QA that uses the product.
Autonomous QA agents that drive real web interfaces the way a user does, and verify the whole stack, not just unit logic. Point OGIAM at a system and it runs the full engagement: it learns the system, diagnoses and pen-tests it, proposes the automations to fix it, and hands your engineers a complete report. Enterprise-grade QA and InfoSec built into the agentic SDLC, not an afterthought.
It clicks through the real interface.
QA agents drive our own apps today, and reach vendor and DMS portals through a server-side browser driver. They use the interface as it ships, without changing the test surface.
- Real rendered content, not a blank 200.
- No CSP or console violations in the browser.
- Key widgets render non-zero content or an explicit empty state.
- Core journeys complete end to end on the deployed URL.
A unit test can pass while the page a customer actually sees is blank. Driving the real UI closes that gap; the agent sees what the user sees.
The server-side browser driver lets a run reach surfaces beyond our own apps without asking the target system to add hooks or change anything for the test.
A 200 is not a pass.
The agent checks what a user would judge: that real content rendered, that the console and CSP are clean, that key widgets show non-zero content or an explicit empty state, and that core journeys complete end to end on the deployed URL.
Real rendered content, not a blank 200.
No CSP or console violations in the browser.
Key widgets render non-zero content or an explicit empty state.
Core journeys complete end to end on the deployed URL.
The same agent attacks the product.
QA that drives the UI is one half. The other half thinks like an attacker. This is active, authorized penetration testing: the agent does not just flag a risk, it confirms it by safely exercising the running app for the flaws that actually get exploited, broken access control and IDOR, authentication bypass, JWT weaknesses, missing rate limits, error and information disclosure, weak security headers, and injection. Every active test is authorized by a time-boxed scope, rate-limited, instantly stoppable with a kill switch, and runs read-only with no destructive actions, all under the same governance gate. Every finding comes back confirmed, classified, located, and fixable, not a vague warning.
Scope the query by the caller's workspace before returning the record.
IDOR confirmed: one user actually reaching another's records.
Confirmed: a protected route reached without valid auth.
Weak signing, missing audience, or tampering the app accepts.
Endpoints proven floodable or open to brute-force.
Stack traces or secrets confirmed leaking in responses.
Missing CSP, HSTS, and the rest of the hardening set.
Untrusted input confirmed reaching a query, command, or template.
Because each risk is confirmed against the running app, not just inferred, the signal stays clean instead of drowning you in maybes. Each finding is severity-ranked, CWE-tagged, and flagged when it would be bounty-grade, and every active test stays inside the authorized scope: rate-limited, read-only, and stoppable at any moment with the kill switch.
It scans the AI surface, not just the app.
Agentic systems add an attack surface a normal scanner never looks at: the Model Context Protocol servers your agents trust, and the AI usage scattered across your codebase. OGIAM statically reads your configuration for the real MCP attack classes, and discovers ungoverned LLM usage and leaked AI keys. No connection is made to the servers; it reads what you already have on disk.
Tool poisoning: a server's tool description carries hidden instructions that hijack the agent.
Rug-pull servers pulled at a floating version that can change under you after review.
API keys and tokens written straight into MCP config instead of a secret store.
Server endpoints reachable with no auth, open to anyone who can route to them.
Zero-width and bidirectional characters that hide instructions from a human reviewer.
Execute-class tools granted far wider reach than the task in front of them needs.
It reads tool descriptions for poisoning, pins it can prove, hardcoded secrets, unauthenticated endpoints, hidden zero-width and bidirectional characters, and over-broad execute-class tools. Static and config-only: it never connects to an MCP server to test it.
It surfaces the AI usage nobody told you about.
AI asset discovery finds ungoverned LLM usage and leaked AI keys across a codebase. It is precision-first and signature-grade, not a noisy regex sweep, so what it flags is worth acting on. The shadow AI a team wired up on its own stops being invisible to security.
It judges the experience, not just the code.
Beyond bugs and security, it evaluates real usability the way a person would: unclear or icon-only controls with no label, dead click states, confusing modals, tap targets too small to hit on a phone. On top of that, accessibility measured against WCAG, color contrast, missing labels, and screen-reader structure. These are the UX gaps that quietly cost conversions and lock people out.
Icon-only buttons with no accessible name, so nobody can tell what they do.
Controls that look interactive but respond to nothing.
Dialogs with no clear exit or an unclear primary action.
Buttons too small or too close to hit reliably on a phone.
Text that fails WCAG contrast and is hard to read.
Missing labels and broken structure that locks out assistive tech.
It walks the journey and counts the friction.
It runs the flows that matter, sign up, check out, finish a task, and surfaces where users get stuck: dead-ends, far too many steps, loops and repeated actions. These are the death-by-a-thousand-clicks problems manual QA misses.
Point it at a system. Get back the work.
Deploy OGIAM against a system and it runs the full engagement on its own: it learns the whole system, finds what is broken and unsafe, proposes the automations to fix it, and hands your engineers a complete report. Every step is governed by the gate and recorded in a tamper-evident log.
Bind to the system through a governed connector. Read-only by default.
Profile the whole system: surface, data model, and every integration it talks to.
Drive the UI and pen-test the app. CWE-classified findings, located and fixable.
Propose gate-governed automations to close the gaps, with the evidence for each.
Compile the engagement into a report the client's engineers can act on the same day.
- Executive Summary
- System Map
- Security Findings
- Diagnosed Issues
- Work Performed
- Recommended Automations
Prepared for your engineers. Every finding located, every action audited.
It learns the system first: the routes, the data model, and the third-party services it depends on. Knowledge before action.
It diagnoses and attacks: real-UI QA plus penetration testing, every finding severity-ranked and CWE-tagged.
It proposes the fix as gate-governed automations, and never executes beyond the authority it was given.
It hands back one report, posture-graded and prioritized, that an engineering team can work straight off.
It does not stop at the finding.
A finding you have to act on alone is half a fix. For issues it can remediate, the agent opens the change as a review-gated pull request: it never merges, your engineers review and ship, and the next run re-verifies it. And the engagement re-runs on a cadence, so fixed issues clear themselves and regressions surface automatically. Posture stays a live line, not a report that goes stale the day it lands.
Re-tested automatically. A real fix clears itself; a regression comes back.
The risk is proven against the running app, not guessed. You start from a real issue.
For issues it can remediate, the agent opens the change as a review-gated pull request. It never merges; your engineers review and ship.
The next run re-tests the same risk, so a real fix clears itself and a regression resurfaces on its own.
The engagement re-runs on a cadence. Security posture becomes a live line, not a one-time report.
A usability grade you can track.
Every scan rolls up to a usability and accessibility grade that trends over time and is monitored continuously, so a regression surfaces the day it ships, not at the next audit.
Signal, not noise.
Findings are precision-tuned against real production apps, so you get issues worth fixing instead of a wall of false alarms. And the report states exactly what it could and could not reach, so a clean result is genuinely clean, never a false sense of safety.
Every scan is recorded.
Each run is kept, so regressions in coverage or health become visible across runs. A health line over time is more useful than a single green checkmark.
A test agent cannot exceed its authority.
QA agents are principals under the same OGIAM gate as every other agent. They operate inside a capability ceiling, so a test run can drive the product without ever stepping outside what it is allowed to do.
One tool sees every layer, and connects the dots.
Most tools check only the website, or only the code, or only the database. We check the front-end people see, the back-end behind it, the database underneath, security, accessibility, and the real user journey, all on the same system, in the same run. And we remember every past scan. That is the difference: most tools see one layer, we see them all, so we catch problems that only appear when the layers combine.
A harmless-looking setting on the page, a missing check on the server, and an open database permission each look minor on their own. Together they form a way in. Only a tool watching all three at once can see the path they make.
A flaw you fixed last month quietly returned. Because we remember every past scan, we catch the comeback the moment it reappears, instead of finding it again from scratch much later.
The same weakness showing up across many of your systems is not bad luck, it is a gap in how things get built. We flag it as a systemic issue automatically, because we can see all of them together.
A tool that watches one layer cannot find a problem that only exists between two. And a tool with no memory cannot tell you something old came back. This is insight a single-layer tool structurally cannot produce, because it is not watching everything at once and it does not keep the history.
It tests itself, and proves it is improving.
We do not ask you to take our word for it. We keep practice systems that are deliberately broken, each with a known list of planted flaws, and we run the tool against them again and again. We count how many flaws it catches and track that score over time. Think of a smoke detector that tests itself every night and hands you the report in the morning. You never wonder if it still works, because you can see the catch rate, and you can see it rising.
Practice systems are built with a known list of flaws hidden inside them.
The tool runs against them with no hints, exactly as it would on a real product.
We count how many of the known flaws it caught and how many it missed.
That score is recorded every time, so you watch the catch rate rise, never guess at it.
We hold ourselves to the same yardstick as the best.
It is one thing to test ourselves. It is another to test ourselves against the best tools available. So we run the most respected scanners on the market on the exact same practice systems, then compare catch for catch. We match or beat them, and we publish the score to you. This is proof on an identical target, not a marketing claim you have to trust.
We test ourselves on the identical systems the most respected tools are tested on, so the comparison is fair.
We count what each tool finds on those systems, side by side, with nothing hidden.
We publish the result to you. You judge us on the number, not on a sales pitch.