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Catch breaking data changes before production does
Your rules,verified in production.
RulePivot gives engineers real-time confidence that the data flowing through their systems still follows the rules they depend on — starting with Runtime Verifications in your own environment.
fig. 01 — runtime verification
const rule = await client.attach('billing_contract')
const outcome = await rule.evaluate(res)
// → result reported automatically
subscription:
×planCode: null required string
✓status: <string> valid
+billingCycle: <string> new field
// values stay in your environment
Required field
planCode is null — expected string.
view evidence → Team notified · severity high
01 / Problem → Solution
Data systems break when their assumptions drift silently.
−
A service changes an API response.
−
A field disappears.
−
A field is renamed.
−
An enum changes.
−
A release passes ordinary tests — but violates a data contract.
Run your rules where the data moves.
Verify rules close to where data actually moves — backend services, API responses, and production data paths.
- 01 Create a rule — by hand or from your codebase
Define what must be true about the data you depend on. The RulePivot CLI can scan your codebase and propose rules — you review and approve.
- 02 Run it in your environment
Add Runtime Verifications to your backend service and evaluate the data flowing through it with the RulePivot SDK.
- 03 Results report automatically
Verification results report back to RulePivot automatically — no private data is sent by default.
02 / Demo — 200 OK, broken contract
A 200 OK response does not mean your data still follows the rules.
Your API can be healthy while your contract is broken. RulePivot verifies the shape, values, and rules your systems actually depend on — the contract behind the response.
what your monitoring saw
GET /v1/customers/cus_123/access-profile
200 OK healthy — nothing to report
The schema still matched and every test fixture passed — the break was a live value. The
added field — billingCycle — appears as evidence, not an automatic failure.
what RulePivot saw
subscription:
×planCode: null required string
✓status: <string> valid
+billingCycle: <string> new field
// values stay in your environment
Required field
planCode is null — expected string.
view evidence → Team notified · severity high
every result identifies
- rule
- billing_contract
- version
- v1.0.0
- integration
- billing-api
- environment
- production
- execution mode
- runtime
- status
- failed
- affected path
- subscription.planCode
03 / Why now
Agents need verification, not assumptions.
As AI agents and automations take more action on structured data, teams need proof that the data still matches the rules those workflows depend on.
Machine-readable results
Agents and automations get stable statuses and structured evidence they can act on — not logs to parse.
One source of truth
Humans, CI systems, and automation verify against the same rule and see the same result.
Drift surfaced early
Silent drift is surfaced before an automation acts on it and makes the wrong call.
04 / Capabilities
Create rules. Verify contracts. Show results.
$rulepivot scan— map your repo's data boundaries, then approve the rules it proposes
Runtime Verifications
Run rules where the data moves.
Verify the data flowing through your services with the SDK — results report back automatically.
Managed Verifications
Send a payload. Get a result back.
POST a JSON payload to RulePivot and receive a direct verification result.
Pre-deploy Verifications
Stop contract-breaking changes before deploy.
GitHub-first PR checks and CI gates that return a deterministic pass/fail before code ships.
Shared Rules
Share rules without losing control.
Publish protected rules that approved partners subscribe to and verify against.
Verify the contractsyour systems depend on.
RulePivot is in private beta. Leave your email and we'll get in touch when your early access is ready.
Request early accessPrivate beta — pricing is simple. Talk to us.