Customers
Securing AI retrieval in production
Engineering teams use Gateco to enforce access control, prove compliance, and make AI agents safe for sensitive data. Here is what production deployments look like.
What teams deploy Gateco for
Internal AI copilots
Teams building internal copilots for HR, legal, finance, or operations use Gateco to ensure that employees only retrieve content they are authorized to see, enforced at retrieval time, not prompt time. A legal team member asking an AI assistant about employment contracts should never receive confidential documents intended for executives.
Policy enforcement happens at the exact moment content would leave the retrieval layer. The LLM context is built only from chunks the requesting principal is allowed to see. No document-level filters, no prompt-level redaction: the unauthorized content never enters the pipeline.
Typical results
- First secured retrieval: under 2 hours
- L3 governance (resource-level policy): 10 to 14 days
- Policy coverage across 50,000+ vectors
Product-derived typical deployment timeline, not a specific customer result.
Enterprise search with compliance requirements
Organizations in regulated industries, including financial services, healthcare, and government contractors, use Gateco to satisfy the auditability and access control requirements that regulators and security teams impose on AI systems. Every retrieval decision is logged. Every denial is explained.
The audit trail captures principal ID, resource ID, policy ID, decision, search mode, and timestamp for every event. 50+ event types are recorded. Exports in CSV and JSON are available on Pro and Enterprise plans. The log structure is designed to satisfy SOC 2, HIPAA, and EU AI Act documentation requirements without post-processing.
Typical results
- 50+ audit event types per retrieval decision
- Immutable log with configurable retention
- CSV and JSON export for compliance evidence packages
Product capabilities, not extrapolated customer figures.
Multi-tenant SaaS with customer data
SaaS companies embedding AI search into their product use Gateco to enforce tenant isolation at the retrieval layer. Each customer's AI agent sees only their own data, guaranteed by Gateco's policy engine, not by application-level logic that can be misconfigured.
Per-principal policy evaluation means tenant isolation is a policy constraint, not an application architecture requirement. A misconfigured query does not become a data leak. Cryptographic tenant binding in the KMS layer ensures that even at the data layer, cross-tenant decryption fails loudly.
Typical results
- Per-principal policy evaluation on every retrieval
- Cryptographic tenant isolation via envelope encryption
- Fail-closed by default: isolation failures deny, not expose
Product-level capabilities as shipped.
Case studies
Detailed production deployments, in progress.
We are working with our early design partner cohort to document production deployments. Case studies are in progress, anonymized and attributed entirely on partner terms.
Partners are in financial services, healthcare, and enterprise SaaS. If you are evaluating Gateco for a regulated workload, we can connect you with a reference customer under NDA.
What a typical production deployment looks like
Product characteristics from the live system, not projected or estimated figures.
Median time to L3 governance
From first connector connected to resource-level policies active.
Audit event types
Captured per retrieval decision, every time.
Vector database connectors
Across Tier 1 (ingestion) and Tier 2 (search) connector classes.
Policy-layer overhead
p95 latency cost added by the Gateco policy layer per retrieval.
Semantic readiness levels
Progressive governance posture from connector connected to chunk-level policy.
Relationship cache
For ReBAC policies. Invalidated automatically after any relationship mutation.
All figures are product characteristics from the shipped system. Latency overhead is measured at the policy layer only; end-to-end retrieval latency varies by connector and search mode.
Ready to gate your AI retrieval?
We scope implementations with teams before they sign anything. A 30-minute call typically gets to connector, policy model, and first retrieval plan.