DebugFlow unifies production signals, correlates them with your multi-repo codebase, and proposes fixes your team can review and merge — so engineers spend less time investigating and more time shipping.
Siloed tools show fragments of the problem; engineers spend hours correlating logs, tickets, and code by hand.
DebugFlow is an incident intelligence platform for engineering teams. It links production signals to your code, uses AI to produce a readable root cause, and supports assisted remediation with suggestions that flow into your team's review process. On top of that, it gathers the surrounding context — repositories, documentation, and operations — so you can move from the alert to the next step with less manual investigation.
Jumping between consoles and chats to assemble the story of an incident slows the response and exhausts the team. In a 30-engineer org, four hours per week per engineer on cross-service debugging adds up to roughly ~$500k per year in salary spent on triage.
Start from a single view: what failed, where in the system, and which parts of the code deserve attention first — backed by retrieval across all your repositories.
Three blocks that cover what most engineering teams actually need — without promising a thousand integrations on day one.
The point is to close the loop: from the signal to shared understanding to the next step in code — with fewer alignment meetings and fewer "does anyone remember where this was deployed?" moments.
Pick the model that fits your data policy and your team's pace.
Quick start, continuous updates, and less operational surface for your team to maintain.
When data and code must stay inside your perimeter — for regulatory reasons or by choice.
DebugFlow is built as a set of stateless containers backed by a managed Postgres database, a vector store, and object storage — a shape that maps cleanly to AWS ECS/EC2, RDS for PostgreSQL, S3, and ElastiCache for Redis. The same architecture is what we deploy in customers' VPCs under the self-hosted option, so SaaS and self-hosted share one operational model.
VPs of Engineering and CTOs at fintechs, healthtechs, and mid-sized e-commerce companies — where multiple services and repositories show up in the same incident.
Incidents that don't fit inside a single service or a single repository.
When every minute counts and context has to be shared fast across the on-call rotation.
When "just use the cloud tool" doesn't pass internal governance or compliance.
We align on expectations, your technical scenario, and a pilot format — no commitment on the first call.
Team context, current stack, and where the pain is.
Validation on a real scenario, on your timeline.
Expansion and operation under the chosen model.
Send us an email and we'll come back with next steps.
Talk to the team