LEA.

The problem

Product practitioners invest heavily in credentials and programs — SVPG, ORSC, and others — then default to the one or two techniques they remember best. The full body of knowledge sits unused, not through intent but because no system surfaces the right technique at the right moment in a live engagement.

The cost is real. In one documented engagement, a team shipped free airport WiFi tested on employees rather than passengers; take rate came in below expectations. Standard techniques like value-stream mapping surfaced symptoms — slow delivery, broken handoffs — but could not explain *why* the team kept building without customer evidence, aligned objectives, or tested assumptions.

The solution

LEA (Learning Empowered Advisor) is an AI diagnostic for product discovery teams. A practitioner works through a structured 43-question discovery across five phases plus 12 ORSC team-dynamics questions, tagging discovery methods and stakeholder profiles as they go.

LEA then ranks the right techniques from a 71-item library (51 SVPG techniques, 20 ORSC tools) by relevance, urgency, and sequencing — with every recommendation attributed to a specific discovery finding. It flags gaps, surfaces contradictions rather than resolving them silently, and generates a leadership-ready summary. In the airport-WiFi engagement, LEA identified three systemic gaps — Customer Voice, Data-Driven Decision Making, and Stakeholder Alignment — in one week, which became three strategic tracks for the team's next initiative.

How it works

LEA's AI layer is semantic retrieval via FAISS vector search, not a generative model — a deliberate choice so every recommendation traces to a specific finding, which a generative model could not guarantee. The frontend runs entirely in the browser; on Generate Summary, discovery findings are sent to a hosted retrieval backend on Railway that returns the most relevant techniques by meaning rather than keyword match, and LEA's built-in logic scores, sequences, and writes the rationale.

This replaced an earlier keyword engine that returned different results for "no customer interaction" versus "no customer access." An anonymization layer strips names and organizational identifiers before findings leave the machine. A generative model is planned only once LEA has data across ten or more engagements for cross-engagement pattern recognition.

Who it's for

The primary user is the product practitioner — coach, consultant, product manager, or product owner — who works with multiple teams simultaneously and cannot hold their full body of knowledge in active recall across every engagement. Value scales directly with engagement volume.

LEA is early-stage: deployed since December 2025 and in active use across one completed live engagement, with three additional practitioners (Jerold, Eva, and Tiffany) onboarding before June 2026. Monetization is deferred; the eventual model is B2B licensing to coaching firms and enterprise L&D teams, whose value proposition is consistent, evidence-based practice outcomes across every practitioner they employ.

Why it matters

The corporate leadership training market is growing at 8.8% CAGR, and the coaching platform market is projected to grow from $4.22B in 2026 to $12.01B by 2036. AI has accelerated engineering velocity, shifting the bottleneck from build speed to discovery quality — teams now ship faster than they learn.

LEA's success definition is producing technique recommendations a senior practitioner would endorse, consistently, across different team contexts, without the builder present. Documented evaluations (EVAL-001 through EVAL-004) confirm the retrieval system outperforms keyword scoring and generalizes across contexts — including one honest failure where LEA produced no output despite 74% question coverage, now queued for a fix. Rather than entering a crowded market, LEA is defining the category of AI-native practitioner knowledge application.

At a glance

Project

LEA

Built by

Suraj John

One-liner

LEA is an AI diagnostic for product discovery teams trying to understand why shipped experiences underperform.