How 30 Crypto Platforms Build Signals & Analysis
with AI
A methodology-first market research report
Nextino research
May 2026
How
30 Non-Exchange Crypto Platforms Produce Signals & Analysis with
AI
A methodology-first deep dive — not “what features they have” but
“what’s actually happening inside the pipeline that turns raw data into
a Buy call or a research note.”
Scope: This report deliberately excludes every CEX
(Binance / Coinbase / OKX / Bybit / Kraken / Crypto.com / Bitget…) and
every DEX (Uniswap / Aave / Hyperliquid…). The 30 platforms below are
independent signal, analysis, research, on-chain and bot
platforms — i.e. the layer above the exchanges. Their job is to
tell users what to do, not to settle trades.
The previous Top-30 reports cataloged what platforms exist
and what features they ship. This one drops to the layer below:
how the sausage is actually made. Specifically:
When Token Metrics says “BTC Trader Grade = 81 / Bullish,”
what computation produces that 81?
When Messari Copilot answers “Is the Solana ecosystem still
growing?”, what data does it search, what model writes the
answer, what guardrails keep it from hallucinating?
When 3Commas auto-rebalances a DCA bot, what’s the ML model
doing and what inputs is it taking?
When Whale Alert tweets “🐳 1,500 BTC moved from Whale to Binance,”
how do they detect it sub-60 seconds and how do they decide it’s
noteworthy?
The answers reveal six recurring archetypes — six
distinct technical recipes that every platform in this space is some
variation of. Knowing them tells you exactly what to build (and what
not to build, because everyone else has commoditized it).
2. Six archetypes
of signal/analysis methodology
Every platform’s “signal/analysis” output traces back to one
or more of these six recipes:
Key insight: Most platforms combine 2-3
archetypes. Token Metrics is A+D (quant ML grades + LLM AI Chat layer).
Nansen is F+E+D (event detection + smart-money labeling + Q&A).
aixbt is D+E (LLM + sentiment) wrapped in a Persona layer. Pure
single-archetype products are rarer and harder to defend.
What this means for Nextino: the question isn’t
“which archetype should we pick?” — it’s “which 2-3 archetypes layer
best for our audience?” (Spoiler in §7.)
3. The 30 platforms
For each: Archetype · Data inputs · AI/ML tech · Output
method · Cadence · Iran accessibility · Methodology
takeaway.
3.1 Token Metrics —
tokenmetrics.com
Field
Detail
Archetype
A + D (quant ML grades + LLM Q&A on top)
Data inputs
~80 features per coin: price/volume across 6,000+ tokens, technical
indicators, market cap dynamics, on-chain (TVL, holders, dev activity),
social mentions, exchange flows
AI/ML tech
Custom gradient-boosting ML model (their public
materials hint at XGBoost-class) trained on labeled historical “what
worked.” Outputs two scores per coin: Trader Grade
(short-term 0-100) and Investor Grade (long-term
0-100). Layered on top: an LLM AI Chat (likely
GPT-4-class) with retrieval over Token Metrics’ own data store — so the
chat cites the underlying grades and metrics rather than
free-styling.
Signal generation
When Trader Grade crosses a threshold (typically 60→80 = bullish
trigger), the engine generates a structured signal with AI-suggested
entry zone, stop-loss, and target — pushed via
Discord/email/Telegram.
Cadence
Grades recomputed hourly; signal alerts fired on threshold-cross
events
Iran access
Blocked; needs VPN + foreign card
Methodology takeaway
The two-grade simplification is genius. They reduce
80 features to two numbers a human can act on. The “97% accuracy in
trending markets” claim is unverified — but the UX of
one-number-per-coin is what wins, not the actual hit-rate.
3.2 Messari — messari.io
Field
Detail
Archetype
D (LLM Q&A on curated knowledge base) —
pure
Data inputs
Messari’s proprietary research library (years of analyst-written
reports), token profiles, tokenomics, governance proposals, news feed,
token unlocks calendar, on-chain summaries
AI/ML tech
Messari Copilot = a RAG (Retrieval-Augmented
Generation) pipeline: user question → vector search over Messari’s
knowledge base → top-K passages stuffed into context → LLM (GPT-4 /
Claude — they don’t publicly commit to one) writes an answer
with footnoted citations. Credit-based
metering: each query consumes credits, which is what made the
x402 micropayment experiment possible (pay-per-question
with USDC on Base, no account).
Signal generation
Messari doesn’t really do “signals” in the trade-call sense. Their
event-based outputs are: token unlock alerts (“X tokens
unlock in 7 days, historical price impact: -8%”), governance
vote alerts, and research-note publication
alerts.
Citations are the moat. A footnoted answer (“per
Messari’s Q1 2026 Solana report”) feels infinitely more trustworthy than
a free-floating LLM response. Anyone building Q&A in this space
should copy the RAG-with-citations pattern.
3.3 Kaito AI — kaito.ai
Field
Detail
Archetype
D + E (LLM search over crypto knowledge +
sentiment/mindshare ranking)
Data inputs
Real-time crawl of thousands of crypto sources:
X/Twitter, Reddit, Discord, governance forums, podcast transcripts,
conference talks, GitHub, Substack. They were one of the first to index
audio (podcast transcripts via Whisper-class STT).
AI/ML tech
A vertical semantic search engine — proprietary
embedding model fine-tuned on crypto-specific corpus. User query embeds,
top-K passages return, an LLM synthesizes the answer. Their second
flagship: Mindshare Arena — they compute, per token,
what % of recent crypto-discourse mentions it. Output: a leaderboard of
“what’s hot RIGHT NOW.”
Signal generation
No direct trade signals. Mindshare deltas (“Token X up 340% in
mindshare this week”) are effectively soft signals; sophisticated users
front-run on them.
Yes; Yaps program shut down Jan 2026 when X revoked
their API access (cautionary tale: single-platform dependency = single
point of failure)
Methodology takeaway
Mindshare is a cheap, powerful primitive. “What is
everyone talking about?” is a sentiment-proxy that doesn’t need
fancy NLP. A 20-line script that counts crypto-keyword mentions on a few
Persian Telegram channels would give Nextino a “Persian Mindshare”
feature.
3.4 IntoTheBlock —
intotheblock.com
Field
Detail
Archetype
A + F (quant ML on on-chain + event-style
indicators)
Per-coin ML pipeline computes 5-8 traffic-light
indicators (🟢 / 🟡 / 🔴): “In/Out of the Money” (% of holders
profitable), “Concentration” (whale dominance), “Smart Money” (top-100
wallets net position), “Network Growth,” “Whale Activity.” Each light is
the output of a separate ML classifier; the user sees an
aggregated dashboard.
Signal generation
They don’t output “Buy BTC at $X.” They output a
constellation of signals — when 4 of 5 lights flip green,
that’s a soft buy signal the user interprets.
Cadence
Daily refresh on each indicator
Iran access
Yes
Methodology takeaway
Traffic-light UX = peak-glanceability. A
70-year-old non-trader can read 🟢🟢🟢🟡🔴 in two seconds. Compare to a
chart with 12 overlays. Nextino should ship 3-5 traffic lights per coin
(not 8 — paradox of choice).
3.5 LunarCrush —
lunarcrush.com
Field
Detail
Archetype
E (NLP sentiment) — pure, and very deep
Data inputs
2 trillion social data points per year (their
public figure). Sources: X, Reddit, YouTube, TikTok, Telegram public
channels. Bot-filtering pipeline is heavy — they need to throw out the
spam to get usable signal.
AI/ML tech
Custom NLP stack: language detection → sentiment
scoring (per-message FinBERT-class model) → bot/spam classifier →
aggregation. Output: Galaxy Score (0-100 per coin,
combines social volume, sentiment trend, and price momentum).
AltRank ranks each coin against the whole market.
MCP server (LLM-ready API endpoints) — explicitly
designed for AI agents to call into.
Signal generation
Galaxy Score breakouts (“Token X moved from 55 → 78 in 24h”) are the
primary signal. The MCP server lets external AI agents query “what’s the
Galaxy Score of XYZ?” inside their own reasoning.
Cadence
Near-real-time
Iran access
Yes
Methodology takeaway
The MCP-server pattern is the future. Don’t just be
a dashboard; be a data source other AIs (and humans) plug into. Future
Nextino should publish a Persian-MCP — “what’s the mindshare of
Token X on Persian Telegram?” — that other Iranian/MENA AI projects can
consume.
3.6 3Commas — 3commas.io
Field
Detail
Archetype
C (strategy marketplace + auto-execution)
Data inputs
User’s connected exchange API (Binance/Bybit/Kucoin etc.), real-time
price feeds, the user’s own portfolio state
AI/ML tech
Two distinct AI layers: (1) Bot-strategy
optimization — for DCA/grid bots, ML tunes parameters
(deviation %, take-profit, safety-orders count) based on the user’s risk
profile + recent volatility. (2) Signals marketplace —
third-party signal providers publish via API; bots execute
automatically. The “AI” in marketplace is mostly vibes —
providers self-grade and 3Commas exposes performance leaderboards.
Signal generation
Two flavors: (a) bots fire on rule-based triggers (price drops X% →
buy), (b) marketplace signal providers manually post calls; bots execute
via the user’s API key.
Cadence
Continuous
Iran access
Blocked (depends on the user’s exchange, which is blocked)
Methodology takeaway
The marketplace flywheel is interesting but slow to
bootstrap. It needs ~50 signal providers minimum for a useful
leaderboard. Nextino phase-3 idea: let Persian KOLs post signals through
Nextino with auto-tracked outcome → forced honesty + a built-in
leaderboard.
3.7 Cryptohopper —
cryptohopper.com
Field
Detail
Archetype
C (strategy marketplace + auto-execution) —
slightly more retail than 3Commas
Data inputs
Exchange APIs, price feeds, user portfolio
AI/ML tech
Strategy designer with template strategies (RSI
< 30 buy, etc.) + AI Strategy Builder (a wizard that
asks risk questions → spits out a tuned strategy). Lower technical bar
than 3Commas.
Signal generation
Marketplace of signal providers (free + paid); bots execute. Also
offers “trailing stop-loss with AI optimization” — the AI dynamically
tightens the stop based on volatility.
Cadence
Continuous
Iran access
Blocked
Methodology takeaway
AI Strategy Builder = onboarding tool, not an analytics
tool. They use AI to configure the bot once, not run
trades. Cheap to copy: a 10-question Persian quiz that maps a user to
“Conservative / Balanced / Aggressive” signal sensitivity.
3.8 Stoic AI (Cindicator) —
stoic.ai
Field
Detail
Archetype
C + A (auto-execution wrapping proprietary quant
strategies)
Data inputs
Cindicator’s long-running quant research, real-time market data,
user’s exchange API
AI/ML tech
Two pre-built strategies: “Meta” (claimed
market-neutral ~45% APY — likely a basket-rebalancing strategy) and
“Fixed Income” (claimed 10-20% APY hedged carry —
funding-rate arbitrage). The AI is the strategy itself:
ensemble ML models trained on years of crypto market behavior,
generating rebalance signals daily.
Signal generation
Fully automated — user doesn’t see signals, just outcomes. 5% AUM
annual fee (no monthly subscription).
Cadence
Daily rebalance
Iran access
Blocked
Methodology takeaway
5%-of-AUM monetization aligns incentives. Their bot
loses, they earn 0. Their bot wins, they earn proportional to gain. This
is the only model where the platform’s incentive is the same as
the user’s. Reserve as a Nextino phase-3 tier.
3.9 Nansen — nansen.ai
Field
Detail
Archetype
F + E + D (event detection + wallet labeling +
Q&A)
Data inputs
Full blockchain data for 30+ chains; 300M+ wallet addresses
with proprietary labels (Smart Money / Whales / CEX hot wallets
/ etc.)
AI/ML tech
(1) Graph ML for labeling: cluster wallets by
transaction patterns, identify “Smart Money” (top-100 wallets by
realized PnL). (2) Event detection: real-time stream
over labeled wallet activity → “Smart Money is buying X.” (3) AI
Signals Dashboard: an LLM summarizes the day’s smart-money
flows in plain English. (4) Q&A layer (newer): ask “Which wallets
bought $SOL between Mar 1-7?”
Signal generation
“Smart Money is accumulating Token X” — when N labeled smart-money
wallets buy the same token within 24h, that triggers a signal.
Cadence
Real-time stream + daily roll-up
Iran access
Yes (VPN helpful)
Methodology takeaway
The labeling is the moat, not the AI. Anyone can
query a blockchain; only Nansen knows that 0xABC… is “Wintermute” and
0xDEF… is “Justin Sun’s market-maker.” Nextino can do a lite
version: label ~50 well-known Persian/MENA crypto wallets and
surface their moves.
3.10 CryptoQuant —
cryptoquant.com
Field
Detail
Archetype
A + F (on-chain ML + event alerts)
Data inputs
On-chain BTC/ETH data (exchange reserves, miner flows, stablecoin
supply, derivatives), plus institutional fund flow signals
AI/ML tech
Per-metric ML models: each indicator (e.g., “Exchange Reserve
Change,” “Miner Position Index”) is its own time-series classifier
outputting a 0-100 score. Aggregated into daily reports written by their
analyst team (LLM-assisted but human-edited).
Signal generation
Threshold alerts (“Exchange Reserve dropped 5% in 24h → historically
bullish for BTC”) + the daily report’s “What to watch” section.
Cadence
Daily report + alert-on-trigger
Iran access
Yes; free tier is genuinely useful
Methodology takeaway
The “5-minute daily report” format is the most readable
format in this whole industry. Short. Numbered. Signal-dense.
No fluff. Nextino’s daily digest should mimic this exact structure.
3.11 Glassnode —
glassnode.com
Field
Detail
Archetype
A (on-chain ML, less event-driven than
CryptoQuant)
Data inputs
Same on-chain primitives as CryptoQuant, plus their own derived
metrics (MVRV, SOPR, Realized Cap, NUPL — many of which they
invented)
AI/ML tech
Less “AI” per se, more research-grade statistical
modeling: regime-detection on long-horizon metrics, cohort
analysis (HODLer vs short-term holder). Their “AI” is more recently
added Q&A on top of the metrics catalog.
Signal generation
Per-metric alerts (e.g., “NUPL crossed into Euphoria zone → cycle
top warning”); weekly Insights write-ups.
Cadence
Real-time metrics; weekly Insights
Iran access
Yes
Methodology takeaway
They named their own metrics (MVRV, SOPR, etc.) and
now the whole industry uses them. Owning a named
indicator in your audience’s vocabulary is a long-term moat.
Nextino should consider coining a “شاخص نکستینو” — one named,
branded signal that becomes synonymous with the product.
3.12 Arkham —
arkhamintelligence.com
Field
Detail
Archetype
F + D (event detection + LLM search)
Data inputs
Full blockchain data + their entity-labeling pipeline + public dox
info (tied to news mentions, gov’t filings, court docs)
AI/ML tech
Arkham Ultra = an LLM agent that translates
natural-language wallet-investigation queries into chain queries. “Find
the wallet that bought $TRUMP before launch” → it explores the relevant
txns, returns a candidate wallet with provenance. Tied to their
entity-labeling ML (similar approach to Nansen).
Signal generation
Entity-based alerts (“This labeled entity moved $X”), plus their
Intel Exchange — a bounty marketplace for
crowd-sourcing wallet de-anonymization.
Cadence
Real-time stream
Iran access
Yes
Methodology takeaway
Natural-language → chain-query is a powerful interaction
model. “نشانم بده کیفپولهای فعال ایرانی” (show me active
Iranian wallets) is a query that would work if you build the
labeling. But the privacy/ethics gray zone is real — Nextino should
think twice before going there.
3.13 Whale Alert —
whale-alert.io
Field
Detail
Archetype
F (event detection) — pure
Data inputs
Blockchain mempool + confirmed-tx feeds for 30+ chains; a curated
list of “interesting” addresses (top wallets, exchange hot wallets,
known whales)
AI/ML tech
There’s barely any AI here — and that’s the point.
A heuristic engine: any tx ≥ a per-chain threshold (e.g., 500 BTC,
10,000 ETH, $5M stablecoin), or any tx from/to a watched address, fires
a templated message: “🐳 1,500 BTC moved from Whale to Binance.” A
simple LLM pass can occasionally write a more contextual caption.
Signal generation
Templated tweet/notification, sub-60-second from on-chain
confirmation.
Cadence
Real-time
Iran access
Yes
Methodology takeaway
The simplest possible product can have the largest
audience. 1.8M+ X followers from one templated tweet. The
lesson for Nextino: a single channel in Bale that fires “🐳
توماتر حوت…” messages with no AI overhead would draw an audience by
itself, and you can convert those eyeballs to your main bot.
3.14 Santiment —
santiment.net
Field
Detail
Archetype
E + F (sentiment NLP + on-chain)
Data inputs
Social (X, Reddit, Telegram, Discord, Bitcointalk, 4chan…) +
on-chain BTC/ETH + dev activity (GitHub commits per project)
AI/ML tech
NLP sentiment scoring per coin (their model has been around since
2018, so it’s relatively mature); on-chain anomaly detection;
dev activity (a proprietary metric: how many real
commits a project’s GitHub has per week). Their “AI” tab includes some
LLM summarization on top.
Signal generation
“Social Volume + Sentiment + Dev Activity converge bullish” → flag.
Also their MVRV cohort breakdowns for BTC.
Cadence
Real-time metrics; daily Sanbase reports
Iran access
Yes
Methodology takeaway
Dev Activity is a quality signal in a sea of
noise signals. It’s hard for a scam token to fake real
GitHub commits. Nextino could add a “Dev Activity 🟢/🔴” per coin (free
via GitHub API) — a credibility indicator non-pros never see.
(1) Per-article sentiment classifier (positive /
negative / important) — likely a fine-tuned BERT-class model. (2)
AI summarization for paid users: condenses 10 articles
on the same story into one paragraph. (3) Filter UI:
users build custom feeds (“only ETH negative news from Tier-1
sources”).
Signal generation
High-impact news flagged with 🚨 (“Important” tag) — a soft signal.
Per-coin filtered streams.
Cadence
Real-time
Iran access
Yes
Methodology takeaway
The “important” tag is the entire product.
Aggregation is commodity. Filtering signal-from-noise is the value.
Nextino’s news pipeline already does this — but the visible tag on
the post (“📌 important”) is a UX move worth borrowing.
(1) Narrative classification: each token tagged
into 1-2 narratives via an LLM zero-shot prompt. (2) Bubble
visualization — a heatmap where bubble size = market cap, color
= % change, position = narrative bucket. The “AI” is in the
categorization, not the trade signal.
Signal generation
“This narrative is heating up — RWA bubbles all green over the last
24h” — visual pattern recognition.
Cadence
Real-time
Iran access
Yes
Methodology takeaway
Narrative-bucketed visualization answers “what’s
the next AI/RWA/DePIN?” — the question retail traders care about most.
Hard to compress into a Bale message but worth showing as a
nextino.ai/narratives page eventually.
3.17 Lookonchain —
lookonchain.com
Field
Detail
Archetype
F (event detection, narrative-style) —
kissing-cousin of Whale Alert but with editorial framing
Data inputs
Same on-chain feeds as Whale Alert + their internal whale-wallet
directory + token-launch announcements
AI/ML tech
Minimal — mostly human curators writing 2-3
sentence narratives from the raw on-chain data (“Wallet 0xABC bought 50
ETH 3 hours after Vitalik tweeted about it”). Some LLM-assisted
drafting; published as Twitter threads.
Signal generation
Twitter posts (no API alerts) — soft signal via X follower
base.
Cadence
Multiple per day
Iran access
Yes (follow on X)
Methodology takeaway
A narrative whale-alert beats a templated
one for engagement. “🐳 500 ETH” vs “Wallet that bought 50 ETH
right after Vitalik’s tweet is now selling” — guess which gets
retweeted. Nextino’s whale alerts should always include the
story.
3.18 TradingView —
tradingview.com
Field
Detail
Archetype
B (rule-based TA), with a marketplace twist
Data inputs
Real-time market data on 100k+ instruments (crypto, FX, stocks,
futures, etc.)
AI/ML tech
The platform itself is a TA-charting infrastructure, not an AI tool.
The “AI” comes from third parties: thousands of community-published
Pine Script indicators, many of which embed ML training
(LSTM-based predictors, e.g.). TradingView’s own “AI Strategy
Tester” is a recent add: describe your strategy in natural
language → it generates Pine Script.
Signal generation
Per-indicator alerts (“RSI crossed below 30”) fire to
webhook/email/push — millions of these per day across the user
base.
Cadence
Real-time tick
Iran access
Yes
Methodology takeaway
The webhook output (“when this indicator fires, hit
my URL”) is the integration pattern every signal product should expose.
Nextino doesn’t need to build TA from scratch — let users connect
their TradingView alerts to Nextino as an input to its
decision engine.
3.19 CoinStats —
coinstats.app
Field
Detail
Archetype
A + D (per-portfolio AI analysis + Q&A)
Data inputs
The user’s connected wallets/exchanges (read-only) + market
data
AI/ML tech
(1) AI Portfolio Assistant — an LLM (likely GPT-4)
prompted with the user’s portfolio snapshot → outputs concentration-risk
warnings, diversification ideas, rebalancing suggestions. (2) AI
Coin Picks — a curated weekly list, partly ML-scored, partly
editorial.
Signal generation
Mostly portfolio-level: “You’re 80% in BTC + ETH; consider adding L1
diversification” — behavioral signals more than trade
signals.
Cadence
On-demand for Q&A; weekly for Picks
Iran access
Yes
Methodology takeaway
Personalization >> generic signals. “BTC is
bullish” is interesting to no one. “Your portfolio is 67% BTC, here’s
how to think about that” is interesting to everyone who sees their own
number. Nextino’s signal personalization should mirror this — show
signals against the user’s watchlist, not “the whole market.”
3.20 The Tie — thetie.io
Field
Detail
Archetype
E (sentiment NLP, institutional-grade)
Data inputs
Twitter Firehose (full historical archive since 2018) +
TikTok + YouTube + ~50 news sources — they were one of the
earliest to get Twitter enterprise access
AI/ML tech
NLP sentiment pipeline tuned on 4+ years of point-in-time crypto
data (so they can backtest “sentiment X% → price Y% in N days”). The
institutional-only Terminal is a research workstation
with Bloomberg-class UX.
Signal generation
Sentiment-vs-price divergence flags (“Sentiment up 30%, price flat →
buy pressure building”). Mostly consumed by hedge funds, not
retail.
Cadence
Real-time
Iran access
Institutional only (custom contract)
Methodology takeaway
Point-in-time historical data is the moat that justifies
institutional pricing. Most retail tools have “current
sentiment” but no historical = no backtest = no statistical claim.
Anyone serious about quant signals needs a versioned sentiment DB.
3.21 Numerai Crypto /
Numerai Signals — numer.ai
Field
Detail
Archetype
A (quant ML — meta-model crowdsourced)
Data inputs
Numerai-provided anonymized features per crypto asset (they
obfuscate inputs to prevent reverse-engineering); participants submit
predictions
AI/ML tech
A tournament — anyone can train their own model on
Numerai’s data and submit predictions. Numerai aggregates predictions
across 30k+ participants and 1,200+ staked models via a
meta-model that weights each participant by their stake (NMR tokens) and
historical accuracy. The output is the meta-prediction.
Signal generation
Per-asset prediction vectors (long/short signal); used by Numerai’s
hedge fund + sold via Numerai Signals API
Cadence
Weekly tournament rounds
Iran access
Limited (token-staked = needs crypto access; possible via
decentralized rails)
Methodology takeaway
Crowdsourcing > internal ML. They get 30k data
scientists working for them in exchange for token rewards.
Nextino should consider letting Persian quants submit models for
free play (no token needed) — leaderboard, then maybe
pay-the-winners later. Cheap research + community moat.
3.22 Coinrule —
coinrule.com
Field
Detail
Archetype
B (rule-based TA) — democratized
Data inputs
Exchange APIs, real-time price + indicators
AI/ML tech
No-code rule builder: drag-and-drop “IF RSI < 30
AND price > 200-MA THEN buy 5%” → translates to exchange API calls.
The “AI” they market is mostly templated strategy suggestions; they
recently added an LLM-driven natural-language strategy entry (“buy BTC
when fear&greed < 20”).
Signal generation
User’s own rules fire when conditions match — alert +
auto-execution.
Cadence
Real-time
Iran access
Blocked (exchange API needed)
Methodology takeaway
Drag-and-drop = the lowest possible TA barrier. A
“build your own alert” feature where Persian users say “اگر بیتکوین به
۹۵ هزار رسید…” (if BTC hits 95k) → instant rule → Bale message — would
be a real moat in a market where every other Persian signal source is a
black-box channel.
3.23 Bitsgap —
bitsgap.com
Field
Detail
Archetype
C (auto-bot, grid-specialized)
Data inputs
Exchange APIs, recent volatility data
AI/ML tech
Grid bot configurator: ML model picks grid spacing + range based on
the last 30d volatility. “AI Smart Order Routing” splits big orders
across exchanges.
Signal generation
The bot itself; no separate signals.
Cadence
Continuous
Iran access
Blocked
Methodology takeaway
Grid bots work in chop, lose in trends. Their “AI”
is one variable — volatility regime. Easy to copy with a 3-day rolling
stddev calc.
3.24 HaasOnline —
haasonline.com
Field
Detail
Archetype
C (auto-bot, power-user)
Data inputs
Exchange APIs + custom user-written scripts
AI/ML tech
HaasScript — their proprietary scripting language for bot logic
(Pine Script analog). Recently added LLM-assisted
script-generation.
Signal generation
User-authored bots; signals are private to the user.
Cadence
Continuous
Iran access
Blocked
Methodology takeaway
Pro-user platforms are a different market entirely.
Don’t go here unless you have a 5+ year roadmap.
3.25 Mizar — mizar.com
Field
Detail
Archetype
C (strategy marketplace) — smaller-scale
3Commas
Data inputs
Exchange APIs + strategy-author signals
AI/ML tech
Mostly rule-based; some AI-assisted strategy ranking via past
performance metrics.
Signal generation
Strategy-author signals → copy-trading.
Cadence
Continuous
Iran access
Blocked
Methodology takeaway
The market has consolidated — 3Commas and Cryptohopper own the
bot-marketplace category; Mizar is a long-tail player. Confirms the
segment is hard to enter as a new player.
3.26 Altrady —
altrady.com
Field
Detail
Archetype
B + C (TA scanner + execution)
Data inputs
Multi-exchange price feeds + TA indicator engine
AI/ML tech
“Smart Trading” scanner: a TA pattern-matcher
(head-and-shoulders, breakout, etc.) runs across all exchanges in
real-time; matches push to user. No real ML — classical
computer-vision-of-charts pattern matching.
Signal generation
Pattern-match alerts; user manually executes
Cadence
Real-time
Iran access
Blocked
Methodology takeaway
Pattern-matching is not AI but vendors
love to call it that. Cheap to implement with TA-Lib or open-source
pattern libraries. For Nextino: don’t oversell “AI” when it’s actually
MACD + RSI math — users sniff that out fast.
3.27 Faradox AI —
faradox.ai
Field
Detail
Archetype
A + D (quant + LLM) — boutique, smaller than Token
Metrics
Data inputs
Market data + own ML models + Telegram channel signal
aggregation
A Telegram-first AI signal product is viable in
2026 — Faradox proves it. The bar to compete is real but not
unreachable. Nextino’s Telegram base gives it a head start over Faradox
in MENA.
3.28 DefiLlama LlamaAI —
defillama.com
Field
Detail
Archetype
D (LLM Q&A on a structured data store)
Data inputs
DefiLlama’s TVL database (every DeFi protocol, every chain,
historical)
AI/ML tech
A text-to-SQL pipeline: user question (“which
Solana DEXs grew TVL >50% last month?”) → LLM generates SQL against
DefiLlama’s open dataset → executes → LLM summarizes results into
natural language.
Signal generation
No direct signals; analytical answers
Cadence
On-demand
Iran access
Yes
Methodology takeaway
Text-to-SQL over your own DB is a 1-week build for huge UX
wins. Nextino has years of price data; “نمودار بیتکوین در ۳۰
روز گذشته در مقایسه با اتریوم” (BTC vs ETH 30d chart) → text-to-SQL →
chart render. Highly doable.
LLM Q&A widget (“what’s the top gainer today?” / “explain
Solana”) — RAG over CMC’s content + a structured-data lookup tool.
Heavily prompt-engineered to avoid trade advice (regulatory
reasons).
Signal generation
None directly; the Q&A is the product.
Cadence
On-demand
Iran access
Yes
Methodology takeaway
The “we don’t give trade advice” guardrail is
legally important. CMC’s AI explicitly refuses “should
I buy?” — Nextino must decide its position here. A safer framing:
“based on these metrics, the case for / against this coin is…”
rather than “buy / sell.”
3.30 CoinGecko GeckoAI —
coingecko.com
Field
Detail
Archetype
D (LLM on their data set)
Data inputs
CoinGecko’s dataset (broader than CMC for tokens and exchanges)
AI/ML tech
Same RAG-over-own-data pattern as CMC AI. Recently added a “ELI5
mode” for new users.
Signal generation
None directly
Cadence
On-demand
Iran access
Yes
Methodology takeaway
The “ELI5 mode” is a smart audience-widening move.
A toggle in Nextino’s AI chat — “حالت ساده / حالت پیشرفته” (simple /
advanced mode) — would expand the addressable audience without two
separate products.
4. AI patterns across the 30
After surveying 30 platforms, only six AI patterns
repeat. Every “AI in crypto” feature is one (or a stack) of
these:
Pattern
What it is
Frequency in the 30
Build complexity
Iran-feasible?
1. RAG-over-own-data Q&A
User text → vector-search the knowledge base → LLM with
citations
14/30
★★ (2-4 weeks with Claude/GPT)
✅
2. Per-coin ML scoring
Features → gradient-boost / NN → 0-100 score
9/30
★★★★ (months of labeled data)
🟡 (depends on data)
3. NLP sentiment over social
Social posts → BERT-class classifier → aggregated score
11/30
★★★ (pre-trained models exist; Persian = harder)
✅
4. Event detection on blockchain
Stream chain data → rules + wallet labels → alerts
8/30
★★ (free chain APIs + cron job)
✅
5. ML strategy optimization
Backtest data → optimize bot params
6/30
★★★ (needs backtesting infra)
🟡 (no exchange exec)
6. AI Persona / agent
LLM with personality + social presence
3/30
★★ (Eliza framework)
✅
The most-copied pattern is RAG-over-own-data
Q&A. It’s the cheapest, the most adaptable, and the
highest-perceived-AI. Nextino’s “AI Analysis” feature is already in this
category — the next step is exposing it as free-text
Q&A, not just button-driven analysis.
The pattern with the largest moat is per-coin ML
scoring — but it’s also the hardest to build (you need 2-3
years of labeled price + outcome data + ML expertise). Token Metrics and
IntoTheBlock have already done this work; competing head-on is futile
for a small team. Nextino should not try to be the next
Token Metrics. Instead: stand on top of their outputs
(where accessible) and add the Persian context layer.
The most overlooked pattern is event detection on
blockchain. Whale Alert built a 1.8M-follower audience on it
with almost no AI. This is the easiest signal product Nextino
could ship in 2 weeks — a Persian Bale channel that fires “🐳
وایت ۱۵۰۰ بیتکوین به Binance منتقل کرد” whenever a major whale moves.
Sub-cost, huge engagement.
5. Data sources patterns
What’s flowing INTO these pipelines? Mapped from the 30:
Source category
Used by
How they get it
Price/OHLC (CEX)
28/30
Binance/Bybit/etc. public APIs (free, rate-limited)
On-chain data (BTC/ETH)
14/30
Self-run nodes OR paid aggregators (Glassnode, Dune, Allium)
Social — X/Twitter
17/30
Paid X enterprise tier ($$$$/mo) OR scraping (fragile)
Social — Reddit/Discord
11/30
Reddit API + Discord bots
Telegram public channels
8/30
Telethon scraping (t.me/s/ endpoint)
News/RSS
22/30
Custom RSS scraper + a few paid feeds (CoinDesk Pro)
GitHub dev activity
6/30
GitHub Public API (free, rate-limited)
Tokenomics/unlock calendars
12/30
Mostly Messari + Token Unlocks (paid)
Key bottleneck for new entrants:X/Twitter
API access. It’s $5k+/mo for the enterprise tier; the cheap
tier is rate-limited to uselessness. This is what killed Kaito’s
Yaps program (X revoked their access) and what makes any
X-centric strategy fragile.
Cheaper substitutes that work today: -
Persian Telegram channels (scrape via
t.me/s/) — Nextino already does this for the poster’s
influencer_takes pillar - Bale public
channels — same approach, no scraping protections -
Google News RSS by topic — free, structured,
multilingual - GitHub commits per project — free,
hard-to-fake credibility signal
Nextino’s existing pipeline already has access to all of these.
The data-source moat for the Persian audience is essentially
free.
6. Cost economics
A quick reality-check on what these pipelines cost to operate.
Estimates based on public pricing + back-of-envelope.
Component
At 1k users/day
At 10k users/day
At 100k users/day
Claude/GPT LLM calls (Q&A)
~$3/day
~$30/day
~$300/day
Vector DB hosting (Pinecone-class)
~$1/day
~$5/day
~$25/day
X API enterprise tier
$5,000/mo flat
$5,000/mo flat
$5,000/mo flat
On-chain node infrastructure
$0 (free public RPCs)
~$100/mo (QuickNode)
~$500/mo
Price data feeds
$0 (CEX public APIs)
$0
$0-$2k/mo (paid Pro)
Total monthly @ no X access
~$200/mo
~$1,500/mo
~$15k/mo
Total monthly @ X enterprise
~$5,200/mo
~$6,500/mo
~$20k/mo
The single line item that breaks early-stage economics is X
API access. This is why Whale Alert (just chain data, no
social) scales to 1.8M followers cheaply, while Kaito (X-centric) needs
subscription + token revenue to survive.
Nextino’s cost profile is dominated by Claude calls
— already capped via the AvalAI proxy at ~$0.01-0.05 per analysis with
the 4-hour cache. At current ~367 active users and ~50 analysis
calls/day, Nextino spends <$2/day on AI. Scaling to 10k
users with the same per-user load = ~$50/day. Eminently sustainable.
7. Takeaways for Nextino
What to copy (each of these is a specific, scoped
build):
RAG-over-own-data Q&A (Messari pattern).
Already 70% built; the remaining 30% is exposing it as free-text
input.
Traffic-light per-coin dashboard (IntoTheBlock
pattern). 3-5 lights (price-momentum / on-chain / social / dev / risk).
Glanceable. Persian-friendly.
Named/branded indicator (Glassnode pattern). Coin
one — “شاخص نکستینو” or similar — that becomes synonymous with
the bot.
Narrative bucketing (Banter Bubbles pattern). Tag
each coin with 1-2 narratives (“AI agent / RWA / DePIN”) via a one-shot
LLM call. Show the hot narrative each day.
Dev Activity 🟢/🔴 per coin (Santiment pattern).
Free via GitHub API. Quality signal in a noise-saturated market.
“Simple / Advanced” mode toggle (GeckoAI pattern).
Same backend, two voices.
Cited Q&A (Messari pattern). When Nextino’s AI
answers, show the source data row it was reasoning from. Trust
multiplier.
What NOT to copy (these are wrong-shaped for
Nextino):
❌ Naming things “AI” when they’re TA (Altrady cautionary tale) —
Persian users sniff this out
The single highest-leverage pattern to copy in the next 30
days:#1 (RAG-over-own-data Q&A) + #4 (Persian
Whale Alert).
Q&A delivers immediate “wow this AI is mine” UX. Whale Alert
delivers an independent audience-growth channel. Together they cost
<$500 to build, add measurable value, and don’t bet the company.
8. Sources
Live web research conducted May 2026. Where pricing or volume figures
appear, the underlying citations are the same as in the previous Top-30
reports — see TOP_30_CRYPTO_PLATFORMS.html and
TOP_30_CRYPTO_AI_USE.html in this library for the full
bibliography.
Specific methodology references for this report:
Token Metrics — public docs + tokenmetrics.com/blog
End of report. Companion documents in this library:
TOP_30_CRYPTO_PLATFORMS (the value-creation report),
TOP_30_CRYPTO_AI_USE (the AI-maturity scorecard), AI_MARKET_RESEARCH
(the strategic recommendations).