Scale AI-assisted development without losing control of engineering quality.
AI coding tools help teams produce code faster. The harder part is keeping standards, validation, ownership, metrics and rollout consistent as usage spreads across teams.
Kodus works hands-on with your engineering team for 4 weeks to find where quality breaks down, improve one or two concrete bottlenecks, and leave you with a model you can reuse across more repos.
Founder-led, one company per segment. From the team behind open source AI code review without vendor lock-in.
Engineering teams that trust Kodus
Your engineers got faster. Your delivery did not.
You rolled out AI coding agents. Individual output jumped and pull requests multiplied.
Cycle time stayed flat. Review, testing and standards turned into the real constraint.
The gains compounded on the engineer, not the organization. Getting past that plateau is the real climb.
AI adoption puts pressure on the whole engineering workflow.
Most AI coding adoption starts with individual developers. Someone uses Cursor. Someone else uses Copilot. A team experiments with Claude Code, Codex, Devin or an internal agent. The early win is obvious: code gets produced faster. The operational problems show up after that:
Buying another tool does not fix the workflow by itself. The team needs to understand where quality breaks down in practice, then turn that into a repeatable way of working.
- Standards become harder to enforce across repos.
- AI-generated code has unclear ownership.
- CI and tests carry more risk than before.
- Review expectations stay tribal.
- Automation creates noise developers stop trusting.
- Leadership cannot tell whether AI adoption is improving quality.
- Rollout depends on one internal champion.
A 4-week sprint to find and fix the quality bottlenecks created by AI adoption.
We work with engineering leadership, Platform or DevEx owners, repo owners and developers to inspect the real workflow: code changes, standards, review, CI, tests, ownership, metrics and AI usage.
Then we choose one or two bottlenecks to improve during the sprint. You leave with a diagnosis, implemented changes in the selected scope and a rollout model for the next teams or repos.
We inspect the engineering quality system AI now depends on.
AI adoption touches standards, ownership, validation, metrics, and rollout. The sprint shows where those pieces are clear, where they are implicit, and what needs to change before AI scales across the team.
AI-assisted development
How engineers use AI coding tools today, where usage is official or informal, and which kinds of AI-generated changes need clearer rules.
Engineering standards
Which expectations are documented, which live in senior engineers' heads, and where standards differ by team, repo or folder.
Ownership and decision rights
Who owns AI-generated changes, who decides what is safe to merge, and where accountability gets blurry as more code is machine-written.
Validation system
Which checks give engineers confidence, which ones are ignored, and where AI-generated changes need stronger validation.
Quality metrics
What leadership wants to understand, what the team can measure today, and which metrics would create better decisions.
Rollout governance
Which repos should go first, who needs to buy in, how exceptions work, and how the model expands without relying on one champion.
What comes out of the sprint
The sprint is scoped around one or two concrete improvements. The exact focus depends on what we find during diagnosis.
- Engineering quality diagnosis for the selected scope.
- Bottleneck map across standards, review, CI, tests, ownership, tools, metrics and AI usage.
- AI adoption risk map.
- Baseline quality metrics.
- Recommended operating model for the selected team or repos.
- One or two implemented workflow improvements.
- Rollout recommendation for more repos or teams.
- Final leadership report with what should become product, process or policy.
The operating model you walk away with
Each sprint turns one messy engineering quality problem into a concrete operating model your team can use.
A clear quality baseline across repos, teams, and AI workflows.
A map of where AI-generated code creates risk in your delivery process.
Written engineering standards that humans and tools can both enforce.
An ownership model for AI-assisted changes before and after merge.
A rollout plan for which teams, repos, and workflows are ready for AI adoption.
A prioritized backlog separating tooling gaps, process gaps, and product work.
A governance model for when AI can assist, when humans must decide, and who signs off.
Metrics to tell whether AI is improving engineering quality or just moving review work around.
Four weeks to turn one quality bottleneck into a repeatable operating model
We inspect the selected team or repos, recent bugs, test signal, CI behavior, standards, ownership, tooling and AI usage.
- Quality baseline
- Top validation gaps
- Agreed sprint focus
We define the target operating model for the selected quality problem.
- Workflow proposal
- Measurement plan
- Owner responsibilities
- Rollout plan
We help change one or two concrete parts of the workflow: test strategy, CI signal, ownership rules, AI usage guidelines, standards or quality gates.
- Implemented workflow changes
- First signal readout
- Team-owner feedback
We document what worked, what still needs product, process or policy work, and how to expand the model.
- Final report
- Rollout recommendation
- Next-step plan
Built for engineering teams where AI usage is becoming an operating question.
Good fit
- 50+ engineers.
- Multiple teams or repositories.
- Active use or evaluation of Cursor, Copilot, Claude Code, Codex, Devin or internal agents.
- Platform, DevEx or Engineering Excellence owner.
- Visible quality, CI, standards or rollout problems.
- Leadership wants to scale AI usage with more control.
Poor fit
- Very small teams.
- Teams with no engineering leadership sponsor.
- Companies looking for cheap implementation labor.
- Buyers who only want Kodus onboarding.
- Teams expecting AI adoption to be solved by buying one more tool.
- Organizations that want fully autonomous merge decisions.
Kodus works close to where quality decisions happen.
Kodus is open source AI code review without vendor lock-in. That matters here because the pull request is one of the places where AI adoption becomes visible. Code has changed. Reviewers need to decide what is safe. CI either gives confidence or it does not. Standards either show up in the workflow or stay in someone's head.
The sprint uses that practical signal to help your team improve the broader engineering quality workflow. Treat it as field work around real repositories, real review behavior and the quality systems your engineers already use.
Open source, no lock-in
Inspect it, self-host it, point it at any model. The review pipeline stays yours.
15+ years in the room
Building software and advising engineering teams, combined across the founders.
Benchmark-grade agents
The agent systems we build rank near the top of public code-review benchmarks.
Want to understand where AI is stressing your engineering workflow?
Bring one team or 3 to 5 repositories. We will help you diagnose where quality breaks down, improve one or two concrete bottlenecks, and leave with a model you can reuse.
Founder-led · one company per segment · we reply within one business day.
Questions before you bring a team in
No. Kodus may be part of the solution, but the sprint starts with the workflow. We look at how quality works today across standards, review, CI, tests, ownership, metrics and AI usage.
No. Review is one area we may inspect, but the sprint is broader than review comments. The goal is to understand and improve the engineering quality workflow around AI-assisted development.
The recommended scope is one engineering team, one business unit or 3 to 5 repositories.
You leave with the final report, implemented changes in the selected scope and a recommendation for the next rollout. If there is a strong fit, the sprint can lead into a broader Kodus rollout or a managed engineering quality program.
No open-ended custom development. If a need repeats across customers, we classify it as product, playbook, configuration, integration, customer-specific customization or not worth doing.
Initial pricing hypothesis: Brazil R$40k–R$80k for 4 weeks; US & EU US$10k–US$25k for 4 weeks. Recommended starting anchors: R$50k (Brazil) and US$15k (US & EU).