What does Prima Partners do?

Prima Partners is the advisory practice of Prasanna Walimbe. It operates in three modes: AI & IT strategy advisory for CXOs, corporate AI enablement and delivery, and angel investment plus mentoring for early-stage founders in fintech, supply chain and applied AI.

Who is Prasanna Walimbe?

Prasanna Walimbe is a deep-tech operator with 20+ years of experience across AI evaluation and safety, AI/ML architecture, and financial research. He is an alumnus of IIT Kharagpur and IIM Calcutta and a Certified Independent Director advising boards on cross-border AI investments, operations and compliance.

Which sectors does Prima Partners focus on?

Fintech and BFSI, supply chain, manufacturing, and applied AI across enterprise use cases. Assurance work is mapped to the EU AI Act, ISO 42001 and the NIST AI Risk Management Framework.

How do I engage Prima Partners?

Email pw@prima-partners.com to start a conversation. Engagements run globally on a remote-first basis from Dubai.

What is AI governance consulting and what frameworks do you use?

AI governance consulting maps an organisation's AI systems, decisions and controls to a recognised risk framework — typically the EU AI Act, ISO 42001 and the NIST AI Risk Management Framework — and closes the gaps. Prima Partners runs checklist-based evaluations, model-risk reviews and board-level assurance for financial services, supply chain and applied-AI use cases.

Do you serve boards as an Independent Director?

Yes. Prasanna Walimbe is a Certified Independent Director and advises boards on AI strategy, cross-border investment and operations, and compliance obligations across jurisdictions — with a specialisation in fintech, supply chain and applied AI.

Do you offer corporate AI training and enablement?

Yes. Corporate AI enablement includes executive workshops, leadership AI literacy, hands-on programmes for delivery teams, and practical playbooks — AI realism vs hype, ML deployment, GTM for AI products, and the founder's mindset inside corporates.

Do you mentor or invest in startups?

Prima Partners writes angel cheques and provides hands-on mentoring to pre-seed and seed founders in fintech, supply chain and applied AI. Support covers technical architecture, GTM, commercial modelling and board readiness.

Which AI model should an enterprise standardise on?

The Stanford 2026 AI Index shows the performance gap between top models has collapsed, so standardising is now a switching-cost and security decision rather than a capability one. Reframe long-running evaluations around integration risk, data-egress controls and exit paths instead of another round of benchmarks.

Should companies slow junior hiring because of AI?

The Index's data on entry-level task exposure argues for redesigning the junior role, not deleting it. Cutting the pipeline solves an immediate headcount problem but removes the bench that becomes your senior engineers and managers by 2028.

Where do AI agents actually belong on the enterprise roadmap?

The Index's agent-benchmark results locate a narrow band of workflows where agentic systems clear the reliability bar today — mostly bounded, tool-using tasks with human review. Broader deployments across open-ended enterprise workflows are a 2027 conversation and should be scoped as such.

Can enterprises trust reported AI productivity gains?

The Index places self-reported productivity uplift directly beside realised-output figures and the two diverge sharply. Boards should measure cycle time, defect rate and unit economics on specific workflows rather than treat satisfaction survey scores as evidence of P&L impact.

Who should sign off on enterprise AI governance?

The Index's transparency scores show foundation-model vendors are disclosing less each year, so control cannot be inherited from the vendor stack. Governance sign-off belongs with an internal owner running a checklist mapped to the EU AI Act, ISO 42001 and the NIST AI Risk Management Framework.

AI Can Grade a Million Essays Overnight. That Doesn't Fix Testing.

2026-06-23 · AI Evaluation & Safety · 8 min read

Scale is the easy problem. Construct validity is the one no smarter model will fix.

The analysis

Automated grading solves a genuine constraint. Marking at scale is slow, expensive and inconsistent between human raters, and a model that produces defensible scores overnight removes a real bottleneck from any large assessment system.

What it does not solve is whether the thing being scored is the thing you care about. Construct validity — the question of whether the test measures the capability it claims to measure — is a property of the assessment design, not of the marker. Automating the marker faster does not make an essay prompt a better measure of reasoning; it makes a possibly poor measure cheap enough to apply universally.

The second failure mode is reflexive. Once scoring is automated and its behaviour is stable, teaching adapts to it. Students and institutions optimise for what the grader rewards, which is observable surface structure far more reliably than it is insight. The measure becomes the target and its usefulness as a measure decays — an old dynamic, but one that automation accelerates because the feedback loop is now instant and universal rather than slow and local.

The recommendation for anyone deploying this in education or in corporate certification is to invest the savings from automation back into assessment design and periodic human audit of a sampled subset, rather than banking the whole saving. Otherwise the organisation buys speed and quietly loses the ability to know what its scores mean.

Full essay on Substack: AI Can Grade a Million Essays Overnight. That Doesn't Fix Testing..

More on AI Evaluation & Safety

Discuss this with Prasanna: pw@prima-partners.com · book a 30-minute call.