For Investors — Research Navigator

Market Pain Points

Why founders struggle to get credible validation at the speed of their market.

High, front‑loaded research costs (typical agency quotes of $15K–$50K) with unclear ROI.
Slow cycles: insights arrive after the opportunity window closes.
Generic AI advice lacks methodological rigor and is hard to trust.
Evidence is fragmented across interviews, surveys, and public data—difficult to synthesize.
Privacy and compliance concerns create friction and stall exploration.

Human‑in‑the‑loop research enhanced by reliable, evaluated AI workflows.

Hypothesis mapping → evidence collection (qual + quant + desk) → synthesis → decision memo. Each step is instrumented for repeatability.

Modular engagements starting sub‑$1K up to multi‑sprint programs, aligning rigor with stage and risk.

Prompt/policy regression tests and guardrails ensure reliability; outputs are auditable and reproducible.

Assumptions map, evidence ledger, and decision memos that translate directly into milestones and risk reduction.

Experienced operators and researchers building practical AI for validated decisions.

Quant + qual background, 10+ years in evidence synthesis and product validation. Prior roles in analytics and grant writing.

Applied LLMs, workflow automation, data pipelines. Focus on reliability, evaluation, and cost/performance trade‑offs.

Domain experts across methodology, product, and commercialization. Formal advisor roster in progress.

Lean, modular, and defensible where it matters.

Composable pipeline: retrieval, planning, evaluation; cloud functions + Next.js front‑end.
Evaluation-first approach with benchmark harness for prompt/policy regression testing.
Reusable templates for grant proposals, study designs, and report generation.
Data governance: privacy-by-default, encryption at rest/in transit, PII minimization.

Milestones, early signals, and what’s next.