Zenlytic

Market position

Zenlytic positions itself against traditional BI tools and semantic-layer approaches, arguing that conventional BI is built for technical users while generic AI agents connected to warehouses improvise SQL and drift as context decays. Its stated differentiator is combining SQL flexibility with semantic governance and explainability. Published comparison content frames it against Power BI, TextQL, ThoughtSpot and Looker.

The company emphasizes deterministic query validation that decompiles executed queries into named metrics and flags which were human-approved, inline citations on every returned figure, explanations rendered in business rather than SQL terms, a self-assembling context layer built from existing modeling assets, observability into what the business asks and where answers fail, and a Git-based governance workflow for metric definitions. Founders cited the combination of deep data expertise and LLM expertise, acquired while building a BI tool as LLM capabilities improved in late 2022, as their competitive advantage.

Technology

The system pairs large language models with a Git-managed semantic/context layer. The Clarity Engine composes flexible SQL, then decompiles and maps executed queries back to governed metric definitions, marking which fields were human-approved versus generated on the fly, and can create dynamic measures and dimensions when definitions are missing. The modeling layer supports dynamic joins, merged results and unified dates, integrates with dbt or an internal data model, and ingests context from LookML, Power BI DAX and Snowflake Semantic Views. Connectors cover Snowflake, BigQuery, Redshift, Databricks, Athena, Azure Synapse, Trino, Postgres, MySQL, SQL Server, MotherDuck and Druid, plus dbt, Looker, Slack and Teams; MCP connectors were announced in June 2026. Infrastructure claims include configurable caching, 256-bit AES encryption, not storing customer data, row- and column-level permissions, an in-VPC option, SOC 2 Type II certification and SSO/SAML.

Go-to-market

The founders described marketing primarily through podcasts and public speaking engagements that generate inbound email, supplemented by product-led sales. The website supports a free trial and demo booking, and publishes an ROI calculator, case studies, comparison content against tools such as Power BI, TextQL, ThoughtSpot and Looker, and a technical blog.

The founders described the target market as mid-market companies with a small, overworked data team, a cloud data warehouse, and many end users waiting on insights; the company says it is vertical-agnostic, with ecommerce and financial services as the largest segments and additional customers in SaaS, consumer tech, healthtech and fintech. Website materials also position the platform for enterprise data teams and for ops, finance and go-to-market functions.

Geography

Headquartered in New York City, New York; no other office locations are stated in the sources. Cookie-consent disclosures reference US visitors and California privacy law.

History

Founded in 2020 by Ryan Janssen and Paul Blankley and based in New York City. The company raised a pre-seed round led by Primary Ventures and a seed round led by Bain Capital Ventures. Product milestones documented on the company blog include the Clarity Engine (August 2025), Artifacts (March 2026), context consolidation across external semantic layers and MCP connectors (June 2026), Snowflake Semantic Views support (June 2026), and Live Queries in Artifacts (August 2026).

Risks & controversies

No controversies are reported in the available sources. Notable limitations for diligence: funding amounts and valuations are undisclosed, customer and revenue metrics are absent, most product claims come from the company's own website, and the employee count of 11 reflects a single third-party interview. Product marketing acknowledges the general risk that AI agents improvise SQL and that semantic layers decay, which the company positions its product as mitigating.