Signal Intelligence for Product Teams

Your signals already know
what to build next.

Vector ingests feedback from every channel, clusters it automatically, and tells you what's actually breaking — before your metrics do.

Pilot partner Sola Insurance

vector — signal pipeline LIVE

09:14:02SLACK→ ingesting 847 messages

09:14:02INTERCOM→ 312 support tickets

09:14:03APP_STORE→ 94 reviews (4d)

09:14:03GONG→ 28 call transcripts

09:14:04ZENDESK→ 541 tickets [open:203]

09:14:04NPS→ 189 responses [avg:6.2]

09:14:05TYPEFORM→ 77 churn surveys

09:14:05Vectorembedding 2,088 signals...

09:14:08VectorUMAP → HDBSCAN clustering...

09:14:11Vector✓ 5 clusters identified

// vector cluster output — 2,088 signals → 5 themes

Payment Friction643 signals

Onboarding Drop-off512 signals

Dashboard Load Speed421 signals

API Rate Limits314 signals

Mobile Export Bugs198 signals

// vector insight — generated from cluster #3

CRITICAL · TRENDING

Dashboard load speed is driving churn among power users

23% of churned accounts in the past 14 days cited slow dashboard loads as their primary reason for leaving. Signal volume up 38% week-over-week.

"It takes 8–12s to load my dashboard every morning" — Intercom, 14 instances

"Competitor X loads in under 2s" — Churn survey, 31 mentions

NPS detractors score dashboard speed 2.1/10 avg

cluster → Dashboard Load Speed View evidence

47 signal sources unified

2.3M signals processed this week

94% cluster accuracy

18 min avg time-to-insight

Zero manual tagging required

Real-time cluster updates

47 signal sources unified

2.3M signals processed this week

94% cluster accuracy

18 min avg time-to-insight

Zero manual tagging required

Real-time cluster updates

How it works

From raw signal
to clear action.

Four steps. No ETL. No taxonomy. No waiting for the analyst.

01

Connect your sources

Intercom, PostHog, Fireflies, your CRM, your database. One MCP integration layer — no ETL pipelines, no exports, no engineering sprint required.

02

Vector embeds and clusters

Every signal is embedded, reduced, and clustered. Related signals group automatically across source boundaries.

03

Insights surface in plain language

Each cluster synthesizes into a ranked insight — scored by density, velocity, business impact, and fix complexity. Written in PM language, not data science.

04

Action is automatic

Tasks, bug reports, and feature suggestions land in Jira or ClickUp with the full evidence chain attached. No ticket-writing. No manual triage. The loop closes.

6 connected sources

Intercom

live

PostHog

live

Gong

live

Zendesk

live

App Store Reviews

live

NPS Surveys

live

2,088 signals ingested · last sync 4s ago

5 themes · 2,088 signals

Payment Friction643

Onboarding Drop-off512

Dashboard Load Speed421

API Rate Limits314

Mobile Crash on Login198

model text-embedding-3-large algo HDBSCAN

// vector insight — cluster #1

CRITICAL · trending ↑ 34%

Payment flow failing silently at checkout — 643 users impacted this week

Users report cards declined with no error message. Stripe webhooks appear delayed. Issue clusters tightly around the 3-step checkout modal introduced in v2.4.

›"Card charged but order never confirmed. Had to call support."

›"Payment failed twice — no reason given. Switched to competitor."

›"Checkout spinner runs forever, then times out silently."

cluster #1 · Payment Friction Create ticket →

GitHub code attribution

When Vector classifies an insight as a code regression, Vector connects to your GitHub repository, analyzes recent commits and diffs against the affected time window, and pins the exact file, function, and author — no manual bisecting required.

Commit diff analysisFile-level blame Author attributionPR linkback Branch awarenessDeploy correlation

// vector code trace — Dashboard Load Speed

regression detected in src/dashboard/metrics.ts

commit a3f9c12 · merged 2 days ago · @jsmith

+148 lines in fetchMetricsBatch() — O(n²) query introduced

↗ correlated with +340ms p95 latency spike

Vectr AI

Ask anything about
your clusters.

Vectr AI reads your live clusters and answers like a senior product analyst who has already read everything — so you don't have to.

vectr-ai — product intelligence ● LIVE CLUSTERS

Why did churn spike this week?

Cluster #7 · Onboarding Drop-off is the main driver — up 41% since Tuesday. Users are hitting a dead end after the integration step with no clear next action. Intercom shows 28 tickets using phrases like "what do I do next" and "stuck on setup."

Sources: Intercom · PostHog · Fireflies View cluster →

What should we prioritize this sprint?

// VECTR AI · ranked by impact × trend

01 Fix silent payment failure at checkout — 643 users impacted, trending ↑34%

02 Add progress indicator to onboarding — root cause of this week's churn spike

03 Improve CSV export reliability — low volume, but high frustration score

Ask anything about your clusters...

Root cause analysis Cross-source pattern matching Sprint prioritization Trend detection Cluster explanation

Live in production

First run. Real data.
Real Insights surfaced.

SOLA

Insurance technology platform

203K+

SIGNALS PROCESSED

On its first production run against SOLA's live customer data, Vector surfaced a critical bug — a broken confirmation redirect causing 400+ insurance applications to submit twice in the Tuscaloosa market. No engineer had been assigned. No support ticket had been escalated. Vector found it by correlating 23 Intercom conversations, PostHog session replays, and 412 duplicate database records — cross-source correlation working exactly as designed.

Intercom · 23 tickets

PostHog · session replay

PostgreSQL · 412 duplicate records

Critical Duplicate submission bug — Tuscaloosa market

412 duplicate applications confirmed. Broken redirect on confirmation page. Affects mid-market insurance workflow. Cross-source correlation: high confidence. Recommended: immediate patch + outreach to affected applicants.

Integrations

Plugs into the stack you already have

No new data pipeline. Vector connects via MCP or API and ingests directly from your existing tools.

Slack

Intercom

HubSpot

PostHog

GitHub

Fireflies

PostgreSQL

ClickUp

+ more via MCP

Slack

Intercom

HubSpot

PostHog

GitHub

Fireflies

PostgreSQL

ClickUp

+ more via MCP

Comparison

Vector vs. the alternatives.

Other tools ask you to do the work. Vector does it for you.

Feature	Vector	Productboard	Pendo	Spreadsheet
Real-time signal ingestion
Automatic clustering
AI insight generation
No manual tagging required
Evidence-linked insights
Time to first insight	18 min	days	weeks	never

Get started

Your next critical insight is already in your data.

Request a demo and see Vector running on your signals in under 20 minutes.