Agents need a governed layer to query
Most enterprise AI programs stall in the same place. The model is fine. The demo was fine. Then somebody asks a question whose answer has to be correct, and it turns out the agent has been summarizing documents rather than querying data.
Why AI pilots stall before production
Retrieval cannot aggregate
Embedding-based RAG finds text that resembles the question. It cannot aggregate, join or reconcile, which is what most real business questions require.
Permissions get flattened
Indexing everything into one vector store tends to quietly dissolve the access controls that governed the source systems.
Answers without provenance go unused
An answer nobody can trace is an answer nobody will act on. Without attribution, every output needs manual verification, which removes the benefit.
A governed substrate agents can actually query
SetMeld gives agents, applications and analysts the same interface: one knowledge graph over every connected system, with a documented schema, datasource attribution on every result, and access control enforced at query time.
Representative systems. SetMeld connects to any structured dataset you have credentials for. No migration, no modernization prerequisite.
Answered in one query, with provenance
- Answer this question by computing over records rather than summarizing documents
- Show which source systems contributed to this figure
- Respect the same permissions the underlying systems enforce
- Return the same answer twice, and explain it months later
- Join across systems that were never designed to be joined
Why the answer is trustworthy
Every result is computed over governed, unified records rather than retrieved from a pile of text. Each answer carries datasource attribution back to the systems that produced it, and agents querying the graph inherit the same access controls as the people who own the underlying data.
The integration is generated, verified by our experts and approved by your team.
Deploy on-prem, in private cloud, or as SaaS with the graph resident in your network.
New sources are additional connections at no additional project cost.
What changes for an AI program
One layer, many consumers
BI tools, applications, analysts and agents all read the same governed model, so they stop disagreeing with each other.
Controls stay intact
Access control and provenance are properties of the graph itself.
Pilots reach production
The gap between an impressive demo and a deployable system is usually the data layer. This is that layer.
Give agents a governed layer to query
Once the layer beneath them is governed, most of the accuracy conversation goes away.