Dawiso AI Context Layer - Connect AI agents to governed metadata via MCP
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AI fails in enterprises not because of models, but because it lacks context. Dawiso’s AI Context Layer turns data catalogs into the semantic backbone for AI. Defining meaning, ownership, access, and relationships. Connected to AI agents via MCP, it enables AI to answer the right question, for the right user, with the right data.
This context is generated automatically through metadata scanning and AI enrichment, with human-in-the-loop governance ensuring it stays relevant, and trustworthy.
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Dawiso
Minara
What stood out to me about Dawiso is how grounded the experience feels.
Instead of trying to impress with complexity or surface-level polish, the product seems focused on helping users stay oriented — clear structure, calm pacing, and interactions that don’t demand constant attention. From a UX perspective, that kind of restraint usually comes from understanding where users actually get stuck.
As a first impression, Dawiso feels steady and intentional rather than flashy, which makes it easier to trust and come back to over time.
@rexlian Really happy to hear this, Rex!
This is exactly what we're aiming for. A tool that helps you get things done without getting in your way. We deliberately avoid adding complexity just for the sake of looking impressive.
A lot of this comes from two things: the deep experience of our team (many of us have spent years working directly with data governance challenges) and, most importantly, continuous feedback from our customers. They keep us honest about what actually matters versus what just looks good on a feature list.
Thanks for taking the time to share your thoughts, it means a lot!
Congrats on the launch — love how Dawiso brings trusted context to enterprise AI.
The framing around context being the missing layer resonates. We’ve seen LLMs give confident but wrong answers when metadata is thin or inconsistent.
Curious how much of the context generation is automatic versus refined manually by domain experts.
Congrats on the launch! Framing this as a dedicated context layer instead of another AI-on-top interface feels like the right abstraction, especially if it reduces misleading answers at the source. How granular the generated context gets, does it stay at schema/metadata level, or can it capture business logic and domain rules deeply enough to influence AI reasoning?
The MCP integration for exposing governed context to AI agents is interesting. For teams working with both structured databases and unstructured content like documentation or articles, does the context layer handle these differently, or is there a unified approach to surfacing relevant meaning regardless of source type?
Do you have any benchmarks or case studies showing how much the accuracy of AI answers improved after adding the Dawiso context layer?