How It Works
From fragmented systems to decision-ready insight
Connect your core data sources, resolve semantic ambiguity, and deliver executive-ready intelligence in Slack.
Architecture
End-to-end intelligence pipeline
From raw data sources to executive decision — every layer is automated, auditable, and confidence-scored.
AI Intelligence Layer
Connect
Connect your existing stack
Read-only connectors to systems you already use. No migration project, no warehouse dependency, and no disruption to current workflows.
Stripe
LivePostgreSQL
LiveMySQL
LiveHubSpot
ComingSalesforce
ComingSnowflake
ComingBigQuery
ComingOracle
Phase 2Security first
Read-only access by default. Credentials are encrypted at rest, and sensitive records never leave controlled infrastructure.
Understand
Build a semantic model of your business
Three coordinated layers transform raw data into trustworthy business context.
Entity Resolution
The same customer appears differently across tools. Vesh links those records into a canonical entity graph with confidence scoring and review workflows for uncertain matches.
Metric Ontology
MRR, churn, NRR, and retention are defined once, then mapped to your schema automatically so every team sees consistent numbers.
Causal Relationships
Metrics are modeled as relationships, not isolated charts. When performance shifts, Vesh decomposes impact into root causes.
Act
Deliver insight where teams already work
Proactive distribution replaces dashboard hunting. Insight arrives in Slack with context, confidence, and next actions.
Daily Brief
Every morning: key metrics, what changed, and why. Scannable in seconds and operationally useful immediately.
Anomaly Alerts
Alerts fire only when material shifts occur, prioritized by severity and expected business impact.
Weekly Digest
Weekly synthesis of trends, momentum, and emerging risks that daily snapshots can miss.
Thread Follow-Up
Ask follow-up questions directly in thread to move from signal to decision without leaving your workflow.
Trust
Trust is built into every insight
Vesh does not ask for blind trust. Each narrative is backed by lineage and explicit confidence signals.
$ 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
Ready to see it work on your data?
14-day pilot. No credit card required. Setup in 30 minutes.