One of the biggest blockers to building agents is getting the data 'agent-ready'. Teams spend months building pipelines, wiring up sources, cleaning data, and centralizing it - before an agent can even ask its first question.
Pylar now does this out of the box.
We re source-agnostic. Whether your data lives across multiple databases and warehouses (Supabase, Snowflake, MySQL, etc.), you can connect one or many instantly, no re-architecture required.
If you don't have a warehouse yet, we ve got you covered. Pylar ships with 100+ built-in integrations across marketing tools, CRMs, support platforms, product databases, and billing systems. Data comes in cleaned, transformed, and centralized, ready for agents to work with.
Next up is agent views - once you've connected to your sources, you can write SQL across or within to create precise, sanitized, sandboxed views purpose built for specific agents.
Agents don t roam your databases arbitrarily. You deterministically scope exactly what fields they can access, so they do their job well, without hallucinating or giving you different answers for the same/similar questions.
Give it a try and let me know what you think!
Prava
Custom APIs are a nightmare and MCP servers are a minefield. This feels like the first real governed layer built for agents. Good work!
Pylar
@zerotox Thanks for your support!
Triforce Todos
Congrats Hoshang! Pylar seems like a huge step forward for safely connecting agents to structured data.
Pylar
@abod_rehman Thanks for the support, Abdul!
DiffSense
Don't fully understand what this is. What's the top 3 use cases for this?
Congrats on shipping this! The security positioning is smart, this is going to be a huge concern as agent adoption scales. Quick observation: your hero has two competing CTAs ("Talk to us" vs "Get started free") that aren't clearly differentiated. For most B2B SaaS, that split creates friction and hurts conversions. Either prioritize self-serve with "Get started free" as the main CTA and hide "Talk to sales" in the nav, or lead with a demo if that's your primary motion.
Good luck on your project!!
Pylar
@leasdsgn very helpful feedback. Thank you! We will work on our landing page CTAs.
Web to MCP
Pylar
@thepmfguy Amazing! Thanks Gaurav. Do let me know if you need anything!
TransferChain
Congratz on the launch team! With agentic AI this will be a must.
Pylar
@mertbaser Thanks for the support, Mert! Would love to have you try Pylar out!
Makers Page
Finally, someone tackling the agent-to-DB mess. I’ve nursed a painful Snowflake bill from a runaway agent. Sandboxed views + audit logs feels sane. How do you cap query spend per agent? Might wire this into Cursor first, then LangGraph if it holds up.
Pylar
@alexcloudstar Thanks Alex! Right now, we cap spend in two ways:
1. Every agent only sees a sandboxed view, never your raw warehouse.
So even if it tries something wild, it can’t fan out into expensive tables or join half your schema.
2. Query-level guardrails on the tool itself.
We let you set limits on row counts, frequency, and even block certain patterns (e.g. unscoped scans) via policies. If an agent tries to exceed it, Pylar shuts it down and logs the attempt.
On top of that, you get full audit logs + costs per tool call so you can see exactly which agent is expensive before the bill shows up.
Looking forward to having you try us out!