Built around human review, not just automation
Every AI app that ships through Kavach passes a manual code review and a security review before it reaches your users. Automated gates catch regressions. Human reviewers catch the things no test was written for.
Manual Code Review
A trained reviewer reads every diff before it ships. Not a linter. Not a bot. A human.
Why manual review matters
Automated tests catch regressions they were written to catch. A human reviewer catches the things no test was written for — the logic error in a new prompt, the endpoint that returns slightly more data than it should, the dependency update that changes how inputs are sanitised. These are the failures that cause real incidents. Manual review is the control that prevents them.
Diff-level Review on Every Deploy
Before any build is eligible for production, a Kavach reviewer reads the exact changes in that deployment — line by line. Automated tests tell you the code runs. A reviewer tells you the code is right.
Model Interaction Logic Scrutiny
Prompt changes, model version updates, and context-window modifications are reviewed for correctness and unintended behaviour before they reach users. A new model version is never a silent drop-in.
Structured Review Checklist
Every review follows a consistent checklist: data handling, error paths, dependency changes, and logic correctness. Nothing is left to reviewer memory or intuition alone.
Full Review Audit Trail
Every review is logged — who reviewed, what was flagged, what was approved, and when. You have a complete record of every human decision made before each deployment.
Security Review
Every deployment that touches auth, data access, or secrets gets a dedicated security pass.
Security review is mandatory — not optional
Any change that touches authentication, secrets handling, data access, or model interaction code triggers a dedicated security review. The build cannot proceed to production until the review is cleared. This is not a checkbox — it is a hard gate. Your users' data is protected before the code ships, not after an incident forces you to act.
Pre-deploy Security Gate
Changes to authentication flows, API endpoints, secrets handling, and data access patterns trigger a mandatory security review. The build cannot proceed to production until the review is cleared.
Secrets & Credential Audit
Reviewers check that no secrets are hardcoded, logged, or returned in API responses. New secrets introduced in a deployment are verified to be encrypted at rest and injected at runtime — never baked into the image.
Prompt Injection & Input Validation
AI-specific attack surfaces — prompt injection, jailbreak vectors, and unvalidated user inputs passed to model context — are reviewed on every change that touches model interaction code.
Dependency & Supply Chain Check
New or updated dependencies are reviewed for known CVEs, suspicious transitive dependencies, and unexpected network or filesystem access. A package update is not automatically safe.
Deployment Controls
After review clears, ship with confidence. Roll back in seconds if anything goes wrong.
Review-gated Deployments
No build reaches production without clearing both the automated health checks and the human review gate. The two work together — neither replaces the other.
Instant Rollbacks
Every deployment is snapshotted. Roll back to any previous reviewed version in under 10 seconds — no rebuild, no downtime, no re-review required.
Canary Releases
Route a percentage of traffic to a new reviewed version before full rollout. Catch runtime regressions on 5% of users, not 100%.
Pre-deploy Health Checks
Kavach runs automated smoke tests against your new build before cutting traffic over. Bad builds never reach production — reviewed or not.
Real-time Monitoring
Know the moment something goes wrong in production — before your users do.
Live Health Dashboard
Per-site uptime, response times, error rates, and memory usage — all in one glance. Refreshes every 30 seconds.
AI Model Latency Tracking
Track inference latency for every LLM call your app makes. Spot regressions the moment a new model version ships.
Request Volume & Throughput
Visualise traffic spikes, sustained load, and per-endpoint request counts across any time window.
Cost Per Request Tracking
Track AI API spend per request in real time. A 30% cost increase after a model update shows up immediately — not at the end of the month.
Rollback & Recovery
When things break — and they will — recovery is instant.
Full Deployment History
Every build, config change, and environment variable update is logged with timestamp, author, reviewer, and diff.
Automated Rollback Triggers
Define rollback policies: if error rate exceeds X% within Y minutes of a deploy, Kavach rolls back automatically — no human in the loop required.
State Snapshot Restore
Restore database snapshots tied to a specific deployment — so your data and code are always in sync after a rollback.
Post-rollback Validation
After a rollback, Kavach re-runs health checks and confirms the previous version is stable before closing the incident.
Logs & Observability
Full visibility into what your app is doing — always.
Structured Log Streaming
Stream application logs in real time with structured JSON parsing. Filter by level, service, or custom fields.
Full-text Log Search
Search across all logs with sub-second latency. Find the exact request that caused an error in seconds, not minutes.
Distributed Trace Correlation
Correlate logs, metrics, and traces across microservices with a single trace ID — follow a request end to end.
Log Retention & Export
Retain logs for 30, 90, or 365 days. Export to S3, BigQuery, or your own SIEM for compliance and long-term analysis.
Ship with a reviewer in your corner
Join the waitlist and get early access when Kavach launches on 1st January 2027.