Below is a practical, India‑focused guide to AI-driven stock advisory services: what they are, representative providers, regulatory & safety points, how to evaluate them, and a short checklist for choosing one.

1. What “AI stock advisory” means in India (brief)

• Broadly includes: (a) robo‑advisors that construct and rebalance portfolios using rules/algorithms; (b) quant/AI‑powered portfolio products (e.g., AI smallcases); (c) screening/research platforms that use ML/NLP for stock ideas, forecasts or natural‑language queries; and (d) algo/strategy engines that backtest and signal trades (some with automation). Each uses data + models to generate signals or portfolios; the human role and automation level vary by product. (InvestorAi.Smallcase.com)

2. Examples of categories and representative Indian services

• Algo / strategy engines and retail algo tools: Streak (Zerodha) — backtesting, scanners and deployable strategies (alerts / one‑click orders). Good for rule‑based algos and technical strategy testing. (Zerodha.com)
• Options / strategy suggestion platforms: Sensibull — options strategy builder, analytics and trade ideas (integrates with brokers). Useful if you trade derivatives. (Sensibull.com)
• AI / quant portfolio products on Smallcase: AI‑powered smallcases (InvestorAi, Jarvis Invest, SmartWealth.ai, QuantSmith etc.) — curated/algorithmic portfolios delivered as investable baskets. These combine model outputs with portfolio construction and are sold as smallcases. (InvestorAi.Smallcase.com)
• Screeners / research & forecasting platforms: Tickertape, Trendlyne, Tickertape’s stock screener — many platforms are adding plain‑English AI screeners and AI analytics to build or explain screens. Useful for idea discovery and screening. (help.Tickertape.in)

3. Regulatory and compliance essentials (what Indian investors must know)

• SEBI has explicitly required firms using AI tools for investment advice to: disclose the extent of AI usage; and take responsibility for client data security, integrity, and legal compliance when AI is used. These requirements were added in SEBI’s Investment Adviser / Research Analyst rule updates (amendments in 2024). That means advisers using AI must make clear what part of the advice is AI‑generated and must safeguard client data. (TaxGuru.in)
• SEBI also tightened/adapted other IA/RA rules (deposits, registration thresholds, disclosures and social‑media reporting) — so verify any advisory firm’s current SEBI registration (IA or RA) and disclosures. (timesofindia.indiatimes.com)

4. Benefits and what AI can realistically do

• Faster screening and idea generation across thousands of stocks; automated rebalancing; consistent rule‑based execution/backtesting; natural‑language Q&A or “explainable” screens; options probability/greek analysis; and production of model portfolios. AI helps scale tasks that are slow manually. (Zerodha.com)

5. Key risks & limitations

• Models rely on historical data — not guarantees of future returns. Backtests suffer from look‑ahead bias, survivorship bias and overfitting.
• “Black box” models can be opaque; performance claims may reflect past backtests, not live results.
• Data & model error risk: wrong data feeds or model bugs can cause bad signals.
• Operational/regulatory risk: some platforms only provide signals/alerts (not automated execution) because regulatory frameworks restrict certain automation for retail; understand exactly what is automated vs. manual. (MarketFeed.com)

6. How to evaluate an AI advisory service — practical checklist

• Regulatory status and disclosures: is the provider SEBI‑registered as an Investment Adviser (IA) or Research Analyst (RA)? Do they declare how they use AI and their data‑security practices (SEBI requires this disclosure)? (TaxGuru.in)
• Track record & transparency: ask for audited/live performance (not only backtests). Prefer providers that publish detailed performance metrics and methodology (and explain model limits).
• Explainability & controls: can the product explain why it chose a stock? Can you adjust risk/constraints?
• Fees & conflicts: fee model (AUA, fixed fee, subscription), and whether the provider sells products they recommend (conflict disclosure). (ksandk.com)
• Execution model: are signals auto‑executed, one‑click, or purely advisory? Know who executes trades and who holds custody (you or them). (MarketFeed.com)
• Data privacy & security: what data do they collect, where is it stored, and do they comply with SEBI’s data responsibilities for AI users? (TaxGuru.in)
• Trial / small proof: start with a small allocation or trial period; use paper trading if available (many platforms provide virtual backtesting or paper trade). (Zerodha.com)

7. Practical step‑by‑step to get started (recommended approach)

• Decide objective: long‑term wealth (AI portfolio/Smallcase) vs short‑term trading (algo/signals) vs options strategies.
• Shortlist 2–3 providers matching objective (examples above). Verify SEBI registration numbers and read their AI usage disclosures. (InvestorAi.Smallcase.com)
• Test on paper or with a small live allocation; track model calls vs real market outcomes for 3–6 months.
• Keep overall portfolio diversification: limit any single model/product to a fraction of your total investible assets.
• Keep records: save model outputs, trade logic and performance for your own review and tax/reporting.

8. Red flags to avoid

• No SEBI registration where one is expected (IA/RA) or no disclosure of AI usage. (TaxGuru.in)
• Vague performance claims (no audited numbers, no clear timeframe).
• Promises of “assured” or “guaranteed” returns — SEBI/Indian law prohibits guaranteed returns from advisers. (LiveMint.com)

9. Quick recommended reading / verification links (to check before subscribing)

• SEBI investment adviser / RA rule updates and AI disclosure requirements — check the latest SEBI circulars and the IA/RA registration list on SEBI’s site (search SEBI website for Investment Adviser / Research Analyst regulations). (legalitysimplified.com)
• Provider pages (examples you can inspect): Streak (Zerodha) for algo/backtest engine; Sensibull for options analytics; Smallcase pages for AI‑powered smallcases (InvestorAi, Jarvis, SmartWealth); Tickertape / Trendlyne for AI screeners. (Zerodha.com)

Bottom line

• AI tools can be very useful for screening, disciplined rule‑based trading, and building model portfolios, but they are tools — not guaranteed money machines. In India, SEBI requires advisers using AI to disclose usage and to be accountable for data protection and advice; always verify SEBI registration and disclosures, start small, require transparent performance evidence, and keep diversification and human oversight in place. (TaxGuru.in)

If you’d like, I can:

• shortlist 3–4 services that match a specific objective (long‑term equity investing vs intraday/derivatives trading vs automated rebalancing) and show their SEBI registration / disclosure pages, or
• prepare a short comparison table of fees, automation level, and sample performance disclosures for 4 providers (Streak, Sensibull, InvestorAi/Smallcase, Tickertape/Trendlyne).

Which of those would you prefer?