How Revelara works, how it handles your code and data, which agents it supports, and what it costs.
Revelara catches reliability risks in your coding agent before they ship. As you build, it surfaces the failure modes that have caused real outages, patterns drawn from thousands of public incident reports and postmortems, and explains them so the engineer at the keyboard can decide what to do. It is proactive risk analysis, not another dashboard you check after something breaks.
It is proactive, not reactive. Observability and incident tools wait for the break, then help you clean up faster. Revelara surfaces the risk before it ships. It also matches your code against thousands of real public incidents, so it is pattern-matching what actually broke other teams rather than guessing from first principles. And it hands judgment to the developer instead of silently auto-fixing.
STAMP (System Theoretic Accident Model and Processes) is a framework that consists of two methodologies: STPA (Systems-Theoretic Process Analysis) and CAST (Causal Analysis and Safety Testing). STAMP came out of Nancy Leveson’s group at MIT. Aerospace, healthcare, and NASA use it to find design flaws in systems where an outage is not survivable. Instead of scanning for known-bad patterns, it treats your system as a set of control loops and feedback paths, then looks for the gaps: the action with no way to detect whether it worked, the loop with no feedback, the failure mode nobody designed a response to.
Revelara’s analysis is STPA-inspired. It is not the full manual process, which is heavyweight and slow by design. We run it against your design docs and agent specs to surface unsafe control actions and missing feedback before a line of code is written, so the gap lands in your spec instead of in an incident review.
It is built for the non-life-critical use case. We would not stake a life-critical system on it, and neither should you. If that’s your use case, Revelara is not a fit and you should find a trained facilitator for these processes. For everyone else shipping normal software who would rather catch a design gap early, it is one of the most useful things in the product.
Fair skepticism. Plenty of AI products are a prompt in a trench coat. For Revelara, the skill (or MCP server) is just how it plugs into your coding agent. If you look at it in isolation you get “look for reliability issues,” which any of us could write in five minutes.
What you’re paying for is what that skill is grounded in:
The skill’s job is to retrieve and apply all of that at the moment code gets written. It’s the difference between a search box and the index behind it. The box is easy. The index is the work.
If it were only the skill, we’d agree it isn’t worth paying for. We wouldn’t pay for it either.
We measured it. Grounding an agent in real incident data and the controls catalog produces a measurable lift in what it catches and a measurable drop in its error rate, and that holds even against newer frontier models. In fact, counter-intuitively, it holds better against newer more advanced models. The lift is largest exactly where the base model is weakest, specialized areas.
Every specialized lens (our word for an agent) is evaluated before it ships. If a lens does not beat the baseline, we would rather fall back than risk a worse answer.
No, Revelara does not upload your source code. The analysis runs on your machine, and only structured findings, not your code, are sent to us.
When you run a scan, the code reading and reasoning happen locally, inside your own developer environment (your coding agent / IDE session). Revelara never clones your repository, and there is no server-side agent that ingests your codebase.
Only the findings are submitted to Revelara, a structured risk record per issue:
internal/api/handler.go:145)So a reference to a location and a brief description travels, but not your files, not your repository, not bulk source. Secrets in git-ignored files are explicitly filtered out before anything is submitted.
Because the analysis is performed by an AI agent running in your own coding session, your code is read by that model provider as part of normal coding-agent operation, the same as any other task you run in that agent. That is your existing tooling relationship, not a Revelara data flow. Revelara itself receives only the findings above.
For security or compliance reviews, DPAs, or our subprocessor list, see our Privacy Policy or reach us at hello@revelara.ai.
Revelara installs into these coding agents today:
If you use a coding agent that speaks MCP, it very likely works even if it is not listed. Want one supported that you do not see here? Let us know and we will add it.
Today Revelara connects to issue tracking (GitHub and Jira, with state tracking), document sync (Confluence, Notion, Google Drive on the way), and notifications.
It does not pull live production metrics. The analysis is grounded in your code and your postmortems, not your telemetry. If you already write incident reports, that is the part worth knowing: bring them in and they become org-specific, tenant-isolated signal. The more of your own history you feed it, the more the analysis tunes to the failures your team actually hits, rather than only the ones that happened to other people that they’re willing to write about in public.
No. The whole design is to put the human in charge and give them the context to reason well. It builds reliability judgment into the person at the keyboard, it does not remove them from the loop.
The opposite. It is built for software development teams that own reliability themselves without dedicated SREs, which is most teams.
No. Measured, the tool definitions are about 3,000 tokens, roughly 1.5% of a 200k context window. That is a focused set of search and retrieval tools, not the double-digit percentages that heavy MCP servers cost. Tool-call responses stay paginated and snippet-sized.
A 21-day trial, no credit card required. It converts to a paid plan after that. Full details are on the pricing page.
Purple and rose on a dark background is the current house style of half the AI-generated sites on the internet, so it’s a fair question. And the answer is the palette is deliberate. It’s a homage to Prince. I’ve been a fan from a very young age, from when I first heard “1999” and watched “Purple Rain”. I wanted the color, and starting my own company finally meant I could use it without asking anyone’s permission.
For the record, I’m a reliability engineer, not a designer, so I had help with the CSS. But the reason it’s purple is all me. Judge the product on how well it works for you, but if the color is what got you reading this far, I think Prince would approve.