AI-Native Data Platform
Accelerate AI Adoption Across the Enterprise.
Provide agents with the right context and data at the right time.
Smart enterprises automate end-to-end business workflows with AI.
Agents don’t just write code — they run every part of the business: optimizing marketing campaigns, resolving support tickets, uncovering insights in data.
Agents are only as good as
their context and data.
The tribal knowledge — what things mean, how you do them, the workflows.
Fragmented everywhere: across business apps and people's heads. Without it, agents sound right but are actually wrong — they don't know the business.
Your data — how the business is actually performing.
Built since the '80s for humans and BI dashboards, not for AI. Ambiguous and duplicated — 18 different definitions of "active user". The result: agents that hallucinate and guess.
"A crucial reason agents didn't work well was a lack of proper data context." — a16z, "Your Data Agents Need Context," Mar 2026
The AI enablement layer
Your company's brain
Connect your data sources and business apps to create your company's brain — where context and data definitions live together as code. Accessible to both AI and humans — visible, structured, and always up to date. Build with your favorite coding agent.
Your context stays yours. Forever.
Your company will run on agents, and your agents run on context. Keep that context yours — forever — by building it directly into your Git repository. No vendor lock-in.
Always healthy, always current
Our build plugin gives your favorite coding agent brain-building superpowers. We provide the tools to keep the brain healthy as your business and data evolve.
Deterministic SQL engine
Encapsulate complex SQL logic into simple, named terms. Agents just pick what they need by name — a deterministic SQL engine translates it into the complex queries that run against your warehouse.
Build with your favorite coding agent.
Lynk build plugin turns your coding agent into a semantic graph builder expert
Agent
Keep the brain healthy and up to date
Your company's brain evolves with your business. Keep it healthy as you scale.
Every business definition exists once
The Lynk semantics engine validates that every entity, metric, feature, and relationship has exactly one representation in the graph.
CI/CD pipeline
Make changes to the brain with confidence — as code.
Evaluations framework
Evaluate changes with your agents before deploying a new context version to the brain's production.
Complete change history
See the complete change log of every item in your company's context.
Collaborate
Work on context changes together — the build agent and your human teammates, side by side.
Stay in control
No black boxes. Changes stay transparent in your favorite ticketing system, and the semantics live in your Git repository.
Lynk data agents
Business users analyze data with trust. Analysts focus on moving the business needle.
Lynk analystLynk data scientistThe best analyst and data scientist in the world — out of the box.
Powered by your company's brain and a production-grade toolset:
Use them directly
Business users ask in natural language and get trusted answers, straight in the Lynk chat UI.
Power your own agents
Plug Lynk agents into external agents as tools — the assistants you already use, or the agents you build.
Build any agent, for any purpose
Give your agents the capabilities to work seamlessly with the brain and your data.
Lynk analystLynk data scientistClaude
ChatGPTSlackLatest blog posts
All posts
AI native data platform
The Fivetran-dbt Merger: Why Now and What Comes Next
The Fivetran-dbt merger marks a pivotal shift in data infrastructure. This post analyzes the reasons behind the merger, current industry trends, and argues it signals the end of the Modern Data Stack era and the rise of AI-driven platforms.
Liran Cohen
Central Source of Truth
DBT As An Industry Shaper: The Pros and Cons
Not many products get to shape an entire industry and become a tool used by millions of people. dbt successfully did this in the data industry, enabling data teams to do things they couldn’t do before.
Liran Cohen
Central Source of Truth
Why Self-Service Analytics Still Fails—And How AI is Changing the Game
Liran Cohen
Data Quality
Deep dive into data quality issues: causes and solutions
In this blog post we will dive into the main causes of data quality issues and offer optional solutions. We will also see which types of data quality issues can be solved by a universal semantic layer and how.
Liran Cohen