A population of AI personas explores your app like real users, reaching states your scripts never visit. When one breaks a rule, Abantu confirms it against your app's own server-side state — a pass/fail verdict you can block a release on, not a model's opinion.
The required PAID step never happened — confirmed against Sylius's own order history, not the persona's say-so.
Scripted tests check the steps you anticipated. Real people do the unexpected — and that's where business logic cracks. Abantu behaves like real users and pushes on the rules, surfacing the weaknesses and flakiness scripts never reach.
Personas pursue real goals across roles — with the messiness, impatience and improvisation of actual people.
Out-of-order actions, invalid state transitions, permission gaps and race conditions — probed, not assumed.
Intermittent failures and states that should never be reachable, caught before your users hit them.
Agents now generate features in loops — and their own tests along with them — faster than any team can read the diffs. The question moves from “did it compile?” to “does the behaviour still hold for real users?” That gap is where Abantu lives.
Agents ship features and their own passing tests on repeat. Volume stopped being the constraint.
Knowing the behaviour is right — across roles, edge cases and state — is the hard part. Reading every diff doesn't scale.
Abantu checks what users actually experience and whether your business rules hold — the layer agent-written tests skip.
In a self-hosted Sylius store, Abantu sends four real users through the shop at once — they browse, fulfil, and probe whether the rules actually hold: can an order reach a state it shouldn't? One ships goods whose payment never cleared. The rail below is read from Sylius's own order ledger; the dashed gap is the required PAID step that never happened.
Write a persona — goal, role, tech-savviness, patience. No brittle test code to maintain.
Each persona observes the live page, decides like that user, and acts — until it reaches the goal or realistically gives up.
A hard pass/fail on the workflow, the business-logic weaknesses and flaky behaviour it found, and the friction along the way.
Not per-seat, not per-run, not per-app. One app, gated — you climb as you verify more of its business logic: more critical flows, deeper verifier types, regression baselines. The bill grows because your coverage did.
Priced as CI infrastructure — a gate on every PR, not a per-seat IDE tool. Anchored against the cost of one prevented business-logic incident, not hours saved.
Tech that earns trust. The AI assists; people stay in control.
Abantu runs only against systems you own or are verified to control. It identifies its own traffic and ships no anti-detection tooling — a guardrail, not a weapon.
Agents surface findings and drive flows — your team reviews and signs off. No silent autonomy over what ships.
Built and run from the EU, with data handling designed around GDPR from the start.
Every run produces a readable trace — what each persona did, decided, and where it got stuck. No black box.
Book a 20-minute demo, or send a note. No pitch — we'll see if it fits.