Replay Any AI Decision.
From Any Point in Time.
A regulator asks: "What did your AI system do on March 15?" A litigator demands: "Reconstruct the decision that denied my client's claim." A model validator needs: "Compare last month's outputs to this month's to detect drift." Without replayability, you cannot answer any of these. With Cachee, every AI decision is stored with the complete execution context needed to reconstruct and verify it — indefinitely.
The questions you cannot answer today
Every one of these scenarios requires the ability to go back in time and reconstruct what an AI system did. Traditional infrastructure makes this impossible.
Regulatory Examination
An examiner asks you to demonstrate how your AI system made a specific decision for a specific customer on a specific date. Your logs show a 200 response code and a timestamp. The examiner needs the inputs, the model version, the parameters, the reasoning, and the output. You have none of it.
Litigation Discovery
Opposing counsel requests production of all AI-generated decisions within a date range that affected a class of users. You need to produce not just the decisions, but evidence that they were made by the system as described and have not been altered. Logs are insufficient. You need verifiable computation records.
Model Drift Detection
Your AI system's accuracy has degraded over the last quarter, but you do not know when the drift started or what changed. To diagnose drift, you need to compare outputs across time for identical inputs. Without stored execution context, you cannot run the comparison.
Incident Response
Your AI system made a harmful decision. You need to understand exactly what happened: what data it saw, what model version was running, what parameters were active, and whether the decision was consistent with prior behavior. The context window has long since rotated. The evidence is gone.
What Cachee stores for every AI operation
Cachee does not log events. It stores the complete computation context needed to verify and replay a decision. Every field is bound to the hash chain.
Input Data Hash
SHA3-256 hash of the exact input data. Proves what the model received, without storing raw PII if configured for privacy.
Model Version
Model identifier, version string, and checkpoint hash. Proves which model produced the output, even if newer versions have since been deployed.
Hyperparameters
Temperature, top-p, max tokens, system prompt hash, and all configuration that influenced output generation. The complete parameter surface.
Output
The complete generated output, or its content hash for privacy-sensitive deployments. Proves exactly what the model produced.
Timestamp
High-resolution timestamp of when the computation occurred. Bound to the hash chain so it cannot be backdated.
Authority Chain
The identity of the requesting agent, user, or system. Who or what triggered this computation, and under what authorization.
How replay actually works
Replay is not re-running the model. It is retrieving the exact cached computation and proving it has not been altered. Here is the sequence.
Query by any dimension
Identify the computation you need to replay. Search by timestamp, agent identity, model version, input pattern, output classification, or any combination. Cachee returns the matching cached entry with its full execution context.
Verify hash chain integrity
Cachee verifies the hash chain from the target entry back to its anchor point. Every intermediate hash is checked. If any entry in the chain has been modified, verification fails and the specific tampered entry is identified. This happens in sub-microsecond time.
Prove output matches context
The stored output is verified against the content hash that was computed at inference time. This proves the output is exactly what the model produced — not a modified version. The hash binds input, parameters, and output into a single verifiable record.
Export or present
The verified computation record can be exported as structured JSON for compliance teams, presented in a dashboard for model validators, or provided to external auditors. The export includes the hash chain so recipients can independently verify without Cachee access.
Logging records that it happened.
Cachee stores enough to verify it.
This is not a better logging system. It is a fundamentally different approach to AI auditability.
- Records that an inference occurred (timestamp, status code, latency)
- Does not capture input data, model version, or parameters
- Log entries can be edited or deleted without detection
- No cryptographic binding between entries
- Cannot reconstruct the decision from log data alone
- Verification requires trusting the logging system
- Rotation and retention policies cause data loss
- No proof that logs have not been tampered with
- Stores complete execution context: input, model, params, output
- Every field bound to SHA3-256 content hash
- Hash chain makes any modification mathematically detectable
- Each entry cryptographically linked to its predecessor
- Full context available to reconstruct and verify any decision
- Verification uses standard hash functions — no trust required
- Retention is explicit and hash-chain-protected
- Optional H33-74 post-quantum attestation for quantum resilience
Who needs replayable AI?
Any organization where AI decisions have consequences that outlast the context window.
Prove what your AI did
When an examiner requests evidence of AI decision-making, produce verifiable computation records that prove what inputs were received, what model processed them, and what output was generated. The hash chain proves the records have not been altered since the decision was made.
Produce defensible evidence
Generate exports of AI decisions that meet evidentiary standards: complete, verifiable, tamper-evident, and independently authenticable. Each record includes enough context for opposing counsel's expert to verify independently without your systems.
Detect drift before it matters
Compare AI outputs across time periods for identical input patterns. Identify when model behavior changed, quantify the magnitude of drift, and correlate with model updates, data changes, or configuration modifications. All from the stored execution context.
Reconstruct what went wrong
When an AI system produces a harmful output, reconstruct the complete decision path: what data it received, what model version was active, what parameters were set, and whether the output was consistent with the model's behavior on similar inputs. Answer "why" in hours, not weeks.
Make Every AI Decision Replayable
Deploy verifiable execution context storage for your AI systems. Every decision captured, hash-chained, and independently verifiable. No infrastructure changes required.