

Features planned: Composable pipeline · Document provenance

Evaluation: Planned: retrieval quality and regression test set.

Problem: When do specialist agents improve a retrieval workflow enough to justify their cost?

Architecture: Coordinator → research agent → verification agent → response.

Features planned: Agent handoffs · Human review gates

Evaluation: Planned: task completion, tool-call accuracy, token usage.

Problem: How can coding-agent performance be compared transparently and reproducibly?

Architecture: Task fixtures → agent execution → isolated test runner → report.

Features planned: Repeatable tasks · Test-based scoring

Evaluation: Planned: pass rate, reliability, execution time and cost.

Problem: How can multi-step questions be answered with an auditable retrieval trail?

Architecture: Planner → tool selection → retrieval → validation → answer.

Features planned: Tool orchestration · Execution traces · Fallback handling

Evaluation: Planned: groundedness, tool success, security and latency.

Problem: How portable are production AI workloads across infrastructure providers?

Architecture: Application container → automated release → cloud-specific runtime.

Features planned: Infrastructure comparison · Deployment automation

Evaluation: Planned: deployment reliability, operational effort and cost.