Kinelo increases quality and decreases cost of AI software development.

With Kinelo, code reviews are faster and more effective, autonomous agents produce code that requires less rework, and your human team can keep up with the frenetic pace.

Kinelo uplevels agents from “naive junior dev” to “senior engineer.”

Kinelo gives coding agents guidance, oversight, and tools they need to produce higher-quality code that solves more of the problem the first time, with less rework, less human guidance, and less review churn.

Coding agent output

Same ticket. Different context.

Without Kinelocontext missing

ticket · ACME-347

Implement rate limiting for /checkout.

agent plan

Per-IP token bucket, Redis store, inline config.

generated code

✗

Developer: “No, per-customer, not per-IP.”

↺

Agent regenerates… still using Redis instead of Valkey.

✗

Developer: “We migrated to Valkey. And externalize the config.”

↺

Agent regenerates… config is inline, wrong format.

✗

Developer: “No, security.yaml, like the other middleware.”

· · ·

Developer scraps the session and starts over — or just writes it themselves.

↺

Correct-by-correction. The developer becomes the context layer.

With Kinelocontext attached

ticket · ACME-347

Implement rate limiting for /checkout.

Kinelo context packet

standupUse per-customer sliding windows, not per-IP token buckets.

historyRedis → Valkey migration is in progress. Use ValkeyStore.

standardSecurity config belongs in /config/security.yaml.

scopeACME-349 handles response headers. Stay clear.

agent plan

Per-customer sliding window, Valkey store, config externalized.

generated code

PR opened · ready for review

→

Right on the first try. No corrections needed.

Kinelo empowers reviewers with the context to focus on the issues that matter.

Teams are overwhelmed by the sheer volume of pull requests to review. Kinelo makes reviews more effective by carrying context into the review, instead of leaving people stuck on pedantic code quality issues.

Review agent output

Same diff. Different depth.

Without Kinelocontext missing

pull request

feat: add rate limiting to /checkout

feature/acme-347 → main · 4 files

code diff

nit

Extract magic number to a named constant.

style

Use the ms() helper for the rate limit window.

nit

Add JSDoc to the config object.

2 hours later

✗ Human reviewer

“Wrong approach — we said per-customer, not per-IP. Wrong algorithm, wrong data store. Needs rework.”

↺

Bot missed everything that mattered. Pedantic nits, no substance.

With Kinelocontext attached

pull request

feat: add rate limiting to /checkout

feature/acme-347 → main · 4 files

code diff

intent

Per-customer sliding window matches the Mar 17 decision.

via Kinelo · meeting awareness

standard

Config externalized to security.yaml, matching team convention.

via Kinelo · team standards

scope

ACME-349 handles response headers. No overlap detected.

via Kinelo · cross-ticket awareness

edge

Add fallback for unauthenticated requests where customerId is undefined.

→

Substantive review. Merged. No rework.

Kinelo carries the reasoning from start to shipped.

With the pace of AI development, engineers, designers, and PMs struggle to understand the decisions embedded in code, prototypes, or docs handed between them. Kinelo carries that reasoning forward and makes it available to the humans and AI next in line.

Work packet