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Know exactly what
to build next.

Connect your Zendesk, Gong, and Salesforce — and know exactly which customer problems are costing you the most revenue. That’s PDSA.

Product Discovery Synthesis Agent — built for enterprise product teams.

↓ Live pipeline demo
pdsa — pipeline demo
Zendesk
INPUT

"The SSO integration with Okta keeps failing. Our enterprise security policy requires SSO and we can't onboard our team without it. This is a hard blocker."

AGENT 00 · INGESTIONprocessing…
AGENT 01 · EXTRACTION
AGENT 02 · CLUSTERING
AGENT 03 · QUANTIFICATION
Running pipeline…
1/3
The Problem

Frequency ≠ Priority.

A real Series C company prioritized "performance issues" based on 2,000 support ticket mentions. When feedback was weighted by ARR, performance issues represented only $200K of revenue impact.

Meanwhile, "missing SSO" appeared just 34 times — but represented $2.4M in at-risk ARR.

"Performance Issues"Wrong priority
Mentions: 2,000ARR: $200K
"Missing SSO"Real priority
Mentions: 34ARR: $2.4M

“PDSA surfaced a $2.4M SSO blocker we had been deprioritizing for a quarter. We shipped it in the next sprint. That single insight paid for the tool a hundred times over.”

SK
Sarah Kim
VP of Product, Series C SaaS · $45M ARR
How It Works

Three agents. One pipeline.
Zero manual synthesis.

AGENT 01

Extraction Agent

Decomposes long-form text into discrete, atomic claims of ≤2 sentences each. Every claim traceable to the source.

AGENT 02

Clustering Agent

Embeds each claim using vector embeddings. Compares against existing themes. Routes to HITL at 70–84% confidence.

AGENT 03

Quantification Agent

Cross-references CRM to attach ARR. Calculates Weighted Severity = Σ(ARR × Frequency) / Total Active ARR.

Features

Everything a product team needs
to prioritize with confidence.

Multi-Source Ingestion

Connect Zendesk, Gong, Salesforce, App Store, and Slack. All feedback normalized into a unified schema automatically.

3-Agent AI Pipeline

Extraction, Clustering, and Quantification agents work in sequence. Each with a single responsibility, independently evaluated.

ARR-Weighted Insights

Every insight is weighted by customer ARR. A $500K customer's complaint carries more weight than a free-tier user's.

Human-in-the-Loop

Low-confidence clusters route to your review queue. Approve, Merge, or Reject — every action improves future accuracy.

Anomaly Alerts

Real-time spike detection. When a theme's mentions jump 200%+ in 24 hours, you know before your customers escalate.

Evaluation Framework

Track Clustering Accuracy, Hallucination Rate, HITL Override Rate, and Time-to-Insight. Drift detection built in.

Open Source

Use the agents
in your own stack.

All three PDSA agents are open-source. Run them as Python scripts, or copy the prompts directly into ChatGPT or Claude — no setup required.

01
Extraction Agent
Raw text → atomic claims
02
Clustering Agent
Claims → product themes via embeddings
03
Quantification Agent
Themes → ARR-weighted insights
terminal~$0.20 to process 100 feedback items
# Install and run in 3 commands
pip install -r requirements.txt
cp .env.example .env  # add your OpenAI key
python run_pipeline.py --input data/sample_feedback.json --output results/
By the Numbers

Built to enterprise standards
from day one.

5
Data sources connected
3
Specialized AI agents
<4h
Time-to-insight
<1%
Hallucination rate
90%+
Clustering accuracy
≥85%
Insight accuracy target
Governance Built In

Every insight is grounded.

PDSA has a formal Behavior Specification — constraints, failure modes, and fallback behaviors. Every insight must cite ≥3 source quotes. Zero-evidence claims are auto-rejected.

constraints:
≥3 source quotes required
Zero-evidence claims auto-rejected
ARR sourced from CRM only
failure_modes:
Low confidence → HITL queue
Model drift → auto-pause

Ready to stop guessing
and start shipping?

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