Open-Source Revenue Intelligence

Ask nothing. Know everything.

AI agents that connect to your existing Stripe, Postgres, and CRM — unify fragmented records, detect anomalies, and explain the “why” behind every metric. Delivered to Slack before your first meeting.

< 3 hrs

Setup to first insight

93%

Record-matching accuracy

< 2 hr lag

Data freshness

Automated

Monday reports

80%

of companies cannot operationalize AI on their own data.

$47B

spent annually on data modernization programs

73%

of migration projects exceed their timeline by 2x+

12+

disconnected data systems in the average SaaS team

You shouldn't need a six-month migration to understand what happened to your revenue last week.

How It Works

From connected systems to daily intelligence in three steps.

01

Plug in your existing tools

Read-only connectors to Stripe, Postgres, MySQL, and your CRM. No migration project, no warehouse dependency. Connect in minutes, not quarters.

StripePostgreSQLMySQLHubSpot
connectors
S

Stripe

Connected

P

Postgres

Connected

H

HubSpot

Connected

M

MySQL

Connected

Read-only access · Encrypted credentials · SOC 2

02

Resolve who is who, automatically

Vesh matches customer records across every system into a single canonical entity graph. MRR, churn, and retention are computed from canonical definitions — so every team sees the same numbers.

Entity ResolutionMetric OntologyConfidence Scoring
entity-resolution

Resolving customer records across systems...

Stripe

cus_NhJ8kLm

sarah@acme.io

plan: growth

98%

Postgres

user_4871

Acme Corp

seats: 47

Resolved → Acme Corp

ent_00472

MRR: $4,230 · health: at-risk · seats: 47 · NRR: 89%

03

Get answers in Slack every morning

A daily brief with key metrics, anomaly alerts prioritized by business impact, and weekly synthesis — all delivered where your team already works. No dashboards to check.

Daily BriefAnomaly AlertsThread Follow-Up
slack — #revenue-intel
V
Vesh AIAPP8:00 AM

Daily Revenue Brief — Jan 15

$1.24M

MRR

+$18K

Net New

3.1%

Churn

Anomaly Detected

Enterprise churn spiked 2.4x vs 30-day avg. Root cause: 3 accounts using Feature X < 3x/week churned simultaneously.

Confidence: 94% · View lineage →

Trust Layer

Every number has a source.
Every insight has a confidence score.

Vesh doesn't ask for blind trust. Full lineage from summary to source record, confidence percentages on every metric, and human-in-the-loop for uncertain matches.

lineage-trace

$ vesh trace “MRR dropped $15K”

Insight

confidence: 94% · generated: 08:22 UTC

Anomaly Detection

method: rate_of_change · severity: 0.87

Metric: churn_mrr = -$33K

records: 8 · confidence: 0.96

Entity: Acme Corp (ent_00472)

stripe:cus_NhJ8 (0.98) ↔ postgres:4871 (0.91)

Source: stripe.subscriptions[sub_Abc123]

status: active → canceled · extracted: 07:45 UTC

Full audit lineage
Confidence scoring on every metric
Human-in-the-loop for uncertain matches
Wall of love

Loved by our customers

Here's what people are saying about us

SC

Sarah Chen

@schen_saas

Vesh found a product issue driving churn that we'd missed for 3 months. It correlated feature usage data with cancellation patterns across our Stripe and Postgres data — something we'd never joined together manually.

DG

Demetria Giles

@dgilescfo

Playing around with @veshai. I'm back logging key thoughts, details and soundbites from MRR trends, churn drivers, and expansion signals from the past week. So far, it's a data team's dream come true.

RD

Ryan Delk

@rdelk

Don't take it from me: @veshai is magic. We went from weekly manual data pulls to daily automated revenue briefs in under a week. The anomaly detection caught a billing issue before our finance team noticed.

MW

Marcus Webb

@marcwebb

The entity resolution alone is worth it. We had the same company appearing as 14 different records across Stripe and Postgres. Vesh merged them in minutes with 98% confidence.

SC

Sarah Chen

@schen_saas

Vesh found a product issue driving churn that we'd missed for 3 months. It correlated feature usage data with cancellation patterns across our Stripe and Postgres data — something we'd never joined together manually.

DG

Demetria Giles

@dgilescfo

Playing around with @veshai. I'm back logging key thoughts, details and soundbites from MRR trends, churn drivers, and expansion signals from the past week. So far, it's a data team's dream come true.

RD

Ryan Delk

@rdelk

Don't take it from me: @veshai is magic. We went from weekly manual data pulls to daily automated revenue briefs in under a week. The anomaly detection caught a billing issue before our finance team noticed.

MW

Marcus Webb

@marcwebb

The entity resolution alone is worth it. We had the same company appearing as 14 different records across Stripe and Postgres. Vesh merged them in minutes with 98% confidence.

JM

Jeremy McPeak

@jwmcpeak

I just got access to @veshai, and holy crap! It is well thought out, and I can see this being my go-to revenue intelligence layer going forward. Well done! I'm looking forward to seeing how the app progresses.

FR

Fabrizio Rinaldi

@linuz90

I'm keeping @veshai open all the time, and I'm using both for simple journaling and long-form anomaly analysis. It's rare to see a single app work so well for both technical and executive audiences.

JS

Jonathan Sima

@jdsimao

All righty. I have to say @veshai has really matured to a point where speed, focus, and a few bits of structured data context make it dramatically better than manual analysis.

PN

Priya Nair

@priyanair_ops

Saved our VP of Finance 4 hours every Monday. The weekly digest arrives before they even open their laptop. Confidence scores on each metric means we actually trust the numbers now.

JM

Jeremy McPeak

@jwmcpeak

I just got access to @veshai, and holy crap! It is well thought out, and I can see this being my go-to revenue intelligence layer going forward. Well done! I'm looking forward to seeing how the app progresses.

FR

Fabrizio Rinaldi

@linuz90

I'm keeping @veshai open all the time, and I'm using both for simple journaling and long-form anomaly analysis. It's rare to see a single app work so well for both technical and executive audiences.

JS

Jonathan Sima

@jdsimao

All righty. I have to say @veshai has really matured to a point where speed, focus, and a few bits of structured data context make it dramatically better than manual analysis.

PN

Priya Nair

@priyanair_ops

Saved our VP of Finance 4 hours every Monday. The weekly digest arrives before they even open their laptop. Confidence scores on each metric means we actually trust the numbers now.

48h

Time to first value

23%

Churn reduction in 60 days

93%

Entity resolution precision

87%

Insight approval rate

Get started

From messy data
to clear decisions.

Join the companies replacing dashboard complexity with proactive intelligence.