Start-up Outperformers 2026: Evaluating Series B & C Start-Ups on Funding, Valuation and Operations

As India’s start-up ecosystem continues to grow rapidly, clear benchmarks are essential to understand where real progress is being made. The study aims to provide this clarity through a structured evaluation that looks at multiple aspects of performance. This is where analytical hierarchy process (AHP) has its use case to compare performance across multiple parameters, allowing meaningful comparison across start-ups, states and cities.

Start-ups are evaluated across three main areas: financial performance, funding and valuation, and operational capabilities. The study focuses entirely on Series B and Series C start-ups. For states and Union Territories, the evaluation adopts a broader lens to capture the larger business environment that enables start-up growth.

Performance is assessed across six key areas: funding and investment, macroeconomic scenario, human capital and knowledge edge, digital evolution, business, safety and legal/regulatory environment, and sustainability and inclusion.

Cities, on the other hand, are assessed through a targeted set of sub-parameters designed to capture the unique characteristics of urban start-up ecosystems.

A Focused Approach

Instead of a broad assessment across all start-up lifecycles, this year’s framework focuses specifically on Series B and Series C companies. This allows for a more precise analysis of the growth phase of entrepreneurship. Early-stage ventures are often in a state of constant flux, where business models are still evolving and performance data is scarce.

On the other end of the spectrum, companies nearing an initial public offering operate under heavy regulatory oversight and massive scale. Trying to compare a 'day one' start-up to a global giant often creates more noise than insight. We have chosen to focus on the middle ground to ensure our findings are grounded in reality and easy to compare.

This stage marks a distinct shift toward financial and operational maturity. As revenue streams stabilise, the unpredictability of early experimentation gives way to more reliable income models. This stability allows the scrappy workflows of the past to be replaced by structured execution, professionalised operations, and high-performance teams.

Consequently, the strategic focus turns towards capital efficiency and managing funds with greater discipline while expanding into new territories.

By narrowing our lens to these growth-stage companies, the study provides a fairer, data-driven comparison based on financial health and operational discipline. This approach highlights the start-ups that are not just innovative but are building the foundations for long-term endurance, offering clearer insights for the investors and policymakers in tracking India’s next generation of scalable businesses.

The Methodology

When designing this study, we didn't want to rely on a one-size-fits-all scoring system. We initially explored using a random forest regression model, a sophisticated machine-learning approach that lets the data autonomously decide which factors are most important. However, we quickly hit a practical roadblock, which was the data volume. Machine-learning models like random forest thrive on massive datasets. While India’s start-up ecosystem is booming, the number of companies reaching Series B and C is an elite, naturally smaller group. We simply didn't have the thousands of data points required to train a machine-learning model without it becoming biased or inaccurate.

As we were working with a concentrated group of high-growth companies, we moved to the analytical hierarchy process (AHP) which is different than any cold algorithm. AHP recognises that in the real world, not all metrics are created equal, there is quantitative and qualitative data. For example, at the Series B stage, a company’s operational maturity might be a much stronger indicator of success than a single spike in funding.

AHP allows us to weigh what matters so we can assign more importance to the factors that truly drive growth. It combines data with expertise; it blends hard financial numbers with the seasoned judgement of industry experts. It also creates a fairer benchmark as it ensures that our final scores reflect the nuanced reality of how these businesses operate, rather than just reducing them to a single set of numbers. By choosing this process, we have ensured that the study is grounded in the actual dynamics of the Indian market, providing a realistic look at the start-ups building for the long term.

Recognising Limits

No methodology is perfect, and intellectual honesty requires us to acknowledge where even a robust framework like AHP has its limits. While it is an excellent tool for organising complex ideas, it does rely on expert judgement to decide which factors matter most. This means a small degree of subjectivity is naturally woven into the process, as different experts may prioritise certain trends differently.

Furthermore, the model is sensitive to how data is prepared. When we combine financial numbers with perception-based scores, we must be incredibly careful about how we scale that information to ensure that the final results are not skewed.

We addressed these challenges through a rigorous validation process and by keeping our weighting rules strict and consistent. By being transparent about these boundaries, we are not weakening the study; instead, we are giving investors, policymakers and founders the honest context, they can use these findings as a reliable compass for the future.

(Singh is associate research manager and Das Purkayastha is market research analyst, Ayvole)