AlphaSense vs Rogo vs Altys: Which AI Research Platform Fits an Indian Investment Team?

AlphaSense, Rogo and Altys are not three versions of the same financial chatbot. AlphaSense is strongest as a broad global market-intelligence and content-search system. Rogo focuses on enterprise finance agents and finished work products. Altys focuses on India-first public-market research, point-in-time analysis, rule-based portfolio governance and continuous monitoring.

This distinction matters for Indian investment teams. A global platform can be excellent and still solve a different problem from the one consuming most of your analysts’ time.

Altys publishes this comparison and is one of the products discussed. Product descriptions reflect public information available on 4 September 2026. Buyers should confirm current coverage, integrations, security and commercial terms directly with each vendor.

The short answer

• Choose AlphaSense when the scarce resource is access to, and search across, a large global content universe that includes premium research, expert calls, filings, news and your firm’s internal knowledge.
• Evaluate Rogo when the bottleneck is turning connected financial and firm data into repeatable institutional work products such as company profiles, deal research, meeting preparation, Excel models and presentation material.
• Evaluate Altys when the core job is Indian listed-equity and fund research, with point-in-time financial history, deterministic factors and backtests, source-linked evidence, portfolio rules and continuous thesis monitoring.

A practical comparison

Dimension	AlphaSense	Rogo	Altys
Centre of gravity	Global market intelligence and content search	Enterprise AI agents for finance workflows	India-first investment research and portfolio governance
Publicly stated users	Financial, corporate, consulting and professional-services teams	Investment banks, private equity, asset managers and finance teams	Indian PMS, AIF, family-office and research teams
Content advantage	Premium research, expert transcripts, company documents, news, financial data and internal content	Connected external financial data plus firm data and precedents	Indian filings, concalls, financials, guidance, ownership, factors, macro and mutual funds
AI workflow	Generative Search, Deep Research, Generative Grid, summaries and monitoring	Finance agents and custom firm workflows	Cited company research, parallel research, scorecards, models and monitoring
Quantitative process	Financial data, screening, comparables, Excel integrations and models	Financial analysis and modelling within agent workflows	Deterministic factors, scorecards and point-in-time fundamental backtests
Typical output	Cited answer, research synthesis, dashboard or briefing	Research, Excel, PowerPoint and Word deliverables	Source-linked analysis, strategy record, scorecard, model, alert and committee-ready evidence
Geographic design	Global	Global enterprise finance	India-first public markets
Execution	Research and intelligence, not brokerage	Workflow software, not brokerage	Research software, no brokerage

AlphaSense: the content universe is the product

AlphaSense combines search, generative research, financial data, monitoring and enterprise knowledge across an unusually broad information universe. Its public platform materials describe hundreds of millions of premium documents, including company material, news, trade publications, broker research and expert-call transcripts, with the ability to connect a firm’s internal memos, meeting notes and files.

That breadth solves a real institutional problem. An analyst researching a global industry may need to connect a competitor’s filing, an expert’s explanation, sell-side estimates, a trade-publication article and an internal note written two years ago. The difficult part is not generating prose. It is finding the right evidence across a library no individual could read.

AlphaSense’s Generative Search returns cited answers across that material. Generative Grid applies repeatable questions across many documents, while monitoring keeps selected companies, competitors and themes current. Its financial-data layer also includes screening, comparables, transactions, valuation data, Excel integration and pre-built models.

For an Indian firm with a global mandate, cross-border diligence or a heavy need for premium third-party content, that is a powerful proposition. The evaluation question is not whether AlphaSense is capable. It is whether your hardest problem is global information discovery or India-specific portfolio research.

Read our AlphaSense in India guide for a deeper treatment of that boundary.

Rogo: the workflow and deliverable are the product

Rogo describes itself as an AI platform for finance. Its public product materials emphasize accurate, grounded research across internal and external data, finance-specific agents, firm customization, enterprise controls and work products created in the applications analysts already use, including Excel, PowerPoint and Word.

Its examples are revealing: earnings comparable analysis, public-company profiles, meeting preparation, sponsor overviews, news runs, pitch material and model work. These are recognizable jobs in investment banking, private equity and institutional finance. The value is not only retrieving a fact. It is moving a repeatable assignment closer to a finished deliverable in the firm’s own style.

Rogo also emphasizes institutional memory and firm-specific workflows. That matters because two firms with access to the same model and market data do not work identically. Templates, precedents, review standards and senior judgement are part of the operating system.

For an investment bank or private-market team, that last mile can be decisive. For an Indian public-equity desk, the buyer should ask how deeply the system handles the local facts and portfolio process that matter every day.

Our Rogo AI and Indian markets guide covers the fit in more detail.

Altys: the Indian research record is the product

Altys begins with the Indian public-market workflow rather than a global document library or deal-team deliverable.

The research record can connect:

• Indian company financials, filings, concalls and management guidance;
• company-specific operating indicators, ownership and forensic signals;
• factors, scorecards and screening rules;
• point-in-time data for reconstructing what was knowable on a past date;
• strategy tests, models and documented assumptions;
• portfolio exposure, limits, exceptions and investment-committee decisions;
• monitoring conditions that keep the original thesis current;
• later outcomes that can be compared with the original expectation.

The quantitative boundary is explicit. Code calculates financial ratios, factors, scores and backtests from identified inputs. AI helps read and organize qualitative evidence. Material claims should carry citations, and absent evidence should produce an unavailable state rather than an invented answer.

The work is also designed to be independently inspected. Important strategy, scorecard, research and alert outputs can be exported to Excel or Excel-ready files. Verification should not require faith in the platform that produced the result.

Altys does not execute trades and does not provide stock recommendations. It is research and governance infrastructure for teams that already own the investment judgement.

Where the products genuinely overlap

All three products recognize the same broad shift: a generic language model is not enough for serious financial work.

Each, in its own way, surrounds AI with specialized data, citations, workflows and institutional context. AlphaSense connects AI to premium external and internal content. Rogo connects it to finance-specific agents, firm data and deliverables. Altys connects it to Indian public-market data, quantitative rules, portfolio context and a point-in-time decision record.

That overlap is useful. It moves the buyer’s question beyond “does it have AI?” toward five harder tests:

1. What information can the system access?
2. Does a citation support the exact claim being made?
3. Are financial calculations generated in prose or computed from compatible inputs?
4. Can the analyst reproduce what the system knew on a past date?
5. Does the output enter the firm’s real workflow, or remain a disposable chat answer?

Three example buyers

A global research and strategy team

The team covers several countries and industries, buys premium research and wants internal knowledge searchable beside external content. AlphaSense is the most natural first evaluation.

An investment bank or private-equity team

The team repeatedly creates profiles, pitch books, comparable-company work, meeting preparation and models from firm and market data. Rogo is shaped around that output-heavy workflow.

An Indian public-markets team

The team runs a concentrated Indian portfolio, combines fundamental judgement with factor or scorecard discipline, needs point-in-time backtests and must keep thesis conditions current across holdings. Altys is shaped around that research-governance problem.

A firm can occupy more than one of these categories. A large institution may use a broad intelligence platform and an India-specific research system together.

What an Indian buyer should test

Do not compare polished demos. Run the same difficult workflow in every candidate system.

Test local evidence. Use an Indian company with a segment recast, delayed filing, demerger, unusual ownership change and several years of management guidance.

Test period correctness. Ask what the system would have shown on a specified past date, before the newest annual report or restatement existed.

Test the calculation. Choose a ratio whose answer depends on reporting basis, share-count adjustment or a one-time item. Reproduce it outside the platform.

Test firm context. Add an internal note, model assumption or standing research question. Check whether the system uses it without confusing it with public fact.

Test the hand-off. See whether research becomes a model change, committee record, portfolio condition or monitoring rule without losing sources and dates.

Test failure. Ask a question the evidence cannot answer. A trustworthy system should show uncertainty or unavailability clearly.

The honest conclusion

AlphaSense is the natural shortlist candidate when the research edge begins with a vast global content universe. Rogo is the natural candidate when enterprise finance workflows and polished work products are central. Altys is the natural candidate when the research system must be India-first, point-in-time, rule-based, portfolio-aware and independently verifiable.

The model will not be the durable difference. The difference is the data, process, context and institutional memory around it.

Related reading

• If everyone has AI, who wins?
• Best AI research tools for Indian stocks
• Why citations are non-negotiable in financial AI
• How to verify an AI stock-research answer
• Point-in-time financial data buyer’s guide

This article compares research software, not securities. It is educational and contains no investment recommendation. Altys Labs publishes the comparison and is one of the products discussed. Product capabilities should be verified directly with each vendor.