What is the difference between Kalpi, Multibagg AI and Altys?

Kalpi is centered on building, backtesting, investing in and monitoring rule-based portfolios. Multibagg AI is centered on accessible AI-assisted company research, discovery, documents, alerts and portfolio analysis. Altys is centered on institutional Indian research, point-in-time data, scorecards, models, backtests, portfolio rules and a reviewable decision record.

Which platform can execute a systematic strategy through a broker?

Kalpi publicly describes a broker-connected workflow for taking a systematic portfolio live. Altys does not execute trades, and its role ends at research and portfolio governance. Buyers should confirm Multibagg's current portfolio and execution capabilities directly with the company.

Which tool is suited to individual Indian investors?

Kalpi suits investors who want to build and run explicit portfolio rules. Multibagg suits investors who want accessible AI-assisted stock research, discovery and portfolio alerts. Altys is designed primarily for professional investment teams whose requirements include point-in-time evidence, rule versioning, committee records and independent verification.

Does rule-based investing remove human judgement?

No. It makes the universe, factors, thresholds, weights and review policy explicit. Humans still decide what should be measured, investigate evidence outside the model, approve exceptions and decide when a rule no longer represents the investment thesis.

Kalpi vs Multibagg vs Altys: Three Different Ways to Use AI and Rules in Indian Investing

Q: Can Altys outputs be checked in Excel?

Yes. Important strategy, scorecard, research and monitoring outputs can be exported to Excel or Excel-ready files so a team can inspect the inputs and independently verify the work.

Kalpi, Multibagg AI and Altys represent three different directions in Indian investment software. Kalpi turns rules into backtested, broker-connected portfolios. Multibagg makes AI-assisted company research and portfolio tracking accessible. Altys connects point-in-time research, deterministic scorecards and backtests, portfolio governance and continuous monitoring for professional teams.

The products overlap on research, data and automation, but choosing by feature count would miss the important difference: what is the product expected to produce at the end?

Altys publishes this comparison and is one of the products discussed. It reflects public product information available on 4 September 2026. Confirm current features and plans directly with each company.

The short answer

Choose Kalpi when you want to define systematic portfolio rules, backtest them, connect a broker, implement the portfolio and monitor rebalancing.
Choose Multibagg AI when you want an accessible AI-first way to research Indian stocks, question documents, discover themes, follow filings and analyze a portfolio.
Evaluate Altys when an investment team needs source-linked Indian evidence, point-in-time fundamental research, deterministic scorecards and backtests, documented exceptions, portfolio rules and continuous thesis monitoring.

The comparison at a glance

Dimension	Kalpi	Multibagg AI	Altys
Centre of gravity	Systematic portfolio construction and implementation	AI-assisted stock research and portfolio analysis	Institutional research and portfolio governance
Typical user	Self-directed systematic investor, adviser or quant-oriented team	Individual investor, active researcher and growing research team	PMS, AIF, family-office and professional research team
Rule building	Core no-code portfolio workflow	Screening and discovery tools	Rules, factors, scorecards, exclusions and governance policy
Backtesting	Core strategy and portfolio capability	Not the centre of the public proposition	Point-in-time fundamental strategy testing
Company research	Financial, technical and market research	AI company questions, documents, discovery, timelines and alerts	Financials, filings, concalls, guidance, KPIs, ownership, forensics and models
Portfolio analysis	Portfolio analyzer, factor and risk views	Connected portfolios, analysis and personalized alerts	Portfolio context, exposure, limits, thesis conditions and decision records
Execution	Broker-connected investing and rebalancing	Check current product for execution scope	No execution
Verification	Backtest metrics and rule outputs	Data export on eligible plans and source material for checking	Source-linked evidence plus Excel-verifiable calculations and outputs

Kalpi: make the portfolio rule operational

Kalpi describes itself as a systematic investing platform. Its public workflow is unusually direct: create portfolio rules, backtest them, invest through a connected broker, then monitor and adjust the live system.

The no-code builder asks the user to define the universe, filters, ranking, weighting and simulation. Around it sit portfolio templates, a portfolio analyzer, stock research, sectors, indices, market breadth, FII and DII tracking and other market tools.

That end-to-end implementation is the point. A rule written in a notebook is only an intention. A rule that can produce holdings, weights and rebalance instructions has become an operating process.

Kalpi is therefore the natural fit when the primary question is:

How do I turn an explicit investment rule into a portfolio I can backtest and actually run?

For an investor who wants the tool to connect with a broker, that is a material advantage. Altys deliberately does not provide execution.

Our separate Kalpi alternative guide explains how a research-first institutional requirement differs from an execution-centered systematic workflow.

Multibagg AI: make company research accessible

Multibagg AI combines company research, an AI assistant, financial documents, thematic discovery, screeners, timelines, alerts and portfolio analysis for Indian markets. Its public plans range from a free entry point to professional and enterprise offerings.

The core interaction is accessible. Instead of beginning with a formula language or a blank model, an investor can ask a company question, read a concise answer, examine documents, explore a theme and keep followed companies on a personalized timeline.

That suits a different bottleneck:

How do I understand more Indian companies and keep up with my portfolio without reading every disclosure from the beginning?

Multibagg’s public materials also tell users that AI can make mistakes and that important information should be checked. That is a responsible disclosure. Any financial AI should be evaluated by how easily a user can move from the generated answer back to the source and verify the exact claim.

Read our Multibagg alternatives guide for the detailed comparison.

Altys: make the research process reproducible

Altys is built for teams whose hardest problem appears after the screen or answer.

A PMS, AIF or family office may already have analysts, models and strong judgement. The operating risk is fragmentation. The screen sits in one tool, the model in Excel, concall notes in documents, portfolio limits in another system, alerts in email and investment-committee reasoning in people’s memory.

Altys connects that process around a shared research record:

a defined investable universe and explicit exclusions;
deterministic factors and scorecards using the firm’s own definitions;
point-in-time backtests that use information available on each historical date;
cited filings, concalls, management guidance and company evidence;
model assumptions and recorded human overrides;
portfolio exposure, position rules and committee decisions;
monitoring conditions tied to the original thesis;
later outcomes compared with what the team expected.

Important outputs can be exported to Excel or Excel-ready files. That lets an analyst inspect the inputs, reproduce calculations and validate how a score or strategy result was produced without treating the application as a black box.

The central question is:

Can the team reconstruct why it owned this company, what evidence it had, which rule applied, why it made an exception and what later changed?

That is portfolio governance rather than broker execution or standalone company Q&A.

Rules are not all the same

All three products can appear in searches for AI investing, data-driven investing or systematic investing, but those phrases can hide several different rule types.

Discovery rules find companies matching a condition. A stock screen is the simplest example.

Selection rules rank eligible companies and determine which ones enter a portfolio.

Construction rules decide weights, caps, diversification and rebalance timing.

Monitoring rules define which changes deserve attention after a company is owned.

Governance rules define who can approve an exception, what evidence is required and how the decision will be reviewed later.

Kalpi is especially strong around selection, construction, implementation and monitoring of systematic portfolios. Multibagg is especially accessible around discovery, research and portfolio updates. Altys is designed to connect discovery and selection with diligence, governance and institutional monitoring.

A useful three-part test

1. Can you understand the rule?

Ask for the complete definition: universe, input data, missing-data treatment, ranking, weighting, rebalance schedule, costs and exceptions.

2. Can you reproduce the result?

Export the output and rebuild one date in Excel. A final return chart is not enough. You should be able to see why each security was eligible, which score it received and how the weight was assigned.

3. Can you govern it live?

When the evidence changes, does the system connect that change to the original thesis, model or rule? Can a human override be recorded? Can the team later separate a bad outcome from a broken process?

The right product is the one that handles the part your current workflow cannot reliably reproduce.

Which one should you choose?

Choose Kalpi when you want to build a systematic portfolio and take it into a broker-connected live workflow. Choose Multibagg when the priority is accessible AI-assisted company research, discovery and portfolio awareness. Evaluate Altys when the work belongs to a professional Indian investment process and the non-negotiables are point-in-time evidence, deterministic calculations, documented judgement, portfolio context and independent verification.

There is no contradiction in using tools from more than one category. A research team may use an accessible portal for exploration, a systematic platform for implementation and a governance system for the institutional record. What matters is that each tool has a clearly defined job.

This article compares software, not securities. It is educational and contains no investment recommendation. Altys Labs publishes the comparison and is one of the products discussed. Altys is not a broker or a SEBI-registered Research Analyst or Investment Adviser.