The quiet trust layer beneath enterprise AI - clean data in, real answers out.
DVSUM, LLC — The DV monogram hides a checkmark: the teal ‘V’ doubles as a tick, a nod to validated, trustworthy data. Sunnyvale, California.
Everyone wants AI. Almost no one wants to admit their data is a mess. DvSum, founded in Sunnyvale in 2014 by Aashish Singhvi, started with the unglamorous half of that problem - cataloging, cleaning and governing enterprise data - and a decade later that foundation is exactly what the AI era needs.
At its core, DvSum is a cloud-based Data Intelligence Platform. It unifies four capabilities that most companies buy separately - a data catalog to discover what data exists, data quality tooling to detect and fix discrepancies, data governance to control access and policy, and data lineage to trace where data came from - into a single, largely automated system.
On top of that foundation sits the part business users actually notice: AI agents. Its conversational agent, CADDI (Conversational AI for Data-Driven Insights), lets a non-technical employee ask a question in plain English and get a real answer from live company data, without writing SQL or filing a ticket with the data team.
The company frames itself, plainly, as “the metadata and governance layer for Enterprise AI.” The bet is simple: a model is only as trustworthy as the data underneath it, so DvSum automates the data first and lets the AI ride on top.
More recently, DvSum pointed that same sense-reason-act engine at a very different target - telecom networks - with an Active Network Intelligence stack branded AURA, designed to catch and resolve network issues before subscribers ever pick up the phone.
Mid-market and large enterprises across telecommunications, insurance, retail and consumer brands, distribution, financial services and the public sector. Named or referenced users include Liberty Latin America, US Venture and M.H. Alshaya. In 2024, DvSum was selected as a data intelligence platform by the U.S. FDA - a signal of governance and compliance credibility that matters to regulated buyers.
Data in most enterprises is scattered, inconsistent and hard to trust. Analysts wait days for answers; governance is manual; AI projects stall because no one can vouch for the inputs. DvSum attacks that by automating discovery, quality checks and lineage, then handing self-service query power to the people who actually need answers.
The core: one AI-automated platform unifying catalog, quality, governance and lineage across the entire data and analytics stack.
An augmented, active catalog so data and analytics teams can discover, monitor and govern data across their landscape.
Cleansing, workflow orchestration, address validation and write-back to detect and resolve discrepancies (DataPARC).
A conversational agent that turns plain-language questions into self-service, data-driven insights for business users.
An agentic stack for operators that senses network signals, diagnoses root causes and triggers automated remediation.
Most data tools force a choice between powerful and usable. DvSum's design bet is that you shouldn't have to pick - a governed foundation underneath, a chat interface anyone can use on top. Where larger incumbents sell heavyweight, separately-licensed modules, DvSum leans on automation and a single unified platform priced by data sources and users.
Its unusual move - repurposing a data-governance engine for live telecom network operations - is the clearest tell of the difference: the product is really a general-purpose sense, reason, act loop, not a single-market tool.
Relative scores are directional, based on Tracxn category positioning - not audited benchmarks.
B2B SaaS subscriptions priced by the number of connected data sources and users - reportedly starting around $1,000 per month - with enterprise deployments, managed services and industry-specific AI-agent solutions layered on top. Distribution is amplified through cloud and ecosystem partners including Snowflake and AWS.
Founder and CEO Aashish Singhvi spent roughly 15 years in supply-chain data analytics before starting DvSum - and it shows in the company's comfort with messy operational data most vendors avoid. A lean team of about 69 people runs the platform for a global enterprise base.
Annual revenue is estimated at roughly $3.9M. Funding details are not publicly disclosed; Tracxn lists one institutional investor, IIT Startups.
Aashish Singhvi launches DvSum in Sunnyvale to automate data quality and governance for enterprises.
Agile Data Quality (DataPARC) establishes cleansing, workflows and observability.
An augmented, active catalog spanning the full data and analytics stack launches.
CADDI conversational AI lets business users query data in natural language.
CADDI deploys at Liberty Latin America; DvSum is selected by the FDA as a data intelligence platform.
CommScope collaboration and Active Network Intelligence extend DvSum's agents to telecom operations.
DvSum repositions around AURA - AI that acts before subscribers call.
Empowering joint customers with self-service analytics on the Data Cloud.
Co-sell partner for cloud data intelligence and network AI solutions.
Collaboration on an advanced AI-powered network intelligence solution.
Selected for the Insurtech Vanguards program connecting insurers with innovation.
Integration with Genesys Engage for network-aware call containment and agent support.
Networks that act before subscribers call.
One Data Intelligence: Catalog, Quality, Governance and Lineage in a single automated platform.
The metadata and governance layer for Enterprise AI.
Product demos, CADDI walkthroughs and network intelligence explainers.
The CEO discusses building a unified AI strategy for enterprise data.
DvSum's show on deploying AI for customer service, from prototype to production.