Make your engineering workflow AI native.
We set up AI-native workflows inside your real codebase. That can include PR automation, AI-assisted review, test generation, commit workflows, and debugging support. Then we keep improving the setup as models, tools, and working patterns change.
Using AI is not the same as
wiring it into how your team ships.
Most teams have AI somewhere in the editor. Very few have it built into pull requests, review, testing, debugging, and delivery. That gap keeps growing because models, tools, and workflow patterns change faster than most teams can properly evaluate them.
We set up the workflow first.
Then your team gets more from it over time.
We work in your actual codebase and set up the first production-ready workflows there. That can include PR automation, AI-assisted review, test generation, commit workflows, and debugging support. This is not a demo or a sandbox. It runs in your repo, with your CI, and around your conventions.
Tooling changes constantly. We track what changed, test what is worth adopting, and bring useful improvements back into your workflow. Your team does not have to spend time chasing every new model, tool, or agent pattern.
As your team starts using the setup, new questions come up. What should be automated next? What should stay manual? Which new tools are worth your time? You get direct access to someone already doing this in production, not generic AI advice.
Most teams could probably build some of this internally. Keeping it useful over time is the harder part.
The real cost is not just implementation. It is the ongoing work of evaluating fast-moving tooling, testing what holds up in production, and deciding what deserves a place in your workflow. We do that work continuously and bring back only what proves useful in real shipping environments.
Book a Call →
Clear enough to qualify.
Flexible enough to fit the work.
This is usually structured as a one-time implementation engagement to set up the first working system in your real codebase, followed by an optional monthly retainer for workflow upgrades, tooling evaluation, and ongoing support.
Scope and pricing depend on team size, stack complexity, and how many workflows you want in place. Most initial engagements start in the low four figures.
Estimate your ROI →
Practical workflows.
Built around how your team already works.
PR Workflow Automation
Generate cleaner pull requests using ticket context, diff context, conventions, labels, and reviewer routing. The goal is not prettier PRs. It is less manual overhead and a faster start to review.
AI-Assisted Code Review
Set up AI review to catch obvious issues, enforce team conventions, and surface risk before human review begins. Humans still make the call. They just start with better signal.
Commit Workflow Standardization
Generate commit messages from real diffs so history stays consistent without constant policing. This helps teams keep better conventions without turning them into a recurring debate.
Repo-Aware Test Generation
Use your codebase context to generate tests that fit your stack, structure, and conventions. The point is not bulk output. It is reducing the effort needed to reach useful coverage faster.
Debugging Support Workflows
Create workflows that can read logs, inspect surrounding code, summarize likely causes, and suggest next steps. That means less context switching and faster movement from symptom to investigation.
Chained AI Development Flows
For teams ready for more, we can set up multi-step flows that read context, make changes, generate tests, and prepare review-ready output inside guardrails you define.
Not AI advice from the sidelines.
Work shaped by production practice.
I'm Vamsi Gunturu. I've spent a decade shipping production systems used by millions, from leading engineering in high-growth environments to building cross-border payment infrastructure handling hundreds of millions in volume.
I did not come to this by chasing trends. This is how I build. I started using AI-native workflows early and refined them in real production environments, not toy repos or one-off demos.
I have run versions of these setups in engineering meetups, internal sessions, and day-to-day product work. What I implement for teams comes directly from that experience.
If it does not help real teams ship better, it does not make it into the workflow.
Want to see what an AI-native workflow looks like in your team?
On the call, we look at how your team uses AI today, where the friction still is, and what a practical repo-integrated setup could look like for your stack.