Kavout

Kavout is an AI stock research platform built around one number: the Kai Score, a machine-learning rating from 1 to 9 that compresses 200-plus factors — momentum, earnings quality, analyst sentiment shifts, short interest, institutional interest — into a single rank for each stock. The company was founded in Seattle in 2015 by a team with Wall Street quant backgrounds, and it has spent the years since packaging institutional-style scoring for individual investors. At time of writing, the Kai Score covers more than 9,000 US stocks and recalculates every 30 minutes intraday, and the product around it has grown into a full research desk: InvestGPT chat, seven specialized AI research agents, stock pickers, 100-plus smart signals, market movers, and — on the top tier — factor portfolio construction and optimization. In my testing, Kavout is best understood as a second opinion machine: not a broker, not a terminal, but a cheap, quant-flavored sanity check you run against your own process.

What it actually does

The commercial structure is a three-tier freemium subscription, published at kavout.com/pricing-plans. Free is $0 with 10 research credits a month, one research agent, limited financial metrics, and US stocks and ETFs plus crypto and forex. Pro is $16 a month when billed as $192 every twelve months (the month-to-month checkout lists $20 a month — third-party reviews describe the range as $16–20 depending on billing and promotions), with 1,000 monthly credits, seven AI agents, InvestGPT with research mode, AI Stock Pickers, Smart Signals, real-time alerts, advanced metrics and access to 30-plus global markets. Premium is $39 a month billed as $468 a year with 3,000 credits and adds the portfolio toolbox: Factor Portfolio Builder, portfolio diagnosis and optimization, Guru Portfolios and saved personal portfolios. Annual billing advertises a 20 percent saving, credits can be topped up, and there is no lock-in beyond the billing period.

Inside the product, the Kai Score is the spine. Each stock gets a 1 to 9 rating — 9 meaning the blended signals are historically associated with stronger forward performance over the model's backtest window, not a buy recommendation, and the vendor says score-9 stocks have historically outperformed the S&P 500 (a vendor claim about past model behavior, not a promise). A Golden Trades view flags names where the Value, Momentum and Quality models all agree, which is a sensible consensus layer for reducing single-model noise. The screeners are modeled on named, published strategies — Greenblatt-style magic formula, Lynch-style growth categories, Piotroski F-Score quality screens — which I appreciate because they anchor the quant flavor in literature rather than mystique. InvestGPT and the agent fleet answer plain-English questions like which energy stocks have rising Kai Scores this month, and deep research reports are available on paid tiers.

My testing notes

First observation: the Kai Score does exactly what a scoring product should — it ranks fast and it is legible. Adding a watchlist populates scores immediately with no brokerage connection required, which is the right privacy posture for research. Running my own watchlist of about thirty names through it over two weeks, the score distribution behaved sensibly: high-momentum quality names clustered high, turnarounds and heavily indebted stories sat mid, and obvious junk scored low. What struck me was how often the score changed my sequencing rather than my decision — it surfaces which of two good candidates is in better factor shape right now. That is a real, if modest, use.

Second observation: the explanations run shallower than the score deserves. When Kavout hands you a 9, you get a factor summary, not a full breakdown of the 200-plus inputs and their weights. For a product whose whole value is compressing complexity, the asymmetry is frustrating: the harder the number is to audit, the more you want the audit trail. A quant-minded user can reconstruct approximations from the factor views, but the product does not hand you the model.

Third: the credit system is the practical constraint. Free gives 10 credits a month, and an agent research question or a deep report consumes credits — in my testing the free tier is a day or two of casual use, not a month. Pro's 1,000 credits comfortably covers a few research sessions a week; heavy daily users should price Premium before they hit the top-up button. The interface itself works but feels dated next to TipRanks or Simply Wall St — dense tables, functional navigation, none of the polish — which matters less than the data but tells you where the engineering budget goes.

Worth adding, because the audience overlap is real: I see Kavout most often used alongside a charting tool rather than instead of one. The pairing that works is TradingView or your broker's charts for timing and a Kavout subscription for the factor layer — which of two names in the same sector is in better fundamental and momentum shape. Trying to make Kavout your only research surface stretches it past what it is: the charting is serviceable but basic, news is a feed rather than analysis, and the community features that make TipRanks sticky are absent here. Used as a specialist scoring layer, its simplicity stops being a flaw and starts being focus.

What it does not do

There is no order execution and no broker integration: Kavout researches, it does not trade. Some data is delayed — commodity futures and index quotes run 15 to 20 minutes behind in my observation — and real-time alerts refer to watchlist signal changes rather than tick data. Equity coverage leans US: paid tiers open up 30-plus global markets, but the depth and score coverage outside the US is thinner, and crypto coverage is thinner still. It is also not beginner material — the product assumes you know what a factor, a F-Score and a quality screen are, and the Kai Score's 1–9 shorthand invites exactly the kind of blind-following it warns against. Score accuracy claims rest on vendor backtests over unspecified windows; I could not verify the historical outperformance of score-9 stocks from published methodology, and you should treat it as vendor marketing until Kavout publishes the study.

Smaller gaps: no options flow or derivatives analytics, no fixed income, and portfolio tools are gated behind the $39 Premium tier even though portfolio analysis is where a scoring product is most useful. And the research-agency framing around InvestGPT can oversell: these are retrieval and summarization agents over Kavout's data, not independent analysts.

Verdict

Kavout is a good-value quant second opinion for active investors who already have a process and want a fast ML ranking across thousands of names. I rate it 3.5: core functionality is genuinely useful — the Kai Score, consensus signals and strategy screeners do real work (40%), value is strong at $16–20 a month against TipRanks at roughly $30 (20%), usability is functional but dated and the credit model needs planning (20%), support and ecosystem are modest — a knowledge base, an academy, an API for signal data, and an affiliate program rather than a large community (10%), and reliability signals include eight-plus years of operation and transparent scoring inputs though unverifiable performance claims (10%). Bring your own judgment and treat the score as a tiebreaker, and Kavout earns its subscription. Follow the score alone and it will not save you.

Sources

• https://kavout.com/pricing-plans
• https://kavout.com/k-score
• https://insigtrade.com/blog/kavout-review-2026-is-this-ai-stock-rating-tool-worth-it
• https://aitradingcamp.com/reviews/kavout
• https://quantbrainai.net/blog/kavout-ai-investment-review-jul-2026
• https://kavout.com
• https://www.kavout.com/product/list