Vision
The Intelligence Layer for Fragmented Data
Most companies cannot operationalize AI on top of fragmented data. Vesh AI turns that bottleneck into a deployable intelligence layer.
The Problem
AI adoption is blocked by data fragmentation
Companies are told to modernize their full stack before AI can deliver value. The result is expensive, multi-quarter programs with uncertain outcomes, while leadership still lacks timely operational insight.
$47B
Estimated annual spend on modernization programs
73%
Of migration projects that exceed timeline by 2×+
80%
Of teams without AI-ready semantic data
The Insight
We do not replace your stack. We make it intelligible.
Vesh AI resolves entities across systems, canonicalizes business metrics, and applies a reasoning layer that explains change with confidence and lineage — without requiring full data migration.
Virtual Integration
Read-only connectors that normalize schemas in-place. No ETL pipelines, no warehouse required.
Semantic Layer
Entity resolution, metric ontology, and causal relationships create canonical business context.
Reasoning Engine
Anomaly detection, root cause decomposition, and NL explanation with confidence and lineage.
Traction
Early traction with strong usage signals.
3
Paying logos
5
Active deployments
87%
Insights rated helpful
2.1h
Median time to first insight
Why Now
Reasoning is commoditized; context is not
Model capability is no longer the constraint. Structured context, clean semantics, and trustworthy lineage are now the bottleneck.
Category demand is already validated
The proactive analytics category has attracted significant capital, but existing products largely assume clean warehouse-first data. Vesh focuses on the much larger fragmented-data majority.
Fragmentation compounds every quarter
Each new SaaS tool creates another data boundary. The need for a unifying intelligence layer grows as systems proliferate.
Competitive Position
Positioned where incumbents struggle.
Why Vesh Wins
Clear answer to diligence:
why us, and why now.
Why us
- • Built bottom-up around entity resolution and semantic integrity, not top-down prompt wrappers.
- • Productized trust with confidence scoring + full lineage as first-class features.
- • Focused wedge (SaaS revenue intelligence) enables repeatable onboarding and fast time-to-value.
- • Founder-market fit in data infrastructure and revenue systems.
Why we win
- • Incumbents optimize for clean warehouse data; we win in the fragmented-data majority.
- • Every onboarded company strengthens matching and metric mapping, compounding product accuracy over time.
- • Daily workflow ownership in Slack creates behavioral stickiness and high switching costs.
- • Land-and-expand motion supports attractive retention and upmarket expansion.
Team
Built by someone who lived the problem.
Shailesh Tripathi
Founder & CEO
Years of experience in data engineering and enterprise software, focused on revenue systems and operational analytics. Built and managed data pipelines that served finance and RevOps teams at scale — and repeatedly watched expensive, multi-quarter modernization programs delay the insights leadership actually needed.
Vesh AI was born from a simple conviction: companies shouldn't need to rebuild their entire data stack before AI can deliver value. The intelligence layer should meet your data where it lives — fragmented, messy, and real.
Hiring: founding engineers
We're looking for engineers who care about data quality, semantic systems, and building products that earn trust. If this resonates, reach out.
hello@veshai.comIf this thesis resonates, let's talk.
We are speaking with investors and strategic operators who believe trustworthy AI analytics starts with semantic clarity, not dashboard volume.