RUBIKSCORPWhy your SaaS AI feature gives wrong answers
Wrong answers are rarely random.
Read: Why your SaaS AI feature gives wrong answersAI audits & practical AI · Pune, India
Understand where your AI fails.
Know what to improve.
We review chatbots, AI features and automated workflows, then give your team prioritized findings and practical next steps. Where a problem needs fixing, we scope that work with you.
For product, operations and industrial teams. Scope and fees agreed before work begins.
What an audit helps you see
Sample report structure · demonstration only
What failed?Unsupported policy answer
Why does it matter?Incorrect refund expectations
What needs attention?Source and escalation rules
What happens next?A focused regression test
Choose the help your team needs
Start with an existing AI problem or a specific operational challenge.
AI System Audit
Find reliability, data exposure and workflow problems in an existing chatbot, agent or AI feature. Receive prioritized findings and a practical improvement plan.
Explore the AI System AuditAI Monitoring
Track agreed checks on a defined schedule. Set alert rules, escalation contacts and review responsibilities around the way your team works.
Explore AI MonitoringApplied AI & ML
Evaluate a specific business problem through data review and a measurable proof of concept. Decide whether further development is justified.
Explore Applied AI & MLFactory AI
Assess a production challenge, camera setup or data source. Define a pilot with clear success criteria before considering a wider rollout.
Explore Factory AIOur work, explained simply
Here is what happens when you work with us, shown with a shop’s chatbot as the example.
Example: a shop’s support chatbot (illustrative)
Can I return my order after 60 days?
Yes, returns are free for a full year.Unsupported: the sample policy allows returns within 30 days.
Testing real and tricky questions…
Problem: the bot invents a return policy.
Why it matters: customers expect refunds you never offered.
Fix first: give the bot your real policy and an escalation path.
Checks keep running on the agreed schedule. The named contact is alerted if answers change.
Checks that run on a schedule
Agreed tests run on the schedule you choose and flag drift, wrong answers and exposure risks. Alerts go to the contacts you name.
Illustrative example, not client data.
From AI assurance to the factory floor
The same discipline applies in production: we assess where AI can add visibility and where it adds risk.
Counting
Illustrative animation, not client footage.
How we work
Review the system, inspect real conditions, report what matters.
Review
We test your AI under real conditions and document what is failing.
Inspect
On site, we look at where cameras, data and workflows can help.
Report
You receive prioritized findings and a clear next step.
Why teams work with us
Practical and technical
Findings are tied to the system and business context we examined, not generic advice.
A defined process
Scope, fees and access are agreed before work begins. You receive ranked findings afterwards.
Honest about limits
We help you reduce and manage risk. We do not claim to remove it.
Who we help
- Companies using AI chatbots
- SaaS businesses with AI features
- Operations teams using AI automation
- Industrial and factory businesses exploring AI
- Teams that want better visibility into how their AI behaves
A clear process, from scope to next steps
You know what will be reviewed and what the work will produce.
1. Define the scope
We discuss your system, the problem and the evidence available. Scope, fees and access are agreed before work begins.
2. Review and test
We examine agreed journeys or operating conditions, document failures and assess their likely business impact.
3. Decide what to improve
You receive prioritized findings and recommended next steps. Implementation and ongoing monitoring are scoped separately.
Know who you are talking to
RUBIKSCORP is based in Pune, Maharashtra. Meet the founders behind the company.
Shreyas Paygude
Founder & CEO, RUBIKSCORP. Also Co-founder of Vervaine.
View Shreyas’s published profile ↗Practical reading
Understand common problems before deciding what help you need.
Why prompt injection breaks chatbots and agents
Models cannot reliably tell your instructions apart from text a user or a web page supplies.
Read: Why prompt injection breaks chatbots and agentsWhere AI actually helps in a factory
AI works best on narrow, measurable problems: counting output, spotting visible defects, watching machine behaviour or showing where a line is stuck.
Read: Where AI actually helps in a factoryReady to understand how your AI is performing?
Request an audit, or register a specific problem. We review it and reply by email; scope and fees are agreed before any work starts.