DecisionBox
Autonomous AI Discovery For Your Data — Open Source
67 followers
Autonomous AI Discovery For Your Data — Open Source
67 followers
DecisionBox is an open-source project and connects to your data warehouse, runs autonomous AI agents that write and execute SQL, and surfaces validated insights and actionable recommendations, without you asking a single question.
This is the 3rd launch from DecisionBox. View more
DecisionBox Enterprise
Launching today
DecisionBox is an autonomous AI agent that writes SQL against your warehouse and ships validated findings. Enterprise runs it fully air-gapped: self-hosted LLMs via Ollama, open-source base models fine-tuned on your schema, SSO, RBAC, three-layer data governance, full audit log. Plugin architecture on an open-source AGPL v3 core — zero fork, zero outbound calls, zero bytes leave your network.
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Launch Team
DecisionBox
I love the fact this is open source and it gives this product much much more credibility !
Also would like to know how you are verifying claims using LLMs - its a big research topic now a days !
DecisionBox
@dhruba_patra Thanks, really appreciate that. Open source was non-negotiable for us. Nobody should have to trust a black box that's touching their warehouse.
On validation, it's not one trick, it's a few layers stacked together.
First, the agent isn't allowed to just report numbers it saw in an exploration query. For every claim it wants to make ("X% of users did Y"), it has to write a separate independent SQL query that verifies that specific number, run it, and check the result matches. If the numbers don't line up, the claim gets adjusted with the verified number or rejected entirely. Both outcomes are visible in the UI so you can see what the LLM originally said vs what your warehouse actually returned.
Second, every insight has to cite the exploration step numbers it came from, so you can trace any finding back to the raw query and results that produced it. No floating claims.
Third, affected counts are forced to be COUNT(DISTINCT user_id) in the verification SQL, which kills a whole class of LLM math errors.
It's not foolproof - an LLM can still pick a misleading angle on correct numbers - but it closes the hallucination gap hard. The honest framing is: the LLM proposes, the warehouse disposes.
DecisionBox
Adding one more thing, happy to go deeper on any of these in the thread:
• Air-gapped deployment: Ollama model choices, GPU requirements, outbound-call audit: https://decisionbox.io/enterprise/audit/
• Governance: the three-layer protection (schema filtering, result redaction, AI query rewriting) : https://decisionbox.io/enterprise/governance/
Drop your questions below, happy to answer.