Datasutram Data Sutram (legal name Extrapolate Advisors Private Limited) is a Mumbai-based B2B data and location-intelligence company that supplies analytics APIs to banks, NBFCs, fintechs and consumer brands.

This Startup Deep Dive explains what Datasutram does, how it makes money, who backs it, how it compares with rivals and what could go wrong, using publicly available information. It is educational and not investment advice. Last updated: October 2026.

Datasutram in Brief

• What it is: Datasutram is an Indian Location intelligence / fraud analytics company based in Mumbai.
• Who it serves: businesses, institutions, consumers, learners and public-sector buyers.
• Converts raw unstructured data from satellites, mobile phones and point-of-sale machines into location and behavioural intelligence using proprietary AI and ML.
• Product suite is called Find, Authenticate and Grow, covering customer acquisition, fraud detection and collections.
• Serves mainly BFSI and consumer goods sectors.
• Funding: About $14.1 to $14.4 million (reports differ).

Datasutram Quick Facts

Figures marked “reported” come from published reports and may differ between sources; “Not disclosed” means no reliable public figure was found.

What Is Datasutram?

Datasutram operates in the Location intelligence / fraud analytics space in India. Understanding a company like this starts with three questions: who is the customer, what painful problem is being solved, and why the solution is better than the alternatives. The sections below apply that lens to Datasutram.

• Converts raw unstructured data from satellites, mobile phones and point-of-sale machines into location and behavioural intelligence using proprietary AI and ML.
• Product suite is called Find, Authenticate and Grow, covering customer acquisition, fraud detection and collections.
• Serves mainly BFSI and consumer goods sectors.
• Claims insights at pincode or grid level up to 150 m granularity and claims results such as 3X sales and 45 percent fewer fraud cases (company assertions).

The Origin Story of Datasutram

Every company starts from a noticed gap and a team willing to commit to closing it. Strong origin stories are specific: a clear problem, a founding team with relevant skills, and an early product that real customers could test. Founder-market fit, meaning the team’s lived experience or technical depth in the domain, tends to predict how fast a young company learns.

• Founders: Rajit Bhattacharya, Aisik Paul, Ankit Das.
• Started around December 2018 by three Jadavpur University batchmates: Rajit Bhattacharya, Aisik Paul and Ankit Das; a directory lists six founders (reports differ).
• Began by offering data solutions and has since specialised in fraud detection services.
• Headquarters is Mumbai; some directories list Kolkata (reports differ).

Key Milestones and Challenges

No startup grows in a straight line. Early years usually involve slow adoption, product rewrites, hiring mistakes and funding gaps. In the Location intelligence / fraud analytics sector the common turning points are a first paying customer, a regulatory approval, a successful product demonstration or a funding round that lets the team invest ahead of demand. How a team responds to setbacks says more about resilience than any single headline.

• December 2018: company founded.
• 2019: pre-seed round reported.
• February 2022: raised $2.07 million led by Varanium Capital and Yatra Angel Network.
• September 2023: raised about $3 million (lead reported as Singularity Growth Fund or Bharat Innovation Fund; reports differ).
• FY24: revenue about Rs 6 crore and loss Rs 10.8 crore.
• May 2025: $9 million Series A led by B Capital.

Datasutram Funding and Investors

Funding history shows how much outside confidence a company has attracted and how long it can operate before needing more capital. Figures below are reported and may differ between sources; valuation figures are estimates unless the company has confirmed them.

• Pre-seed, December 2019 (amount not stated in sources read).
• February 2022: $2.07 million led by Varanium Capital and Yatra Angel Network.
• September 2023: about $3 million, lead differs by report.

How Does Datasutram Make Money? (Business Model)

A business model describes who pays, how often and how much it costs to serve them. Strong models show repeat usage, improving margins with scale and acquisition channels that do not depend only on paid spend. Weak models show one-off sales, rising costs and growth that stops when spending stops. For companies selling to businesses, institutions, consumers, learners and public-sector buyers, sales cycles and contract size matter as much as headline growth.

• B2B SaaS and API sales to banks, lenders, NBFCs, fintechs and retailers.
• Products cover customer targeting, onboarding, fraud checks and collections.

The Numbers: Datasutram Financials

Financial figures show whether growth is turning into a self-sustaining business. Small and private companies often disclose little, so any number below is included only if it was found in public reporting.

• FY23: revenue about Rs 4.8 crore.
• FY24: revenue about Rs 6.0 crore (another report says Rs 5.6 crore, reports differ); total expenses about Rs 20.4 crore; loss about Rs 10.8 crore.
• Employees about 103.

Market Opportunity for Datasutram

Market size is easy to exaggerate, so a useful approach separates the total market from the portion a company can realistically reach with its current product and distribution. The main forces behind demand in this sector are rising digital adoption, cloud and mobile penetration, growing enterprise technology spending, government digitisation programmes and a deep pool of engineering talent. Each of these widens the opportunity, but capital efficiency and execution decide who captures it.

• Lenders and consumer brands increasingly use alternative and location data for credit and fraud decisions.
• Primary customers are BFSI and consumer goods companies.

Technology and Product at Datasutram

Technology is rarely the whole moat, but it drives speed, cost and customer experience. A practical way to assess it is to separate the capability the company controls from the commodity infrastructure it rents. In this sector the important factors are product quality, data and integration depth, security, scalability and the speed of shipping improvements.

• Proprietary AI and ML data engine processing satellite, mobile and POS data into geospatial scores.

Team and Culture at Datasutram

Teams are the most important and least measurable asset of a young company. Look at how complementary the founders’ skills are, how deep the second layer of leadership is, and how the company hires and retains specialists. Culture shows up in small repeated behaviours: how quickly bad news travels upward and whether commitments to customers are kept.

• Rajit Bhattacharya is CEO.
• Roughly 100 employees.

Datasutram Competitors: Competitive Landscape

A fair competitive analysis names direct rivals, adjacent players who could move into the space, and the customer’s default alternative. Durable advantages include proprietary data or technology, licences and approvals, brand trust, switching costs and cost leadership.

Risks and Challenges for Datasutram

• Large losses relative to revenue.
• Founder lists and funding details differ across sources.
• Customer concentration in BFSI.
• Competitor sets in sources overlap little, indicating a fragmented field.
• Competition: low barriers to entry and well-funded rivals can compress pricing and slow growth.
• Funding risk: technology businesses often burn cash before reaching profit and depend on investor appetite.

Risk is not a reason to avoid a company; it is a reason to price it correctly and to monitor the right indicators over time. Regulation in this sector is shaped by data protection rules under the Digital Personal Data Protection Act, sector regulators, consumer protection law and IT rules.

Datasutram Growth Outlook: What to Watch

• Series A capital supports expansion of fraud and acquisition products.
• Growth depends on narrowing losses as revenue scales.
• Key indicators: recurring revenue, customer retention, gross margin, customer acquisition cost and runway.
• Funding position: Whether Datasutram can reach its next milestone before needing new capital.
• Regulation: Changes from data protection rules under the Digital Personal Data Protection Act, sector regulators, consumer protection law and IT rules.

Industry Context: Where Datasutram Fits

India's technology startup ecosystem is one of the largest in the world, supported by cheap data, a large base of smartphone users, digital public infrastructure and a deep engineering talent pool. Companies now build for education, software, mobility, logistics, agriculture, defence, media and more, selling both to Indian customers and abroad.

Durable technology companies usually combine a clear customer problem with repeatable distribution and disciplined unit economics. Growth funded only by spending tends to fade, while products that customers keep using and paying for build compounding advantages.

For readers new to this field, a useful habit is to separate announcements from evidence. A plan, a partnership or a funding headline describes intent, while a delivered product, a paying customer or an audited financial statement describes results. Comparing the two over time shows how reliably a company turns promises into outcomes.

Bull Case and Bear Case for Datasutram

An investment thesis is a short, testable statement of why a company could become far more valuable, together with what evidence would prove it wrong. The points below are general to this sector and are not forecasts for Datasutram.

Bull case

• A large and growing base of digital users and businesses expands the addressable market.
• Recurring revenue and strong retention can compound growth.
• Selling into global markets widens the opportunity beyond India.

Bear case

• Heavy competition can compress pricing and margins.
• Dependence on external funding can force painful cost cuts.
• Regulation or platform changes can disrupt a product quickly.

Lessons Founders and Investors Can Take from Datasutram

Studying a company in the Location intelligence / fraud analytics space offers lessons that go beyond any single firm. The points below are general lessons for the sector and are not claims about Datasutram.

• Solving a clear, frequent customer problem matters more than adding features.
• Retention and repeat usage are better health signals than sign-ups.
• Disciplined spending extends runway and keeps options open when funding tightens.
• Distribution, not just product quality, decides who wins in crowded markets.
• Building trust through data protection and reliability is part of the product.

How to Evaluate Datasutram as a Startup

• Customer clarity: Can Datasutram name its customer and the problem it solves in one sentence?
• Evidence of demand: Look for repeat customers, signed contracts or measurable usage rather than announcements alone.
• Economics: Check whether margins improve as volume grows.
• Execution: Compare announced timelines with what has actually been delivered.
• Funding runway: Estimate how long current capital lasts at the present spending rate.
• Regulatory standing: Confirm required approvals are in place.
• Customer concentration: Check whether a few customers account for most of the revenue.
• Management depth: Look for experienced leaders beyond the founders and a stable core team.

The Takeaway

Datasutram is part of India’s growing Location intelligence / fraud analytics ecosystem. The useful lesson from studying it is the discipline of asking structured questions about customers, economics, advantage and resilience. Companies that answer them well, repeatedly and under pressure are the ones that endure. Readers should treat any single report, ranking or funding headline as one input among many, and should re-check key facts against recent company announcements. Because public information on young companies is often limited, patience in verifying claims is part of good analysis. Public sources can also describe the same company in different ways, so it is worth checking dates, definitions and whether a figure refers to revenue, total income or transaction value, and whether it covers one entity or a wider group, before drawing conclusions. Small differences in rounding, currency conversion or reporting periods can also change how a figure reads. Comparing the same figure across several recent reports helps show whether it is stable or has changed over time. This article is educational and is not investment advice.

• Clear customers and repeat demand matter more than announcements.
• Regulation and capital needs shape how fast a startup in this sector can grow.
• Track a few leading indicators over time rather than one headline metric.

Datasutram FAQs: Frequently Asked Questions

What is Datasutram?

Data Sutram (legal name Extrapolate Advisors Private Limited) is a Mumbai-based B2B data and location-intelligence company that supplies analytics APIs to banks, NBFCs, fintechs and consumer brands. It is profiled in the Startup Deep Dive series, which explains business model, funding, competitors and risks.

Who founded Datasutram?

Datasutram was founded by Rajit Bhattacharya, Aisik Paul, Ankit Das, and the company reports a founding year of 2018.

How much funding has Datasutram raised?

About $14.1 to $14.4 million (reports differ). Funding figures are reported and may differ between sources, so readers should check the latest company announcements.

How does Datasutram make money?

Datasutram earns revenue from its Location intelligence / fraud analytics products and services sold to businesses, institutions, consumers, learners and public-sector buyers. The exact revenue mix is described in the business model section and is not fully disclosed publicly.

Who are the competitors of Datasutram?

Reported rivals include Periculum, Goals101, DataSeers. See the competitor comparison above for how they differ.

What are the main risks for Datasutram?

Main risks include competition: low barriers to entry and well-funded rivals can compress pricing and slow growth, funding and capital needs, competition and regulation by data protection rules under the Digital Personal Data Protection Act, sector regulators, consumer protection law and IT rules. This is educational content, not investment advice.

Related Startup Deep Dives: EVage — Raised $28 million (Rs 207 crore) seed round led by RedBlue Capital in January 2022 | Bgauss — Raised Rs 110 crore in a Series D in August 2026, led by founder Hemant Kabra with Rs 50 crore. Browse the full Startup Deep Dive series.

Source

Source: Publicly available data, published reports, industry research, and other credible sources.