Senior counsel to enterprise operators, institutional investors, and frontier AI labs on the strategic questions that get asked once, and have to be right.
Quantumleap Insights was founded by Babith Bhoopalan, a former Microsoft senior leader with 20+ years at Microsoft leading Product, AI, and customer experience strategy and enablement. He now runs the firm as a fractional consultant and Microsoft Alum.
The firm's edge is simple: insider perspective paired with independent candor. Clients get the inside-the-room view of how enterprise AI actually gets built, deployed, and adopted, from someone free to say what current employees cannot.
Every engagement produces proprietary frameworks, not shelf-ware decks. Clients walk out with instruments they can use in their next board review, investor update, or customer conversation.
A slice of Q1 and Q2 2026 work with strategy firms, institutional investors, and frontier AI labs. Client identities are kept confidential by default; outcomes and frameworks are the deliverable.
Multi-phase engagement with a tier-one global strategy firm on an enterprise cloud ERP's value-added reseller market and distributor tier differentiation. Delivered a research brief, live advisory, and a market sizing correction.
Deep advisory session for a tier-one firm's IT cost practice, centered on productivity suite SKUs and bundling economics. Covered price-increase cycles, EA volume dynamics, AI add-on economics, and ghost license waste.
Investor advisory on AI inference economics: colocation vs hyperscaler positioning, GPU density retrofit challenges (40 to 100 kW per cabinet vs traditional 5 to 10 kW), workload migration patterns, and frontier compute spend trajectory.
Two-part engagement on MaaS platform economics across the major hyperscaler model marketplaces. Covered GPU deployment architecture, regional footprint, daily token volumes, gross margin structure by model family, and PTU discount architecture.
Month-long async expert engagement with a leading AI lab reviewing LLM outputs and prompts. Applied a product and strategy lens to behavior evaluation, prompt quality, bias and hallucination identification, and output usability for enterprise contexts.
Multi-session engagement establishing the three-category measurement framework (Adoption, Impact and Velocity, Quality) for AI-assisted engineering. Grounded in practitioner benchmarks: AI Code Contribution Rate, Acceptance Rate, Cycle Time Reduction, Escaped Defect Rate.
Every engagement, whether a one-hour expert call or a multi-month retained advisory, follows the same three-move pattern. The output is always something the client can hold, use, and defend.
Rapid, pointed assessment of the actual question. We separate the business problem from the technology framing and surface the one or two questions the client should be asking that they're not.
Build the framework, model, or scorecard that answers the real question. Grounded in proprietary IP where possible, named-source research otherwise. Every number is defensible.
Hand off the instrument plus a one-page summary the client can walk into a board meeting with. Follow-up access included; next engagement scoped only if it earns its keep.
Why the $321 billion question isn't whether you need CRM. Five clients, same worry. Pulling the data, stress-testing architectures, mapping what holds and what has to change.
A year of consulting with technology companies across Asia and Europe surfaced a gap: legal teams, ML engineers, and business leaders all working on AI patent strategy, rarely together. The software playbook will not work for ML.
A $300B AI-plugin move inside 48 hours proved out the thesis in "The AI Patent Blind Spot." Here is what IP, patent, and general counsel teams should actually do this quarter.
Development leaders must transform how teams work, not just which models they call. A practitioner view of why AI rollouts stall at the organizational layer, and what to change first.
Fastest path is a 30-minute discovery call. If you'd rather send context first, use the form. Direct inquiries from strategy firms, investors, enterprise buyers, and AI labs welcome.
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