Inference-Time Data Control Plane
Most AI governance tools label data before use or detect issues after the fact. Caber prevents recurring inference failures by applying deterministic, policy-driven control to each chunk at time of use, before the model sees it.
Recurring AI failures come from recurring context assembly problems. They are preventable with deterministic policy at time of use.
Tenant boundaries break at retrieval time
A multitenant AI assembles an answer with chunks from the wrong customer.
Stale chunks drive new decisions
A retrieval system returns an older chunk after a newer version shipped.
Blunt policy creates new failures
A governance tool blocks or redacts chunks, leaving gaps that push the AI to hallucinate.
Why These Failures Are Systemic
The three scenarios above repeat because of six root causes. Hover each numbered point to see why label-before-use and detect-after-the-fact tools miss them, and what must be evaluated before inference to prevent recurrence.
Duplicates break provenance
Most tools today assume where a chunk was ingested from is its 'true source'. But at the chunk level, nearly all enterprise data is stored in multiple places with potentially conflicting contexts. Without duplicate-aware provenance, policy cannot reliably identify origin, ownership, or authority.
Labels decay after ingest
Labels assigned when data is curated become stale as business context changes. By retrieval time, the original label may not match the latest meaning, authorization needs, or source status.
Content without context
AI consumes chunks of content, like sentences, tables, and charts. But the context that give meaning to those chunks is hidden inside source documents, external systems, and databases. Rarely does the chunk content itself contain enough context to determine relevance.
User attribution lost
Agents often query sources with service credentials, which hides the requesting human. Without on-behalf-of attribution through APIs, MCP, RAG, and agent chains, policy decisions can be wrong and auditing becomes unreliable.
Governance is too late
Problems with stale data after inference can't be corrected after the fact because your data is continuously being updated. The control point must evaluate policy with the context on the chunks at the moment of inference.
Authorized but irrelevant
Document-level access control can return chunks that are individually allowed but semantically incompatible in the same response. Authorization alone does not ensure relevance, consistency, or safe composition.
Caber Prevents Problems Before They Happen
Deterministic Chunk Context
Caber does not guess meaning from semantic similarity alone, and it does not assume the chunk contains all required context. Caber deterministically identifies chunks, then continuously updates a Context Graph with the latest context on what each chunk means and how it should be used. This is the foundation for policy-driven prevention.
When What Data Looks Like Is Not What It MeansDuplicate-Aware Lineage
When the same sentence, table, chart, or byte sequence appears in many places, Caber treats that as signal, not noise. It links duplicate chunks across sources and transformations so policy can evaluate origin, ownership, freshness, and authority using the full picture.
How Duplicate Data Kills Your RAGGovernance That Tracks Meaning
Meaning and policy relevance change as documents move through workflows, versions change, and business context evolves. Caber continuously updates the Context Graph so governance reflects current context at time of use, not a stale snapshot from ingest.
Context Graphs for GovernancePolicy Control Before Inference
Caber evaluates each chunk before inference using policy and current context, including freshness, authorization, conflicts, and proper use. It can allow, block, redact, or replace individual chunks so the model receives clean, authorized, relevant input instead of bluntly truncated context.
Business Context on AI Data Won't Be Solved by RetrievalUser-Aware Actions Everywhere
When one agent calls another agent or tool, Caber preserves the identity of the person whose request started the chain. Policy evaluates against that originating human, not only the last service account, with on-behalf-of attribution preserved across multi-agent flows.
Why Securing MCP Servers is the Wrong ApproachImmediate Value From Live Traffic
Many governance and catalog programs require long inventory projects before they help. Caber starts with live traffic across APIs, MCP, and RAG pipelines, then builds coverage continuously as events update the Context Graph. No prerequisite inventory is required before value begins.
How Caber Prevents Recurring Failures
Observe Live AI Data Flows
Caber observes live traffic across APIs, MCP, and RAG pipelines so prevention starts where data is actually assembled for inference.
Identify Chunks and Link Duplicates Deterministically
Caber deterministically identifies chunks and links duplicates across sources, creating a stable foundation for lineage and policy evaluation.
Update Context Continuously and Evaluate Policy at Time of Use
Caber continuously updates context on meaning and proper use for each chunk, then evaluates policy at time of use for freshness, authorization, conflicts, and relevance before inference.
Preserve User Attribution Across Chains and Enforce Before Inference
Caber preserves on-behalf-of attribution across APIs, MCP, RAG, and agent chains, then records the policy decision for each chunk that was allowed, blocked, redacted, or replaced.
We're building this with early design partners.
If you are building or securing AI systems that use RAG, MCP, APIs, or agent workflows, we would value your perspective.