WCAG specialist focused on criterion-level evidence across perceivable, operable, understandable, and robust behavior, without overstating automated scan results as conformance.
A perspective available to CARBON—not a claim that this tester has reviewed your project. Findings require execution evidence.
Compatibility specialist focused on browser, device, viewport, operating-system, assistive-technology, and support-matrix evidence, with explicit coverage gaps rather than assumed portability.
A perspective available to CARBON—not a claim that this tester has reviewed your project. Findings require execution evidence.
Performance specialist focused on user-visible latency, Core Web Vitals, payload and request cost, caching, API timing, scalability, mobile constraints, memory, and leaks.
A perspective available to CARBON—not a claim that this tester has reviewed your project. Findings require execution evidence.
Content and UX-writing specialist focused on page identity, clear copy, information architecture, credibility, navigation, status communication, readability, and content quality.
A perspective available to CARBON—not a claim that this tester has reviewed your project. Findings require execution evidence.
Forms specialist focused on input contracts, validation, boundaries, state, submission, recovery, data quality, conversion barriers, and accessible interaction.
A perspective available to CARBON—not a claim that this tester has reviewed your project. Findings require execution evidence.
First-impression and conversion specialist focused on value clarity, trust, navigation, calls to action, responsive composition, dead ends, and page credibility.
A perspective available to CARBON—not a claim that this tester has reviewed your project. Findings require execution evidence.
Checkout and payment specialist focused on order accuracy, address and payment input, trust, retry safety, pricing truth, completion, and conversion-blocking failures.
A perspective available to CARBON—not a claim that this tester has reviewed your project. Findings require execution evidence.
AI
Virtual AI tester
Priya
Shopping Cart Tester
Shopping-cart specialist focused on line-item state, quantities, promotions, totals, inventory changes, persistence, accessibility, and safe transition to checkout.
A perspective available to CARBON—not a claim that this tester has reviewed your project. Findings require execution evidence.
AI
Virtual AI tester
Mateo
Pricing Page Tester
Pricing and subscription specialist focused on plan clarity, comparison, currency and locale, hidden conditions, billing cadence, conversion paths, and truthful claims.
A perspective available to CARBON—not a claim that this tester has reviewed your project. Findings require execution evidence.
AI-generated-code specialist focused on logic, boundaries, null and empty states, failure handling, API use, security, privacy, tests, code smells, state, and misleading AI shortcuts.
A perspective available to CARBON—not a claim that this tester has reviewed your project. Findings require execution evidence.
Turn a promising answer into a repeatable investigation.
Jason Arbon CEO, testers.ai
/carbon-eval
AIDiegoAI ChatbotAIJasonAI Code Review
The assistant gave a good answer when you tried it. Then you tried it
again and it gave a different one.
That is not automatically a defect. It is a reason to evaluate the
behavior over the population and conditions that matter.
/carbon-eval helps design, implement, run, and analyze
evaluations for prompts, models, retrieval, ranking, agents, and other
variable AI features.
Choose the population
before the average
A retrieval assistant may answer common questions well and fail when
documents conflict. A personalized recommendation can look sensible for
the default profile and become inappropriate for a less common one.
Useful evaluation defines the intended population, important slices,
expected behavior, unacceptable outcomes, and repetition strategy. A
convenient collection of prompts is not automatically
representative.
Exact checks are valuable where the contract is deterministic.
Semantic judgments need a defensible rubric and some examination of the
judge itself. A model confidently rating another model is not
independent ground truth by default.
Keep the experiment
attached to its version
/carbon-eval compare the current and proposed retrieval prompts on the same approved cases; include conflicting sources and repeated runs
The report should preserve the relevant prompt, model, data,
retrieval, tool, and evaluator context. Show variation and severe
failures rather than hiding them behind a single mean.
Running an evaluation is subject to access, data-sharing, and cost
permissions. A generated harness is still unexecuted until it actually
runs.
The question is not whether AI can produce a pleasing example. It is
whether the behavior remains useful under the conditions your product
will encounter.
Install CARBON at testers.ai/carbon for a supported
coding agent such as Claude, Codex, or Cursor.
