DataGrout

Enterprise AI Platform for Agentic AI & MCP Integration

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Enterprise AI Platform for Agentic AI & MCP Integration

73 followers

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AI Workflow Automation

DataGrout is an AI agent connectivity platform that enables AI agents to connect and interact meaningfully with enterprise applications like SAP S/4HANA, Oracle Fusion Cloud, Workday ERP, NetSuite, Microsoft Dynamics 365 ERP, Salesforce CRM, HubSpot, and more. DataGrout offers a variety of MCP servers, as well as agent connectivity via A2A and ACP. Enhance your agent's capabilities with enterprise-grade connectivity, deeper application context, robust security, and scalable performance.

This is the 2nd launch from DataGrout . View more

DataGrout Invariant

Launched this week

Semantic code analysis for the AI era

Ship AI-generated code with confidence. Invariant, the semantic code analysis tool for AI agents, specializes in AI code review & agentic coding pipelines. It extracts facts, runs Prolog queries to detect security risks, intent mismatches, ensuring code goals are met. Prompt injection prevention grounds agent reasoning in verified code facts. Integrate Invariant into your agent’s workflow to empower self-correction, bug prevention, and high-quality, secure code delivery, every time.

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Launch tags:Productivity•Developer Tools•Artificial Intelligence

Launch Team

FramerLaunch websites with enterprise needs at startup speeds.

Promoted

Hunter

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Hey Product Hunt 👋 We've been building DataGrout for a while now, an intelligence layer that helps AI agents connect to enterprise systems, plan workflows, and execute safely. One thing kept coming up as we built it: AI agents write code, but they have no real way to verify whether what they wrote actually does what they intended. That's the problem Invariant solves. What it does: Invariant is a semantic code analysis tool built for AI agents (and the developers who build them). It's not just a linter. It uses a neuro-symbolic engine , combining LLM-based semantic understanding with deterministic Prolog rules, to give you answers you can actually trust. Here's what it can do in a single workflow: code_lens — analyze source code and extract structured facts: which functions call what, what their intent is, what side effects they have code_query — query those facts with a Prolog engine. No LLM at query time. Results are reproducible and instant. Find orphan functions, test gaps, dependency cycles, security concerns, intent mismatches diff_analyzer — give it code before and after a change plus the goal you stated, and it returns an alignment score telling you whether the change actually did what you said it would review — a full automated code review: criteria, constraints, pass/fail verdict, per-item reasoning The part we're most excited about is the agent feedback loop. You can drop two lines into an agent's system prompt and it will call diff_analyzer automatically after making changes. If the alignment score is below threshold or there are unexpected changes, the agent revises before presenting its work. It's a self-correction loop that doesn't rely on the agent trusting itself. Why we built this: We kept seeing agents produce code that was plausible but wrong in subtle ways. The goal drifted. Functions were added that weren't needed. Constraints were silently violated. You'd only catch it in review or worse, in production. Invariant makes goal alignment a checkable property, not a hope. There's also a CLI for local use in CI pipelines. Facts extracted locally get uploaded to DataGrout for server-side enrichment and querying. If you're running AI in your code review pipeline, tell me where it's breaking down, that's the kind of feedback that actually shapes what we build next. —The DataGrout team

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3d ago

Previous DataGrout Launches

DataGrout Enterprise AI Platform for Agentic AI & MCP Integration

Launched on April 15th, 2026

Forum Threads

p/datagrout-ai

•

23h ago

We built a diff analyzer that scores whether a code change matched the stated goal

One of the core tools in Invariant is diff_analyzer.

You give it:

p/datagrout-ai

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2d ago

Your coding agent ships code that works but does the wrong thing. How are you catching it?

We kept running into this while building DatGrout Invariant.

The agent makes a change. Tests pass. No errors. But the behaviour doesn't match what you actually asked for. No crash to debug, no obvious failure, just silent goal drift that shows up later.

We called this the "plausible but wrong" problem.

The frustrating part is that existing tools don't catch it, linters check syntax, test suites check output, but nothing checks whether the change matched the original intent.

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