The memory that makes every AI agent better.
One governed context engine gives agents the knowledge to finish the work, remembers what your experts taught them, and compounds that learning with every use.
One source of context. The right knowledge at every step.
An agent cannot finish a task if it has to guess which policy applies or where the latest case evidence lives. The Context Engine centralizes that knowledge, provides what each run needs, and verifies decisions against it.
Your context today
Fragmented- Regulations & jurisdictional rulesRules
- Underwriting, claims & servicing guidelinesRules
- SOPs, playbooks & desk manualsProcedure
- Business logic, pricing & rating rulesLogic
- Industry language & product glossariesLanguage
- Systems of record — policy, claims, core bankingLive state
- Documents, inboxes & vendor portalsEvidence
- Prior decisions & reviewer correctionsMemory
Unified Context Engine
Centralizes
01One governed context graph per enterprise — rules, language, logic, agent operating procedures, and the live state of every case. Versioned, permissioned, residency-pinned.
Provides
02Assembles exactly the context each step needs — scoped by function, line of business, jurisdiction, and role — before the agent reasons or acts.
Verifies
03Checks every output against the context it came from — citations resolved, rules satisfied, figures reconciled — before anything is staged for approval or written back.
One agent, one task
Autonomous · end to end- 01Intake & classifyAgent
Reads the case, the documents, and the request.
- 02Retrieve contextAgent
Pulls the rules, procedures, and live state that apply.
- 03Reason & decideAgent
Weighs the evidence against guidelines and appetite.
- 04Act in systems of recordAgent
Reads, reconciles, and stages the writes.
- 05Verify against contextAgent
Every citation, rule, and figure checked.
- 06Stage for approvalHuman approves
Decision-ready packet with rationale attached.
- 07Write back & logAgent
Systems updated, full audit trail recorded.
Your operation remembers. Your agents learn from it.
Working memory carries a case through a run. Institutional memory carries your enterprise's expertise from one run to the next — governed, scoped, and grounded in actual work.
Rules and expertise
Regulations, guidelines, procedures, product language, and the decisions your experts make become usable context, not another document an agent has to find.
Live, scoped evidence
Current records and case history are assembled for the particular task, line of business, jurisdiction, and role. Each agent sees the context it is allowed to use.
Institutional memory
Approved decisions, reviewer corrections, exceptions, and resolved outcomes retain the reasoning behind the work so the next run does not start from zero.
Every correction becomes a chance to improve the next run.
The feedback loop connects expert judgment to context, evaluations, and model improvement. Better context can help immediately; model adaptation follows only after curated data and evaluation show it is worth doing.
Capture the work
Keep the evidence, decision, agent action, human correction, and eventual outcome together as a traceable signal.
Improve the context
Validated corrections can refine the rules, examples, and retrieval context supplied to future runs without retraining a model.
Evaluate the change
Test candidate improvements against representative cases, quality standards, and guardrails before promoting them.
Compound into training
Curated, permissioned decisions become evaluation sets and, where appropriate, data for fine-tuning or other post-training of specialized models.
Go deeper on how agents specialize in our continuous learning approach.
Give every agent a better starting point.
Bring your enterprise knowledge, decisions, and outcomes into one governed context, then let every workflow build on what your team already knows.