AI workflows lose continuity
Agents and copilots drop prior decisions, customer context, handoffs, and policy nuance unless teams keep stuffing larger prompts into every request.
Enterprise AI memory control plane
Enterprise AI memory that stays under your control.
RecordorAI gives enterprise AI systems a governed memory layer that can run locally, retrieve relevant context without an LLM in the retrieval pipeline, and let customers decide where their data goes.
Local-first memoryNo LLM required for retrievalModel-provider neutralEnterprise governance
Incoming AI requestWhat changed since the Q2 renewal call?
Tenant scopedPolicy gateAccess approved
Local retrievalNo LLM call
Context package3 memories returned
Memory matchRenewal risk moved to security review
Memory matchCustomer requested EU-only routing
Audit trailSource, timestamp, and policy visible
Why it matters
Enterprise AI gets better when memory is treated like infrastructure.
RecordorAI is built for teams that need AI systems to remember, but cannot give up control over security, data residency, model choice, or auditability.
Context costs compound
Enterprise AI programs often pay to resend the same background repeatedly. RecordorAI retrieves only the relevant memory before the model call.
Model-side memory creates risk
When memory lives inside a vendor model, security and compliance teams have less control over storage, retention, deletion, and routing.
Teams need evidence trails
RecordorAI returns traceable memory context so an AI answer can be reviewed against the source material that informed it.
Security and data sovereignty
Keep memory local. Send only the context you approve.
RecordorAI can run as a local memory layer for enterprise AI systems. Because retrieval does not require an LLM call, customers remain in control of which memory records stay inside their environment and which approved snippets are routed to a chosen model.
Request security briefCustomer-controlled data planeMemory can live in the customer environment instead of a model vendor memory service.
No LLM required for retrievalRetrieval can happen before model routing, reducing unnecessary data exposure.
Portable model strategyUse public APIs, private inference, local inference, or a mix as requirements change.
Governance-ready memorySupport retention, deletion, export, tenant controls, and audit review.
Customer sources
Conversations, tickets, documents, workflow events, meeting notes, and internal knowledge systems.
RecordorAI local memory
A customer-controlled memory layer that can run locally, in a VPC, or in an on-prem deployment path.
Policy and retrieval gate
Tenant, role, retention, deletion, and routing rules decide which memories can be retrieved.
Approved context
Only the selected snippets and evidence package move forward to the customer-chosen AI system.
Any model strategy
Public APIs, private inference, local inference, or hybrid routing as enterprise requirements change.
Platform
From memory capture to governed context delivery.
RecordorAI sits between enterprise knowledge sources and AI systems, giving teams a repeatable way to retrieve the right memory before prompts are sent.
Capture enterprise context
Conversations, tickets, documents, support notes, and workflow events become governed memory records inside the customer-controlled environment.
Retrieve without an LLM call
RecordorAI uses a retrieval pipeline that does not require sending raw memory to a model just to decide what context matters.
Route only approved context
Policies, tenants, retention rules, and access controls determine what memory can be returned and which AI system may receive it.
Measure the business result
Pilots compare answer quality, retrieval accuracy, token usage, latency, and governance evidence against the current AI workflow.
Buyer lanes
Built for the teams that have to make enterprise AI work.
AI platform teams
Add durable memory beneath agents, copilots, and orchestration layers without locking the business into one model provider.
Security and compliance
Review where memory lives, what leaves the environment, which policies were applied, and how deletion or export is handled.
Support and customer operations
Carry account history, preferences, prior resolutions, and escalation context into every AI-assisted interaction.
Enterprise use cases
Memory for the AI systems companies are already deploying.
Customer support copilots
Recall account history, prior resolutions, preferences, and escalation context without overloading every support prompt.
Internal knowledge agents
Give teams durable memory across meetings, decisions, policies, and projects while preserving access boundaries.
Engineering and operations AI
Carry incident history, runbooks, architecture notes, and postmortems into the next troubleshooting session.
Regulated enterprise workflows
Support environments where data residency, deletion, auditability, and vendor routing matter as much as answer quality.
Proof
Benchmark-backed retrieval before enterprise pilots.
Current gates show strong retrieval performance on long-memory benchmark tasks. These are retrieval-only measurements, not final answer QA scores, and are used to guide pilot design.
Retrieval-only benchmark gate
Latest internal gate
Evidence-session hit@1
How to read the numbers
The benchmark layer is used to verify memory retrieval before a customer pilot. Final pilot scorecards should also include task quality, latency, token usage, and governance evidence.
- Latest internal benchmark refresh: June 4, 2026
- Measures retrieval quality before the final model answer
- Used to design customer pilot baselines, not presented as final QA scoring
- Benchmark packet available for security, platform, and diligence teams
Integrations and deployment
Built for the way enterprise AI stacks actually evolve.
RecordorAI is designed to be infrastructure beneath agents, copilots, and AI workflows. The goal is to let customers change models, deployment patterns, and security requirements without losing the memory layer.
REST and hosted HTTP deployment pathLocal protocol and MCP-style integration pathDocker, VPC, and on-prem deployment planningCustomer-controlled memory storePortable across OpenAI, Anthropic, Google, local, and private inferenceRetention, export, deletion, and audit workflows
Enterprise pilot
Prove the ROI in one workflow before expanding.
The recommended starting point is a focused pilot where RecordorAI is measured against an existing AI workflow on memory accuracy, token usage, latency, security review, and operational fit.
- Baseline one high-value AI workflow
- Connect RecordorAI to the customer-controlled memory source
- Run side-by-side retrieval, token, latency, and quality measurements
- Review governance evidence with security and compliance stakeholders
Timeline
6-8 week pilot with a defined workflow, owner, and security review path.
Success metrics
Retrieval accuracy, answer quality, context-token reduction, latency, and policy evidence.
Customer inputs
A representative AI workflow, approved memory sources, and deployment constraints.
RecordorAI outputs
Pilot report, benchmark comparison, architecture notes, and expansion recommendation.
Pilot request
Start with the workflow where memory matters most.
Send the basics and the RecordorAI team will follow up with a pilot scoping note, security review path, and recommended measurement plan.
Prefer direct email?enterprise@recordor.ai