Enterprise AI’s data horse race just got more interesting with a new $50 million Series A announcement from WisdomAI, led by Kleiner Perkins and NVentures (Nvidia’s venture arm). The raise follows a $23 million seed led by Coatue, and brings total funding to $73 million for the Rubrik alumnus-led startup, which was founded by Soham Mazumdar.
The pitch is short and sweet: allow business users to ask natural language questions across dirty enterprise data and receive reliable, pat answers with zero LLM hallucinations. Across the sprawl of data in warehouses and apps, CIOs are seeking speed while retaining control—WisdomAI is betting its architecture can deliver both.
What WisdomAI Actually Does for Enterprise Data
Since the need for a solution is not infinite, as more specific queries are generated by LLMs and pushed out to company data sources, we’ll naturally start moving out of research into development. That approach funnels AI strength toward translation—of natural language into SQL, say, or its equivalent—while preserving the “truth” at a layer close to the underlying systems of record.
The company constructed an “enterprise context layer” that learns a customer’s schemas, joins and business definitions, and maps synonyms and quirks in “dirty” data. In practice, that looks like a sales manager being able to ask, “What is blocking deals this quarter?” and the platform resolves fields, permissions and lineage before issuing the query and sends back citations for auditing.
For security-heavy companies, this is important. By generating results from governed sources instead of LLM free text, it’s easier to enforce row-level policies, capture provenance and reproduce results—core parts needed, as outlined in the Gartner article, for enterprises scaling up generative AI.
Early Traction with Big Enterprise Logos and Growth
Since a late-2024 launch, WisdomAI expanded from a couple of pilots to about 40 enterprise customers, among them Descope, ConocoPhillips, Cisco and Patreon. A few accounts took off after early trials, like one reportedly growing from 10 seats to around 450 seats, with their use of the platform doubling in just a few weeks as non-technical teams embraced it.
Mazumdar and a group of co-founders earlier helped build at-scale systems for storing data at Rubrik, an expertise that resonates with buyers trying to navigate warehouse complexity. And this credibility is driving adoption beyond analytics teams to finance, support and operations.
Why Kleiner Perkins and Nvidia are leaning in now
Investors at the highest levels are pursuing platforms that will make AI more than experimental demonstrations and something closer to day-to-day decision making.
Kleiner’s participation is a sign of confidence that natural-language analytics is going from novel to necessary, especially as companies standardize on Snowflake, BigQuery, Databricks and lakehouse patterns.
NVentures brings another dimension: enterprise analytics increasingly relies on GPU-accelerated inference and vector search, and Nvidia has invested in companies that extend the AI stack beyond models to data, orchestration and agents. IDC and other market analysts have noted that data preparation and integration continue to be the greater part of AI project effort—exactly the agony that WisdomAI serves.
From static dashboards to proactive, reliable agents
Aside from Q&A, WisdomAI has also introduced an agentic feature that monitors metrics and events and alerts users only when something significant changes. Think of it as an always-on analyst that watches product usage, ticket backlogs or pipeline health and pings you when thresholds are crossed or anomalies arise.
It’s a move from pulling weekly dashboards to getting targeted, explainable alerts. In other words, for overworked teams, fewer reports and the right timing can often be more important than scaling up the number of charts. The key to it all will be accuracy: too many false positives, and the “AI analyst” becomes meaningless background noise.
How it compares in the crowded enterprise AI field
Incumbents are scrambling to incorporate natural language BI: Microsoft has Copilot across Fabric and Power BI, Google is baking Gemini into BigQuery, Databricks has Genie and notebook copilots, while ThoughtSpot and Tableau both have products around conversational analytics. WisdomAI’s differentiator is its query-only LLM architecture, served through context modeling and a governance-first posture.
Buyers should evaluate controllability (i.e., can administrators approve or modify produced queries), lineage and citations, complex joins between structured and unstructured sources, and deployment models that meet compliance. It’ll be accurate benchmarks, not demos, that distinguish resilient platforms from wrappers.
What to watch next as WisdomAI deploys funding
With fresh funding, anticipate WisdomAI will extend connectors and security controls further, into evaluation and guardrails that describe answer quality in some detail. Partnerships with data platforms and systems integrators frequently come next for startups at this stage, and a push to standardize pricing around usage-based setups as adoption spreads beyond analysts.
The wager is simple: if companies can question all their data, with precision—and trust the answers—analytics will become faster, cheaper and much more prevalent. If WisdomAI maintains the win rate it is enjoying early and can keep hallucinations off the critical path, this round could be the beginning of a much larger footprint in the modern data stack.
