Crayon Data is a Singapore-headquartered big data and AI company with a development centre in Chennai, India, whose maya.ai platform delivers data, recommendations and marketplaces to banks, fintechs and travel firms.

This Startup Deep Dive explains what Crayon Data 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.

Crayon Data in Brief

• What it is: Crayon Data is an Indian Enterprise AI and recommendation platforms company based in Singapore.
• Who it serves: businesses, institutions, consumers, learners and public-sector buyers.
• Sells maya.ai, an enterprise platform with modules for data, recommendations, customer experience and marketplaces.
• Typical deployments include marketplaces, digital wallets and travel concierge services.
• Targets banks, fintechs and travel companies in Asia Pacific and the Middle East.
• Funding: Not disclosed in public sources reviewed.

Crayon Data 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 Crayon Data?

Crayon Data operates in the Enterprise AI and recommendation platforms 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 Crayon Data.

• Sells maya.ai, an enterprise platform with modules for data, recommendations, customer experience and marketplaces.
• Typical deployments include marketplaces, digital wallets and travel concierge services.
• Targets banks, fintechs and travel companies in Asia Pacific and the Middle East.
• Company claims over 127 million customers onboarded (self-reported).

The Origin Story of Crayon Data

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: Suresh Shankar, Srikant Sastri.
• Founded in 2012 by Suresh Shankar, a former IBM executive, and Srikant Sastri, a former VivaKi Chairperson; other sources add further names, so founder lists differ.
• Early focus was big data analytics mining insights from consumer data across public and enterprise sources.
• Headquarters reported as Singapore (most sources) with Chennai also cited as a base.

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 Enterprise AI and recommendation platforms 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.

• 2012: company founded.
• Feb 2014: raised Rs 7.3 crore (about $1.2 million) from Jungle Ventures and Spring Seeds Capital for overseas expansion.
• Dec 2017: Series A listed at about $6.4 million.
• 2018: reported to have turned EBITDA positive.
• FY23: revenue Rs 52.6 crore, up from Rs 42.5 crore.

Crayon Data 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.

• Before 2014: about Rs 27 crore from angels including Meng Wong, William Bissell, Ernest Pomerantz and Ravi Thakran.
• Feb 2014: Rs 7.3 crore, Jungle Ventures and Spring Seeds Capital.
• Dec 2017: Series A about $6.4 million, NSI Ventures among investors (aggregator).
• Later: a Series B planned at about $11 million backed by Ratan Tata and Kris Gopalakrishnan family offices; 2021 amounts conflict across sources.

How Does Crayon Data 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.

• Enterprise software with setup fee plus consulting and integration services (user-review site).
• Platform deployments for banks and travel firms.
• Revenue mostly from corporate clients in Asia Pacific and the Middle East.

The Numbers: Crayon Data 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.

• FY22: revenue Rs 42.5 crore (Crayon Data India entity).
• FY23: revenue Rs 52.6 crore, total expenses Rs 51.9 crore, profit after tax Rs 33.0 lakh, estimated EBITDA Rs 99 lakh (Indian entity, aggregator).

Market Opportunity for Crayon Data

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.

• Personalisation and AI marketplaces for financial services and travel.

Technology and Product at Crayon Data

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.

• maya.ai platform combining data, recommendation engine and customer experience tools.

Team and Culture at Crayon Data

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.

• Suresh Shankar and Srikant Sastri are named as founders in an early funding report.

Crayon Data 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 Crayon Data

• Thin profit margins in FY23 (about 0.6%).
• Founder, funding and headquarters data conflict across sources.
• Competition from global personalisation vendors.
• No FY24 or FY25 figures found.
• 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.

Crayon Data Growth Outlook: What to Watch

• No recent public outlook found; FY24 and FY25 results not located.
• Key indicators: recurring revenue, customer retention, gross margin, customer acquisition cost and runway.
• Funding position: Whether Crayon Data 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 Crayon Data 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 Crayon Data

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 Crayon Data.

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 Crayon Data

Studying a company in the Enterprise AI and recommendation platforms space offers lessons that go beyond any single firm. The points below are general lessons for the sector and are not claims about Crayon Data.

• 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 Crayon Data as a Startup

• Customer clarity: Can Crayon Data 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

Crayon Data is part of India’s growing Enterprise AI and recommendation platforms 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.

Crayon Data FAQs: Frequently Asked Questions

What is Crayon Data?

Crayon Data is a Singapore-headquartered big data and AI company with a development centre in Chennai, India, whose maya.ai platform delivers data, recommendations and marketplaces to banks, fintechs and travel firms. It is profiled in the Startup Deep Dive series, which explains business model, funding, competitors and risks.

Who founded Crayon Data?

Crayon Data was founded by Suresh Shankar, Srikant Sastri, and the company reports a founding year of 2012.

How much funding has Crayon Data raised?

No reliable total funding figure is disclosed in the public sources reviewed. Funding figures are reported and may differ between sources, so readers should check the latest company announcements.

How does Crayon Data make money?

Crayon Data earns revenue from its Enterprise AI and recommendation platforms 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 Crayon Data?

Reported rivals include Coveo, Dynamic Yield, Qubit, Perx Technologies. See the competitor comparison above for how they differ.

What are the main risks for Crayon Data?

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.

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Source

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