Raise AI and Fuzz: What They Are, and an India-Focused Research Alternative

Fuzz, from Raise Financial Services, is one of the more serious India-focused AI products to arrive recently. It is an agentic AI model dedicated to finance and Indian markets, built to synthesise deep research from regulatory filings and official sources, give transparent and source-backed answers, and connect to a user’s own portfolio and tax data. If you are a professional investor weighing it against a research platform built around point-in-time history and backtesting, this piece explains what Fuzz is, where it is genuinely strong, and how Altys Labs approaches the same problem from a different angle.

To be clear up front, Fuzz and Altys share a lot of the same conviction: India-focused, grounded in filings and official financial sources, aimed at serious investors rather than a generic chat experience. The differences below are about focus and method, not about one being right and the other wrong.

What Fuzz is and who it serves

Fuzz (askfuzz.ai) is built by Raise AI Possibilities, part of Raise Financial Services, the company founded by Pravin Jadhav that also runs the broking platform Dhan. Raise reported a Series B round of roughly USD 120 million led by Hornbill Capital with participation from MUFG, at a reported valuation of around USD 1.2 billion, which puts real backing behind the product. Fuzz launched in the second half of 2025 and is positioned for serious investors and finance professionals who want India-specific analysis rather than a general-purpose assistant.

Based on its public materials, Fuzz is an agentic AI for finance: it continuously ingests market, news, company, and sector data, draws on regulatory filings and official financial sources, and aims to return answers that are transparent and traceable back to a source rather than generic summaries. It emphasises India data residency, with reporting that user data never leaves the country. It can also connect to a user’s portfolio and income-tax returns to produce more personalised insights. Fuzz has been reported to offer free access, with public materials also mentioning a complimentary tier alongside premium plans.

Fuzz sits inside a wider Raise ecosystem that is worth knowing for context: Dhan for broking, ScanX for research and analytics, Upsurge for learning, and Filter Coffee for content, and Raise has also acquired Stratzy. That gives Fuzz a distribution and data footprint that most standalone tools do not have.

Where Fuzz is genuinely strong

Fuzz is doing something we have a lot of respect for, and it deserves real credit on its own terms:

• India-first grounding. Building specifically for Indian markets, on Indian filings and official sources, is the right instinct. A tool trained and tuned for this market answers India questions better than a global model bolted onto Indian data.
• Source-backed answers. The commitment to transparent, traceable outputs rather than generic ones is exactly the discipline serious research needs. Being able to see where an answer came from is what separates a usable assistant from a plausible-sounding one.
• Agentic synthesis at speed. For reading across filings, news, and sector data and pulling a coherent picture together quickly, an agentic model is a genuinely powerful way to work, and it covers ground fast.
• Personalisation and ecosystem. Connecting to a user’s portfolio and tax data, backed by the broader Raise stack, lets Fuzz tailor its output in ways a standalone research tool cannot easily match.
• Data residency. Keeping data in the country is a real consideration for many Indian users and firms, and it is good to see it treated as a design choice.

If you want a fast, India-aware research assistant that reads the filings and shows its sources, Fuzz is a strong option and worth trying on its own terms. That shared conviction about India-deep, filings-grounded research is exactly why we put it alongside the other tools in our roundup of the best AI research tools for Indian stocks.

Where a professional research desk tends to want a different tool

None of the following is a knock on Fuzz. It is a different job. An agentic AI assistant and a point-in-time research platform are built for different parts of the workflow, and a desk running concentrated books often needs the second kind of tool for specific tasks.

Point-in-time history. Most tools, including AI assistants reading the latest filings, present today’s version of the past. For a research process or a backtest, what matters is what a company had actually reported and what was knowable on a given past date, before restatements and reclassifications. Judging a past decision with today’s tidied-up numbers is how lookahead bias creeps in, which is why point-in-time data matters for anyone testing a process.

Deterministic calculation versus synthesis. An agentic model reads and synthesises, which is excellent for understanding and speed. A separate need is a figure that is computed the same way every time from a company’s own filings, with forecasts produced by explicit statistical methods rather than model synthesis. Both approaches have a place. The difference is that a deterministic calculation gives the same number on every run and can be checked against the exact line it came from.

Figure-to-filing auditability. When a number feeds an investment-committee memo, an analyst usually needs to trace it to the exact filing, line, and date. Source-backed prose is a big step in that direction. A desk building a model on top often wants each figure in a table linked to its filing line, so the whole model can be audited cell by cell.

Backtesting and the full workflow. Answering questions is one job. Testing how a rule would have behaved across past cycles using only the data available at each step is another, and it needs a point-in-time backend and an explicit backtest. PMS firms, AIFs, and family offices tend to want screening, modelling, forecasting, and backtesting in one place tied to an auditable data layer. That is the shape of an institutional equity research workflow, and it sits alongside a research assistant rather than replacing it.

How Altys approaches the same problem

Altys Labs is an equity research and fundamental analysis platform for Indian stocks (NSE and BSE) and Indian mutual funds, built for professional users: PMS firms, AIFs, family offices, and MFDs. It is currently invite-only, in private preview. A few points on how it approaches the work, stated as focus rather than as any claim of superiority:

• India-first and India-deep. It brings India’s market data into one place: company filings, earnings-call (concall) transcripts, management guidance, shareholding patterns, 80-plus global and domestic macro series, FII and DII flows, factor scores, and mutual-fund data.
• Point-in-time by design. It keeps data as it stood on each past date, so research and backtests reflect what was actually knowable then rather than restated history.
• Source-linked and auditable. Every figure links back to its source filing, line, and date, so a model can be traced cell by cell.
• Calculated, not guessed. Numbers are computed from a company’s own filings, and forecasts come from explicit statistical methods rather than a language model producing a growth rate.
• Tools on top of the data. You can screen, build financial models, forecast, and backtest a strategy in one place.

Altys is not a broker, not a tip service, and not a SEBI-registered research analyst or adviser. It does not tell you what to buy or sell; it is software for doing your own research.

Which fits whom

	Fuzz (Raise)	Altys Labs
Best for	Serious investors and finance pros wanting fast, India-aware answers	PMS firms, AIFs, family offices, MFDs
Core approach	Agentic AI that synthesises from filings and official sources	Deterministic figures from filings plus statistical forecasts
India coverage	India-focused, India data residency	India-first (NSE/BSE plus Indian mutual funds)
Point-in-time history	Not a stated focus	Core design principle
Backtesting	Not a stated focus	Screen, model, forecast, backtest in one place
Auditability	Source-backed answers	Every figure linked to filing, line, date
Access	Reported free access, with premium plans mentioned	Invite-only private preview

The honest summary: Fuzz is a well-backed, India-focused AI assistant that reads the filings and shows its sources, and if you want fast, source-backed answers grounded in Indian markets, it is a strong choice worth using on its own terms. If you are running money professionally and the binding constraints are point-in-time history, figures calculated from filings and traceable to a line and date, and the ability to backtest a strategy on Indian equities and funds, that is the gap Altys is built to fill. Many desks will happily use both, an assistant for reading and a research platform for building and testing.

If you want to see how the different AI stacks in this space compare, our Tijori Stack alternative piece covers a sibling AI suite, and for the broader picture see our roundup of the best AI research tools for Indian stocks.