Releasing fast shouldn’t mean breaking things. As your product grows, Ogoron takes over your QA process end‑to‑end. It understands your product, generates and maintains tests, and continuously validates every change - replacing a systems analyst, test analyst, and QA engineer. Get predictable releases, fewer bugs in production, and full coverage without manual effort. Ship faster. Stay in control. Break nothing
Free Options
Launch Team / Built With
How „smart“ is the analysis? Does it really understand business logic? We have complex financial rules - would love to know how deep it goes.
Ogoron
@sevryukov_vs Good question. The analysis can go fairly deep when the business logic is actually expressed in the artifacts available to the system — code, product behavior, specs, and provided documentation.
If the rules are complex but still fairly standard for the domain, modern models are often much better at reconstructing them than people expect. We were honestly surprised ourselves by how much sensible structure they can extract directly from code.
That said, we try to stay realistic: if critical business logic is not recoverable from the available sources, Ogoron should not pretend to understand it perfectly. In those cases, trustworthy grounding and user clarification still matter
Curious how it handles edge cases and unexpected flows .That’s usually where automated QA tools start to break down.
Ogoron
@francis_dalton Very fair point – edge cases and unexpected flows are exactly where automated QA usually starts to get real.
Our view is that the goal is not to pretend everything is expected. It is to recognize when the system is operating inside a high-confidence pattern, and when it is not. When Ogoron can reliably interpret the situation, it handles it automatically; when it cannot, it surfaces the ambiguity instead of forcing a false answer.
A big part of the product is continuously expanding that high-confidence zone. In practice, many "unexpected" cases are not unique at all – they are recurring patterns that different teams have already run into in one form or another. A lot of the work is turning more and more of that real-world experience into something the agent can recognize and handle safely
Congrats!
Can I opt out of any data sharing for product improvement? We can't allow any data to leave our network
Tnx!
Ogoron
@konstantinkz Thanks – in the standard managed setup, some data does pass through our infrastructure, and requests currently also go to OpenAI as the external LLM provider.
So if your requirement is that absolutely no data leaves your network, we should be transparent: we do not fully support that today. We can discuss deployment on your own infrastructure, but external LLM calls still remain part of the current architecture
congrats with the launch, guys! how do you evaluate a complex product like this? any benchmarks/metrics you can share?
Ogoron
That’s a good question.
We do have internal benchmarks built mostly from our own projects and OSS codebases. They measure the technical side of Ogoron – things we can evaluate automatically: whether it works across different stacks, produces actually runnable tests, operates on historical git diffs, tolerates a moderate amount of context, and follows flexible but strict-enough workspace rules without degrading.
But a big part of Ogoron’s value is its ability to operate over long periods, keep tests maintainable, and stay useful in changing real-world projects. That part is much harder to benchmark well, and often prohibitively expensive to simulate. So a lot of our evaluation comes from using Ogoron on our own projects and partner projects and watching how it performs in practice.
If we talk about early technical numbers, we currently see roughly 85% runnable UI tests, and around 70% of generated UI tests being immediately useful and correctly implemented. Unit tests perform noticeably better, while API tests are somewhere in between.
We also track our code bug / test bug / unsure classification on failing runs. In early pilots, the share of unsure cases varies significantly depending on the project and failure type, while misclassification is currently around 12-15%. This remains one of our main areas for improvement
The speed and cost numbers are compelling. Curious about one specific scenario: how does Ogoron handle testing when the valid test paths depend on prior user data? A dashboard that renders different options based on account history, or a workflow where step 3 depends on step 1. Does the agent discover those conditional paths, or does someone pre-define them? That's usually where automated QA starts requiring manual scaffolding.
Ogoron
@muggleai Yes, that’s a very good question. Modern products often have heavy customization based on account history, location, prior actions, and other contextual signals.
In practice, we usually approach these flows in two ways. First, many conditional branches can be reconstructed from the source code, and Ogoron is quite good at digging them out. Second, we try to atomize the problem.
Our strategy is not to brute-force every possible end-to-end variation – that space is effectively unbounded. Instead, we validate individual logic blocks with their conditions, and combine UI, API, and unit testing so they reinforce each other. That’s also why we don’t rely on Playwright-only coverage: using E2E for everything would be too expensive and not pragmatic enough.
We’re aiming for a balance between run cost and overall confidence, rather than exhaustive enumeration of every path
I like that it doesn’t pretend to know everything. Asking for clarity instead of guessing feels more usable. The challenge is still logic that isn’t written anywhere.
Ogoron
@sourav_sheth1 Thanks – and yes, that’s exactly one of the core limits.
Ogoron can read large amounts of code, project files, and any explicit context you give it. But the bottleneck begins where the correct decision is not supported by artifacts and cannot be inferred from general logic alone.
In those cases, making a plausible assumption is often worse than asking for clarification. For QA, false confidence is usually more dangerous than uncertainty
QA usually breaks once scope expands and edge cases pile up. This feels like it tries to handle that reality better. GitHub only support might limit some teams.
Ogoron
@manash_pratim2 Hi Manash – that’s exactly the direction we’re aiming for. Our view is that truly exceptional cases are a much smaller share than cases that are simply rare. So our goal is to keep expanding into narrower domain edges over time and make Ogoron genuinely strong at handling nuance.
As for GitHub-only support, email me at vmynka@ogoron.com and I’ll give you early access to Ogoron for repositories in other Git systems as well