Customer service
Tickets resolved.
83% → 96% vs the agent alone.
Tide-EnterpriseExplore the use caseTide-1 API
Rippletide checks agent actions against your company’s policies and shows you why a decision was made.
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Coming soon Tide-1 · for builders · Tide it
Decision models / Performance
Customer service
83% → 96% vs the agent alone.
Tide-EnterpriseExplore the use caseIAM approvals
Over 9K replayed decisions.
Tide-EnterpriseSee the evidenceCode diff review
Happy path passes. Isolation test fails.
Tide-1Run the sample testsCredit
100+ cases.
Tide-EnterpriseExplore the use caseCustomers & design partners
01 / Tide-1 playground
Customer service agent
A shopper returns an item after 37 days. Your agent serves two stores with different return policies.
30-day policy37 days is outside this customer's 30-day window.
"action": "decline_return"45-day policy37 days is within this customer's 45-day window.
"action": "approve_return"Code review agent
A patch passes the happy path but exposes another customer's data. Correct the decision, then test the same mistake in a new file.
Only the happy path and unknown-record check inform the decision.
Make account isolation part of the next decision.
“Reject any change that can return another customer's data.”
These interactive examples run the JavaScript shown here in your browser. The feedback example applies a structured correction to the next sample decision-policy version. It demonstrates the workflow; it does not call a hosted Tide-1 model.
Customer support agent
The blender arrived cracked. What should the agent do?
Send a replacement
12 facts. Each question recalls only the ones that match it.
Every active Policy applies to every decision in this bank.
Customer support agent
Opened kettle, returned 41 days after delivery. What should the agent do?
Store credit
Was Decline, before your correction.
13 facts. Each question recalls only the ones that match it.
Every active Policy applies to every decision in this bank.
Illustration: placeholder facts and scores, not a live Tide-1 call.
Request early access02 / Use cases
Customer service agent
Use the right store policy, order facts, and customer exceptions to choose the next step.
Input: ticket + customer policy + current facts Choices: replace / refund / request information Output: the next decision for your support agent
Incident investigation agent
Evaluate competing root-cause hypotheses against incident evidence, so the agent can pursue the cause that best explains what happened.
Input: incident evidence + candidate causes Choices: recent change / dependency / configuration Output: the best-supported cause to investigate
Up to +100% Top-1 RCA accuracy
Relative improvement vs. compared solutions
Coding agent / Code diff review
A lookup still returns the expected order, but now exposes other customers’ orders too. Find the missing account check. Run the tests, apply the fix, and test again.
- orders.find(order => order.id === orderId && order.accountId === accountId) + orders.find(order => order.id === orderId)
1 critical access check
2/3 → 3/3 sample checks Tide-1
Financing agent
Use application facts, documents, and business criteria to determine what can proceed and what needs a missing document or further review.
Input: application + supporting documents + criteria Choices: proceed / request document / refer for review Output: the next step for the financing agent
98.9% correct credit assignment
100+ cases Automotive group
Portable decisions
Keep your policies, corrections, and learned specialization when you switch agents. Your GPT-based agent, Claude-based agent, or next workflow calls the same decision model. No re-teaching the business rules.
03 / Tide-Enterprise
Add a document, played through. Device return policy is dropped in. Rippletide reads its 3 clauses one at a time. §1 Return window: A faulty device can be refunded within 30 days of delivery, dated by the carrier record. Becomes a Draft Rule. §2 After the window: After 30 days, a faulty device is replaced under warranty instead of refunded. No Rule: an instruction for the agent. §3 Proof of delivery: With no delivery date on the order, ask the customer for it before any refund. No Rule: an instruction for the agent. Then the document connects to the Map: Source, Device return policy, new; Rule, Refund a device only within 30 days of delivery, a Draft, new; Action, refund_customer, already there, now linked. Rules from a document start as Drafts.
02 / Source-quality checks
The Resolve step of Add a document, played through. Rippletide checks Device return policy against the 4 Sources and Systems it touches: CRM · Accounts, System · 2,140 accounts; ERP · Customers, System · 2,163 customers; Help center · Returns, Source v4 · Zendesk; Order API, System · 18,420 orders. Three flags appear one by one. Inconsistent: One customer, two records, CRM ACME-FR and ERP ACMEFR. Linked automatically. Same registration ID: 552 100 554. Conflicting: Two refund windows for a faulty device. The approved policy and the help center disagree: 30 days, Approved policy, §1, or 14 days, Help center article. 30 days is picked; the Draft Rule uses it: Refund a device only within 30 days of delivery. Saved. Rippletide reuses it when they disagree again. Missing: No delivery date on orders. The Order API returns no delivery date. Until then, the Rule asks the customer for the date. Hypergraph built: 3 flags handled, one Draft Rule.
03 / Runtime enforcement
Enforce each Rule when you're ready. Every decision traces back to its Rule and Source.
The Map, one question played through. The question is typed into the ask box: A phone delivered 41 days ago is faulty, and the customer wants a refund. Refund or replace? Then Ask. The chain lights in order: Source, Device return policy; Rule, Refund a device only within 30 days of delivery, in Observe; Action, refund_customer. The answer: Recommended: send_replacement. Delivered 41 days ago, past the 30-day refund window. In Observe, Rippletide records this and lets refund_customer run. Then the Rule moves from Observe to Enforce: Blocked: refund_customer. In Enforce, Rippletide stops refund_customer before it runs.
04 / Enterprise evidence
Consumer goods / Customer service
Resolution: 83% → 96% vs the agent alone.
Benchmark on new cases.
Automotive / Credit
100+ cases.
Credit policies turned into executable rules.
Enterprise software / IAM approvals
2% escalated, 2% incorrect. Over 9K decisions replayed.
383 business rules made explicit.
Each result comes from a separate customer evaluation.
For the agent you are building
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