Your stack, your boundary
Embedding, indexing, retrieval, and generation all run on hardware you control. Zero external API calls. Zero silent data egress. Zero vendor handshakes to answer a question your team already owns.
A retrieval platform built so sensitive corpora never have to leave the environment they were authored in. For regulated organisations where internal knowledge is a strategic asset, and exposing it to a public AI service is off the table.
Every RAG deployment answers two questions: where does the corpus live, and who gets to retrieve from it? Public RAG services route both through a third party. An on-prem deployment answers both internally. Your infrastructure provides the physical boundary; RBAC provides the logical one.
The payoff is a platform your legal team can audit, your platform team can operate, and the people whose work it surfaces can trust. No renegotiating data-processing agreements every time a model weight changes upstream.
Embedding, indexing, retrieval, and generation all run on hardware you control. Zero external API calls. Zero silent data egress. Zero vendor handshakes to answer a question your team already owns.
Role-scoped retrieval means the same query returns different answers for different people, and the platform knows why. Your existing identity and access rules become the substrate, not an afterthought.
Citation-bound responses, verification loops, and operator-visible reasoning chains. Your legal and compliance teams can reconstruct the trail behind any answer in minutes.
Open-source LLMs run inside your environment. Roadmap includes distilled, domain-tuned models trained privately on your corpus and deployed privately alongside it.
A concrete set of platform capabilities and documentation, shipped as one coherent delivery rather than a box of components for you to assemble.
Platform-native ingestion covers common document formats and standard repositories. Connectors to proprietary systems, specialised DMS stacks, legacy content engines, or bespoke data pipelines get scoped per engagement so the integration work stays bounded and predictable.
We start with repository mapping, access constraints, and corpus quality. Model selection comes later. From there we produce a deployment blueprint that aligns platform architecture, compliance posture, and rollout sequencing, so the first domain ships value before the second one starts.
Need a concrete architecture proposal? Start a conversation, or explore GRIMOIR directly.