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.
ticket · ACME-347
Implement rate limiting for /checkout.
agent plan
Per-IP token bucket, Redis store, inline config.
rework Developer corrects per-customer vs. per-IP.
rework Agent regenerates against the old Redis assumption.
rework Developer points it back to team conventions.
ticket · ACME-347
Implement rate limiting for /checkout.
agent plan
Per-customer sliding window, Valkey store, config externalized.
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.
pull request
feat: add rate limiting to /checkout
feature/acme-347 → main · 4 files
Extract magic number to a named constant.
Use the ms() helper for the rate limit window.
Add JSDoc to the config object.
pull request
feat: add rate limiting to /checkout
feature/acme-347 → main · 4 files
Per-customer sliding window matches the Mar 17 decision.
via Kinelo · meeting awareness
Config externalized to security.yaml, matching team convention.
via Kinelo · team standards
ACME-349 handles response headers. No overlap detected.
via Kinelo · cross-ticket awareness
Add fallback for unauthenticated requests where customerId is undefined.
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
The work moves faster when it carries what the next actor needs.
The task starts with context attached.
The next actor does not reconstruct the history.
The result hands forward what changed.
Context
Relevant decisions, conventions, and customer signals travel with the work.
Direction
Kinelo keeps the next step clear instead of leaving a bare ticket behind.
Memory
What each actor learns becomes available to the next human or AI.
The architecture
Integrations feed Kinelo. Kinelo feeds the agents doing the work.
Team tools
Channels, tickets, PRs, meetings, specs, and commitments.
Decisions
What was decided, who decided it, and what changes next.
Conventions
The working rules and standards that never made it into docs.
Projects
Status, dependencies, owners, and open questions in flight.
Customers
Promises, risks, priorities, and signals from customer work.
People
Who owns what, who knows what, and when judgment is needed.
History
What happened last time, what failed, and what the team learned.
Company context
Kinelo turns scattered signals into usable memory for agents doing the work.
Coding agents
Build with project intent.
Review agents
Review against decisions and standards.
Team in Slack
Gets reports, status, and surfaced concerns.
General AI tools
Draft and analyze with team context.
Close the gap between the promise of AI-powered development and reality.
Kinelo brings the context, connections, and oversight needed to make AI development sane, high quality, and cost effective.