Question 1

What does SquareNumbers actually do?

Accepted Answer

Two things. We build quality engineering and test automation systems that prevent software failures and give teams confidence before every release. And we design and build AI automation, from intelligent workflows to bots and agentic systems, so businesses can adopt AI in a way that is practical and reliable.

Question 2

Can you build a chatbot for me?

Accepted Answer

Yes, end to end. That covers scoping the use case, LLM selection and integration, conversation flow design, guardrails, validation, and production readiness.

Question 3

What kinds of AI automation do you build?

Accepted Answer

Chatbots, document processing, decision-support tools, automated reporting and summarisation, API-connected agents, LLM-powered internal tooling, and AI-based routing or classification.

Question 4

Can you help figure out where AI actually adds value in our business?

Accepted Answer

Yes. Part of the work is scoping, identifying where AI reduces real friction and designing a solution that fits rather than one that follows a trend.

Question 5

What is test automation and how does it help my business?

Accepted Answer

Test automation replaces manual testing with scripts that run on every change, catching regressions early and giving teams consistent quality signals without manual QA overhead.

Question 6

What does a QA consultant actually do?

Accepted Answer

A QA consultant assesses your testing and release process, identifies gaps where failures could reach production, and designs or implements the automation and validation systems to fix them. The focus is on measurable release confidence, not just test coverage.

Question 7

What usually breaks in AI system releases?

Accepted Answer

The most common issues are inconsistent model outputs that passed evaluation but fail on real user inputs, integration points where the AI connects to other systems, and missing guardrails that only matter in edge cases. Releases also break when teams skip regression testing after model updates assuming nothing changed.

Question 8

Why do automated tests still miss production failures?

Accepted Answer

Most test suites cover happy paths. Production failures happen in the gaps: unexpected input combinations, infrastructure behaviour under load, third party dependencies, and timing issues that only appear at scale.

Question 9

How do you validate AI behaviour before production?

Accepted Answer

We build evaluation frameworks that run the model against a defined set of inputs and expected outputs. We test for consistency, edge case handling, and regression after any change. The goal is a clear pass or fail signal before anything goes live.

Question 10

What causes flaky end-to-end tests?

Accepted Answer

Usually timing issues, shared test data, environment instability, or tests that depend on UI state that changes. Flaky tests are a signal that the automation is brittle, not that the system is broken. The fix is usually at the architecture level.

Question 11

How do you reduce release risk in distributed systems?

Accepted Answer

By adding validation at the boundaries. Contract tests between services, synthetic monitoring in staging, and release gates that check system health before traffic is shifted.

Question 12

How do you validate backend workflows without relying on UI tests?

Accepted Answer

Through API level testing, event driven assertions, and direct validation of the data state after each step. Testing at the service layer gives faster, more stable feedback on whether the workflow actually did what it was supposed to.

Question 13

How do you detect AI drift or behavioural changes after deployment?

Accepted Answer

By monitoring production outputs against a baseline. We track metrics like output distribution, confidence scores, and user correction rates over time. When those signals shift beyond a defined threshold, it triggers a review.

Question 14

When should teams invest in automation architecture?

Accepted Answer

Before the manual effort becomes the bottleneck. If your team is spending more time running tests than writing code, or if releases are being delayed because QA cannot keep up, the investment is overdue.

Question 15

What makes testing AI systems different from traditional software?

Accepted Answer

Traditional software has deterministic outputs. AI systems are probabilistic, so the same input can produce different outputs. Testing shifts from pass or fail assertions to evaluating consistency, accuracy, and behaviour across a range of inputs over time.

Question 16

How do you assess confidence before a release?

Accepted Answer

Through a combination of automated test results, coverage of known risk areas, comparison against previous release baselines, and monitoring of staging behaviour. Confidence is not a feeling, it is a set of signals.

Question 17

What are common gaps in CI/CD validation pipelines?

Accepted Answer

Most pipelines run unit and integration tests but skip system level validation, performance checks, and AI behaviour regression. Teams also often lack a clear go or no-go decision point.

Question 18

How do regulated teams approach AI validation?

Accepted Answer

With documentation, traceability, and defined thresholds. Regulated teams need to show that AI outputs were evaluated against known criteria and that any change was tested before release.

Question 19

What should be manually validated versus automated?

Accepted Answer

Automate anything that runs repeatedly: regression, smoke tests, API contracts, AI output scoring. Keep manual validation for exploratory testing and judgement calls that require human context.

Question 20

How do you scale QA without scaling manual testing?

Accepted Answer

By shifting from manual test execution to automation and monitoring. You build systems that run tests on every change, flag anomalies automatically, and surface only what needs human attention.

Question 21

What signals indicate a system is becoming operationally risky?

Accepted Answer

Increasing incident frequency, test suites that are ignored because they are unreliable, releases that take longer to validate over time, and teams losing confidence in their own deployments.

Question 22

Do you work with regulated industries like fintech or healthtech?

Accepted Answer

Yes. Much of the work has been in regulated financial environments where release confidence, auditability, and compliance risk are real constraints. The same rigour applies to any regulated domain.

Question 23

What is MCP and why does it matter for testing?

Accepted Answer

MCP (Model Context Protocol) is a standard for exposing tools and data to AI agents. In testing, it means quality signals like coverage and execution results can feed directly into AI-driven engineering workflows.