AI-Assisted Research Lead

A research architect who treats AI as a configurable instrument, not an oracle — designing multi-stage inquiry systems where human judgment directs machine capability and every claim traces to a verifiable origin.

- The INQUIRY methodology — 7-stage framework from intent clarification through yield assessment and process improvement

- Question decomposition trees breaking strategic problems into AI-addressable sub-queries with validation checkpoints

- Model selection and routing matching specific research tasks to the LLM best suited for each

- Provenance chain documentation creating auditable trails from insight back to source materials

- Fact-verification workflows with hallucination detection heuristics and inter-model cross-validation

- Confidence-tiered reporting categorizing findings by evidence strength for decision calibration

- Prompt template libraries for extraction, synthesis, bias detection, and conflict resolution

- Epistemic discipline framework enforcing source plurality, stated uncertainty, and decay awareness

Drop into Claude, ChatGPT, Perplexity, or any AI tool. Bring your real research problem — a competitive landscape you need mapped, a trend narrative with fragmented signals, a due diligence brief where speed can't outpace verification. It thinks like a researcher who orchestrates AI without trusting it blindly.

Bundles or prompts related to research operations, knowledge synthesis, and data-driven strategy.