Service · Aarohii AI Solution Private Limited
AI quality engineering & UI testing
AI testing has two layers: whether the model's answer is acceptable, and whether the product UI still works. Aarohii covers both. The UI product is PixellPeep.
Model evaluation
Golden sets, human review for high-impact actions, and a rollback switch. This is not a substitute for product QA. It is how you notice drift after launch. See the launch checklist.
A workable eval set is smaller than teams expect: fifty to a hundred real cases, chosen to include the awkward ones, with expected outcomes written by someone who does the job. Public benchmarks are not your product — a support assistant needs tests built from your tickets, and a retrieval feature needs two tests rather than one.
For a RAG feature those two are: did retrieval fetch the right passage, and did the answer stay inside it. Kept together, they hide the failure that matters. Background: what LLM evaluation is.
What we set up
- The eval set, and the habit of adding every real failure to it.
- Pass/fail rules, so results are a decision rather than a discussion.
- A named owner — evaluation without an owner stops within two months.
- Traces, so a failure can be replayed instead of described. See AI observability.
- A rollback switch, and a person who is allowed to use it.
UI and visual regression
PixellPeep is Aarohii's AI-assisted UI testing product, live at pixellpeep.com. Hub: UI testing.
It compares screens and flows visually with AI assistance, which catches the class of defect functional tests are blind to: the layout that broke on one breakpoint, the control that moved behind a banner, the state that renders empty for a real account. Those reach users because every assertion passed.
We do not publish percentage "bug reduction" figures without a named baseline and method. If a number is not on a case study with context, treat it as marketing elsewhere — not ours.
What we will not do
We do not claim to replace your test suite, we do not report a quality number without saying how it was measured, and we do not hand over an eval set only we can run.
How an engagement runs
- 20-minute fit call — we say yes or no.
- Written plan — scope, success tests, and what we will not do.
- Build with evaluation hooks and weekly demos you can inspect.
- Support after launch — we stay for the messy month, not just demo day.
Open the proof without an NDA: PixellPeep, Captverse, Auvora, ViraQueue. Buying notes: what drives cost · how to choose a company.
Questions
Is PixellPeep the same as Selenium?
No. PixellPeep compares screens and flows visually with AI assistance. It complements functional automation; it does not replace your whole suite by slogan.
How large does an eval set need to be?
Fifty to a hundred real cases is enough to catch regressions, if the awkward cases are included. Size matters less than whether the expected outcomes were written by someone who does the work.
Who owns evaluation after handover?
Your team. We set it up, document it, and make sure it runs without us — a test suite only the vendor can run is a dependency, not a deliverable.
Why not just review outputs manually?
Manual review does not scale and does not catch drift, because nobody remembers what last month's answers looked like. Tests do.
If the problem maps to work we actually ship, we will say so in 20 minutes.
Request a fit call