Research

Structures a market sizing exercise with top-down, bottom-up, and sanity-check methods.

A grounded research assistant that only answers from the sources you give it.

Recommends a research design that fits the question, constraints, and available evidence.

Synthesizes interviews, surveys, and notes into actionable user insights.

Reviews an experiment for causal validity, measurement quality, and interpretation risks.

Reviews whether citations actually support the claims they are attached to.

Design a pragmatic delivery and operations model for a software system.

Design a production-ready AI agent architecture from a business goal or automation idea.

Turn APIs and actions into agent tools that an LLM can call reliably.

Audit an LLM application for direct and indirect prompt-injection vulnerabilities.

Combine lexical and semantic retrieval into a practical hybrid search design.

Investigate a software bug systematically and distinguish symptoms from root cause.

Review Kubernetes manifests or Helm values for reliability, security, and operational readiness.

Design an end-to-end retrieval-augmented generation system for grounded answers.

Design an AI-ready knowledge base with clean structure, metadata, ownership, and freshness.

Plan a safe refactor that improves design without changing intended behavior.

Turns observations into testable hypotheses with mechanisms, predictions, and falsification criteria.

Design a secure remote MCP server with clean tools, resources, prompts, and authentication.

Decide what an AI agent should remember, for how long, and under which privacy rules.

Score research sources for authority, relevance, recency, transparency, and bias risk.

Understand an unfamiliar repository and explain how the system fits together.