Selected work

We work on problems that sit between strategy, capital and execution.

Sundarbans works with ambitious companies at the moments when the usual playbook stops working — raising capital, entering a new market, transforming a business, choosing a growth path, or making sense of a financial and competitive landscape that resists a simple read.

Five examples follow. Clients are not named: this work is commercially sensitive and confidentiality is part of what we sell. What we can show is the shape of the problem and the shape of the thinking.

Education fintech

Building the financial operating system for education

Reframing a payments business as infrastructure.

The challenge

The company was assembling an integrated financial infrastructure layer for education — payments, reconciliation and embedded finance in one stack. The opportunity was genuinely large.

The problem was that it did not sound large. Described feature by feature it read as a payments business with some add-ons, and payments businesses are valued as payments businesses.

What we did

Refined the positioning and the category narrative
Built the market thesis for education payments
Developed market sizing and growth scenarios
Structured the embedded-finance opportunity
Built the fundraising narrative and the investor deck
Developed financial and operating projections
Quantified the opportunity across institutions, parents and financing
Structured the use of funds and the capital-raising story

The result

A fragmented fintech proposition was reframed as an infrastructure opportunity: a financial operating system for education. The three things investors underwrite — the size of the market, the traction already on the books, and the financial infrastructure the company could become — were connected into a single thesis rather than three separate claims.

Strategy
Finance
Fundraising
Market intelligence

AI commerce infrastructure

Turning product capability into a market story

What should a company that can do many things be known for?

The challenge

The company had built AI infrastructure for e-commerce: virtual try-on, digital twins, 3D models, automated product imagery, generated video. The technology could do a great many things.

That was the problem. A company that can do many things has to decide what it is for. Without that decision, every customer conversation starts from scratch and every investor conversation ends in a list.

What we did

Analysed the emerging AI commerce landscape
Mapped product capabilities against actual customer problems
Sharpened the positioning of the try-on and digital-twin proposition
Developed the broader product narrative
Worked on investor communication and fundraising material
Assessed competitive differentiation
Developed go-to-market and content directions
Translated the technology into something a commercial buyer could hold

The result

A collection of capabilities became one commercial proposition: automating the visual commerce stack. The work closed the gap that sinks most deep-tech companies — the distance between what the technology does and why a customer, and then an investor, should care.

AI strategy
Product positioning
Go-to-market
Fundraising

Listed industrial manufacturer

Looking past the financial statements

Reported numbers describe the past. We wanted what was available next.

The challenge

A specialised manufacturer of power cables and conductors. Understanding a business like this takes more than revenue, EBITDA and a P/E multiple — those describe what happened, not what the company can do next.

The real questions were about manufacturing and technological capability, competitive differentiation, geographic footprint, industry structure, management quality, and whether the market's valuation reflected any of it.

What we did

A full strategic and investment analysis covering:

Business and segment economics
Competitor benchmarking and peer comparison
Manufacturing and technological capability
Geographic footprint
Product differentiation
Management assessment
Industry structure
Growth opportunities
Valuation and relative positioning against listed peers

The result

A conventional company analysis became a strategic and investment framework — one that evaluates the business on its underlying competitive capabilities rather than only on what it has already reported. The difference matters most exactly when reported financials and underlying capability are diverging, which is usually when the decision is hardest.

Investment research
Competitive intelligence
Financial analysis
Strategy

Indian MSME credit

Finding the signal in a trillion-rupee market

Finding companies was never the difficulty.

The challenge

MSME lending in India spans public and private banks, NBFCs, fintech lenders and a long tail of smaller listed companies.

The difficulty was finding where economics, competitive position and valuation had come apart from the sector average — and doing it on a like-for-like basis, in a universe where no two lenders report quite the same way.

What we did

Mapped the MSME credit universe
Segmented companies by business model and scale
Benchmarked operating and financial performance
Compared valuation across the peer set
Analysed relative strength and differentiation
Examined asset quality and lending economics
Identified where valuation appeared dislocated from fundamentals
Built a framework for deeper company-level investigation

The result

A fragmented sector became a systematic research universe, with every company comparable on the same operating, financial and valuation dimensions — and a repeatable structure for the next question, rather than a snapshot that expires.

Sector research
Public markets
Financial analysis
Data intelligence

Battery manufacturing

Navigating an incumbent through a technology transition

“Should we enter lithium?” is not a decision-useful question.

The challenge

India's battery industry is moving from lead-acid to lithium-ion. For an established lead-acid manufacturer, “should we enter lithium?” is not really the question — the answer is obviously yes, eventually, and it is not decision-useful.

The decisions that matter are how much to invest, where to build capacity, which chemistry and which partners, and how fast to move without damaging the economics of a profitable existing business that is funding the transition.

What we did

Analysed the transition across:

Indian battery market structure
Lead-acid versus lithium-ion economics
Manufacturing capacity requirements
Technology choices and localisation
Competitive positioning
Strategic partnership options
Capacity-building scenarios
The approaches taken by the two listed market leaders
International technology partnerships
Transition risk
Strategic pathways available to an incumbent

The result

The question was reframed — from should we enter lithium to what is the optimal transition architecture for an incumbent manufacturer. That produced a framework for deciding capital allocation, technology, partnerships and pace together, rather than as four decisions taken in sequence by different people.

Corporate strategy
Industry intelligence
Technology strategy
Capital allocation

The common thread

These five projects span fintech, AI, industrials, financial services and energy. The subject matter has almost nothing in common. The problem does.

In each case a company was sitting on something complicated — a stack of capabilities, a sector with four hundred lenders in it, a technology shift with no obvious entry point — and needed it to become a decision. Not a summary. Not a set of options. A decision, with the reasoning visible enough that a board or an investor could follow it.

That is the work. Strategy, finance, research, AI, content, talent and network, brought together because the problem does not respect the boundaries between them. We don't just produce a report. We help founders and leadership teams understand the problem, find the opportunity, build the numbers, tell the story, and act on it.

Talk to us about your problem