Gurbani's Profound AI Rep
Gurbani builds structured analysis from incomplete data and owns it from brief to client delivery.
Gurbani Makkar
Edges
Valuation Research and Benchmarking
Gurbani builds financial analysis in situations where the standard comparable set does not exist. Her instinct is to go upstream: map the business, understand what drives its value, and construct a peer framework that can hold up under scrutiny. This pattern showed up consistently across her two years at KPMG, where she worked on more than 20 engagements spanning pharma, FMCG, banking, telecom, and technology. When direct comparables were unavailable, she turned to analyst reports, annual reports, and qualitative sources, translating them into quantifiable inputs. The pharma demerger engagement is the clearest example. With no segment-level data available and no direct listed peer, she built the entire competitor framework by mapping therapeutic categories, sourcing peers by product mix, and constructing the analysis from first principles. Her work spans business valuations, M&A benchmarking, IFRS impairment testing, purchase price allocations, and PE transactions across India, the Middle East, and the UK.
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End-to-End Project Ownership
Gurbani takes full ownership of complex, time-pressured work from brief to delivery. She does not hand off at the research stage; she carries the analysis through to the client conversation. This pattern is visible across both her roles. At KPMG, she was the sole analyst on the pharma demerger, owning all secondary research and delivering on a board-meeting deadline the team had initially flagged as at risk. At Bough, she owned two of three workstreams on the Fortune 500 Germany migration end to end, including the client presentation. The shift from Big Four to a lean consulting firm sharpened this further. Without the review layers of a large firm, she had to recalibrate her own quality bar and present directly to clients. A difficult first client call on the migration project accelerated that adjustment. Her accountability standard is self-imposed: she reviews her own work, catches her own errors, and owns the outcome.
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Qualitative Research Synthesis
Gurbani translates qualitative sources into structured, quantifiable outputs. When financial data is incomplete or incomparable, she goes to analyst reports, annual reports, and primary interviews, and builds the analytical layer from what she finds. At KPMG, this was a recurring requirement. Many of the companies she valued were too small or too specialized for standard benchmarking, so she quantified qualitative findings from industry reports and news coverage into financial inputs that could support a valuation. At Bough, she has applied the same instinct to primary research, running interviews with potential clients to map their needs and translate those findings into service design. The pattern is consistent: she does not stop at gathering qualitative information. She structures it into something a decision can be made from.
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AI-Augmented Financial Delivery
Gurbani integrates AI tooling into financial work to accelerate delivery without sacrificing accuracy. Her instinct is to identify where manual processes create bottlenecks and replace them with AI-assisted workflows. On the Fortune 500 Germany migration at Bough, she started with a manual approach and hit the timeline wall. She introduced Claude and built AI-assisted templates and dashboards that allowed the restatement work to move at a pace the manual process could not sustain. The migration was completed in under two months, against a multi-month industry benchmark. She is actively building this capability further, learning SQL and Python alongside her finance work to deepen her ability to work at the intersection of financial analysis and technology.
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