Connect your data
Bring documents, policies, business records and decision history into the hypergraph.
Add a decision call to your existing agent. Keep rules and corrections in one decision system, across calls and agents.
Decision API / Quickstart
Use a Connection key from Decision API in the platform. Keep it in your backend environment.
export RIPPLETIDE_API_KEY='your-connection-key'Understand the result
The response keeps the chosen action, scores and supporting context together.
choiceThe selected option from the choices you supplied.
choice_scoresThe model’s score for each candidate action.
evidenceThe memory excerpts and policy versions supplied for this decision.
decision_idThe identifier to use when sending a correction.
Specialize the decisions
Save an active policy in the same decision bank. Subsequent calls can use that policy without adding its full text to every request.
/v1/policies/:policyIdExample policy
For a package marked delivered but not received, request carrier delivery evidence before recommending a replacement or refund.Download executable policy example
Feedback
Send the original decision ID, the correction and its source. The Decision API stores that correction in the same bank for later retrieval.
/v1/feedbackPortable decisions
Connect your next backend agent to the same bank through the API. Its policies, decision history and retained corrections stay available when the calling agent or its LLM changes.
Tide-1 Coming soon
Tide-1 will train specialized decision models from your rules and corrections. Keep the model when you change agents or LLMs.
Tide-Enterprise
Build a shared hypergraph from the information your agents need. Follow the same three stages as the platform.
Bring documents, policies, business records and decision history into the hypergraph.
Compare CRM and ERP identifiers, an approved policy with a conflicting help-center page, and missing fields in operational APIs. Reconcile the source data into a common decision reference.
Check each proposed action against validated rules before its tool executes. Enforce the decision at runtime with deterministic execution, explanations and traceability.
Runtime reinforcement brings outcomes and reviewed corrections back into the reference, with replay before release.