LogRiteLogRite

Pass the security review that decides the deal

Your buyers now ask what your product does with their data once a model is involved. LogRite governs every AI call your software makes, keeps tenant data where it belongs, and turns log coverage into evidence instead of an assurance.

Control and audit evidence aligned to

SOC 2GDPR

Every AI call

Governed before tenant data leaves

7% → 100%

Log coverage, measured and closed

30–40%

Faster root-cause analysis with Cortex AI

Before merge

Compliance findings caught in CI/CD

What the review actually asks about

Shipping fast was never the hard part. The hard part is answering for the AI inside your product, the tenant boundaries it crosses, and the evidence you can produce on the day someone asks.

The AI you shipped last sprint

Your product now calls a model on a customer's data

The in-app assistant, the support copilot, the summarizer someone added on a Friday. Each one sends customer data to a provider, and none of them are in your threat model yet. AI Warden sits in front of those calls, checks each one against your rules, and keeps the record of what was sent.

Every AI call in your product, visible
Sensitive fields held back before they leave
A record your reviewers can read

Multi-tenancy

One tenant's data surfacing in another's answer

Shared vector stores, shared prompts, shared caches. The failure is quiet, it looks like a good answer, and you find out when a customer does. LogRite governs the call at runtime and records which tenant's data went where.

Tenant context on every AI decision
PII auto-detected before write
Cross-tenant exposure traceable

The security questionnaire

Prove it, do not attest to it

Enterprise buyers stopped accepting a filled-in spreadsheet. They want evidence: what you log, how much of it, and who reviewed the AI touching their data. LogRite measures coverage per service and generates the artifact instead of a claim.

Coverage as a number, not an assurance
Evidence ready before the review
Findings closed before merge

Coverage and Gatekeeper

Coverage that does not decay as you ship

Coverage is not a one-time cleanup. It erodes with every service someone adds on a deadline. LogRite measures it as a number, writes the logging that is missing at the code level, and Gatekeeper blocks the merge when a push would take the number backwards.

Coverage measured, not estimated
Missing logs written before runtime
Regressions stopped at the pull request
logrite — coverage report
measured, not guessed

Log coverage

100%

7% baseline
after Autofix PR
payments/charge.ts100%
auth/session.ts100%
orders/create.ts100%

AI Warden

The model in your product is your responsibility

Your customer signed a contract with you, not with your model provider. When your assistant reads their data to answer a question, you own what happens next. AI Warden sits in front of those calls as a drop-in relay: each request is checked against your rules, scanned for sensitive data, judged, and then allowed, flagged, or stopped. The decision and the payload are kept either way.

Every AI call your product makes, visible
Allow, notify, or block on your terms
Tenant data held back before it reaches a model
The rule that fired, the verdict, and who sent it
logrite · compliance
Audit-ready
Compliance status
Passing

SOC 2

Security controls

100%

GDPR

Data subject rights

100%
PII masked before write Audit fields complete

Evidence, not attestation

Answer the questionnaire with a number

“We log comprehensively” is what every vendor writes, and every reviewer discounts. LogRite measures what share of your code is actually logged, scans it against the frameworks your buyers ask about, and shows the gaps behind the number, so the answer you give is a measurement rather than a claim.

Coverage measured per service, not estimated
The gaps behind the number, itemized
Compliance findings closed before merge

Cortex AI

One customer is affected. Which one, and why

A multi-tenant incident is a scoping problem before it is a fix. Cortex AI reasons across your logs and your AI decisions together and returns the chain: which tenant, which service, which change, and what else it touched.

Ask in plain language, not a query syntax
Blast radius scoped by tenant
Root cause with the evidence attached
Example investigation
Something is wrong with the reporting API for one of our tenants
Checking the reporting service logs across tenants…
Found 214 failed report builds in the last hour. Checking which workspaces are affected…

Here's what I found:

  • 214 failed report builds in the last hour
  • 1 tenant affected, the rest are unaffected
  • 12s query time on their largest workspace (normal 0.4s)
  • the 14:20 deploy introduced the unindexed filter

Built for platforms selling upmarket

Wherever your product handles a customer's data, the same question follows the deal: can you show what happened to it.

Early-stage platforms

Get to your first enterprise deal without a six-week evidence scramble.

Scaling multi-tenant SaaS

Keep coverage from decaying as services and teams multiply.

AI-native products

Show customers how the model touching their data is governed.

Walk into the review with evidence

Govern the AI inside your product, keep tenant data where it belongs, and show coverage as a number. See it on your own systems.