Ridhika Dhani works with founders, CFOs, and technology leaders on strategic FP&A, AI and R&D economics, and capital allocation as companies move from Seed through scale.
The practice is intentionally narrow. Ridhika is not trying to replace accounting, tax, or bookkeeping. She is brought in when the financial question becomes a business decision.
Driver-based planning, operating models, forecasting, board metrics, scenario analysis, and the management cadence behind them.
Engineering, cloud, compute, data, product, and technical investment economics for companies where R&D is one of the biggest uses of capital.
Hiring, runway, GTM investment, infrastructure, CAPEX/OPEX, fundraising, and decisions about where the next dollar should go.
Ridhika has operated inside public companies, AI platforms, cloud software, infrastructure, and early-stage businesses. The value to a startup is simple: she has already solved harder versions of the decisions founders and CFOs face as the company scales.
Led forecasting, annual planning, long-range planning, executive reporting, and driver-based financial models across complex public-company environments.
Managed financial planning across engineering, product, cloud, vendors, and a $150M+ R&D portfolio.
Led CAPEX, OPEX, headcount, and cash-governance decisions that contributed to $500M+ in savings and deferrals.
Implemented and operated planning systems including Anaplan and other enterprise finance tools, improving forecast accuracy and reducing manual reporting.
Built board packages, KPI structures, long-range plans, and investor-facing financial narratives across growth-stage and public-company settings.
Built finance and FP&A functions from the ground up, including planning cadence, controls, reporting infrastructure, equity administration, and finance team design.
Cost savings and deferrals through CAPEX controls, headcount governance, and operating discipline.
R&D portfolio oversight across engineering, product, vendors, and cloud infrastructure.
Improvement in forecast accuracy through driver-based planning and finance systems.
Reduction in engineering costs through resourcing and operating-model changes.
A closer look at the situations behind the numbers: what the business was facing, what was built, and what changed as a result.
Built CAPEX, OPEX, headcount, and forecasting discipline that contributed to more than $500M in savings and deferrals while improving operating visibility.
Connected engineering resources, cloud spend, vendors, and product investment to financial planning across a $150M+ R&D portfolio.
Built planning, pricing support, fundraising materials, controls, equity administration, and 409A readiness for an early-stage AI company.
The trigger is not “we need more finance.” The trigger is that a capital, hiring, product, or growth decision is now large enough that getting it wrong materially changes the company.
What does that do to runway, output, and the next financing milestone?
What happens to gross margin and unit economics as usage grows?
Is the company ready to scale sales and marketing, or is the model not proven yet?
Are the forecast, KPIs, board narrative, and diligence materials investor-ready?
Where can capital come out without damaging the product or growth engine?
Can leadership explain the operating model, assumptions, and tradeoffs with confidence?
Engagements are scoped around the company’s stage, operating complexity, and the decisions leadership needs to make.
Build or reset the forecast, KPI structure, hiring logic, operating cadence, decision framework, and finance roadmap.
Executive-level support across operating plans, capital allocation, board preparation, scenario planning, and finance leadership.
Structured analysis around one consequential decision: hiring, compute, GTM expansion, fundraising, runway, or major resource allocation.
Pre-seed does not need an enterprise finance team. Series A does not need founder-led finance. The work changes by stage, and the finance model should change with it.
Keep finance simple, accurate, and useful enough to support the next financing milestone.
Connect the company’s plan to burn, hiring, unit economics, and the metrics investors will pressure-test.
Finance starts becoming an operating function, not just a reporting function.
Add functional depth without losing the operating discipline established earlier.
Ridhika’s focus is practical: improve planning, forecasting, reporting, and decision workflows through stronger systems, automation, and selective use of AI where it improves speed, visibility, or operating leverage.
Move from static planning cycles to driver-based models, more responsive scenario analysis, and better executive visibility.
Reduce manual reporting and improve how finance turns operating data into decision-ready information.
Clarify which work should be automated, which requires human judgment, and how finance teams should be structured as the company scales.
Experience matters, but so does judgment. These are the principles behind how Ridhika approaches finance inside technology companies.
AI companies should manage compute with the same rigor traditional companies apply to headcount.
The hiring plan is a capital-allocation model, not an HR spreadsheet.
A forecast that finance owns alone is already broken.
AI should eliminate finance work, not financial judgment.
Ridhika Dhani has 15+ years of experience building and leading finance functions, owning planning processes, partnering with executives, and guiding capital allocation across technology companies.
Her experience includes Meta Superintelligence Labs, Apple, KAMET.AI, Lucid Motors, LivePerson AI, Citrix, Conga, GlobalFoundries, Bain & Company, and Yahoo. She earned her B.S. in Business Administration from UC Berkeley’s Haas School of Business.
Share the capital, hiring, R&D, fundraising, or operating question your team is working through.