Opportunities › AI-powered cryptocurrency trading automation › Proposed app
Tillgate
Multi-model market analysis with hard risk rules that always have the last word
- Score
- 29
- confidence 42
- Evidence
- 2
- items on the page
- Rivals
- 1
- named in sources
- First version
- 45 d
- estimate
- Type
- Desktop and mobile
- PWA: on computers and phones alike
- Market since
- 2023
- Emerging market · AI estimate
ScreensConcept
Desktop and mobile: Traders monitor models and set risk rules at a desk, but also need to stop trades from anywhere with the phone kill switch feature. Sample screens on a computer and a phone, in the proposed brand. Their numbers are sample data.
Signal
Sent to risk check3 of 4 agree
About this appAI proposal
Tillgate helps crypto and quant traders automate trading decisions with several AI models, while a fixed set of written risk rules decides what is allowed to execute. The models read the market and propose trades. The rules check each proposal and can veto it.
Every rule is plain and deterministic: position size caps, daily loss limits, exposure per asset, and a kill switch. Nothing is learned or guessed at the gate. Each decision is logged with the model votes, the rule that fired and the outcome, so you can audit what happened and why.
It is for traders who have outgrown manual analysis or single-model bots and want AI help without giving it unchecked control. Start in paper mode, compare models side by side, then connect an exchange with trade-only keys.
- Tagline
- Let the models propose. Let your rules decide.
- Voice
- PlainCarefulPrecise
- Why this name
- A tillgate suggests a gate that holds back flow until conditions are met, like the risk rules that hold back trades. Short and easy to say.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Several models, one verdict
Run multiple language models on the same market snapshot. See where they agree, where they split, and set how much agreement a trade needs.
No member yet
Risk rules that override AI
Deterministic limits on size, loss, exposure and frequency. A rule veto always wins over any model output.
No member yet
Readable rule editor
Write limits as simple conditions with sample checks. Test a rule against past proposals before turning it on.
No member yet
Full decision audit trail
Each trade stores the inputs, model opinions, rule checks and result. Export it as CSV for your own review.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#12202BDeep Ink · primary, dark surface#F4F1EAPaper · surface#1B2530Charcoal · text#E0A526Signal Amber · accent#2E7D5BGate Green · data, approved#B3402FVeto Red · data, blocked
Typography
Aa
Tillgate
PricingAI proposal
The material gives no competitor prices. These tiers are a starting point to test, using the tier ideas in the opportunity: volume, call limits and exchange capacity. No share of trades is taken.
Paper
Free
- Paper trading only
- 2 models
- 3 risk rules
- 7-day decision log
Trader
$49 a month
- 1 exchange, live trading
- 4 models
- Unlimited risk rules
- Full audit log and CSV export
- Kill switch and alerts
Desk
$149 a month
- Up to 4 exchanges
- Higher model call limits
- Multiple strategies in parallel
What it could be worthEstimate
What a software business making this much sells for today, from real deals. Pick a monthly revenue.
If it makes
Worth about
$194,000
Sale range $132,000 to $263,000
$120,000 a year in revenue
From 87 software businesses listed and sold on Empire Flippers in the last year, revenue vetted by the broker: they sell for about 19× their monthly revenue, most between 13× and 26×.
Measured on profit, the middle deal is 32× monthly net profit: about 2.7 years of profit.
An estimate, not an offer or financial advice. A real price depends on profit, growth, churn, how much of the work is the owner's, and the buyer.
How it would growAI proposal
How the product would reach new customers through its own use, and how the first ones would be found. A plan to test, not a result.
- Built-in loop
- No loop in the product. Trading runs privately for each user, so use does not expose the product to others. Reach comes from content, community posts and open example rule sets.
- Do they become customers?
- Test whether traders who try paper mode from community posts move to a paid live plan. Public interest on GitHub is verified, but paid conversion is not.
- First customers
- Solo quant-minded crypto traders who already run their own scripts or bots on one major exchange and want AI analysis with enforced limits.
- Where to reach them
- GitHub and open-source forums: Publish a small open rule-set library and a demo so people who starred similar projects find the product.
- Crypto trading communities: Share paper-mode results and write-ups on how veto rules blocked bad trades, inviting people to a waitlist.
- First milestone
- Fifty paper-mode users in four weeks, then ten paying for live trading on one exchange.
Why people already pay for this
- VerifiedGitHub repository gained 376 stars in 46 days
- Public claimA market exists for autonomous trading systems that combine AI analysis with deterministic risk controls
Tech stack
Every proposal builds on the same PWA base, installable from the browser on any phone or computer. Dashed boxes are what this app adds.
- AppNext.js and TypeScript
- PWAManifest and service worker
- UITailwind CSS
- Data and authSupabase Postgres
- PaymentsStripe
- EmailResend
- HostingVercel
- This app · Risk engineDeterministic Python rules serviceRules must run as plain code, separate from the AI output, so results are repeatable and auditable.
- This app · Model accessMulti-provider LLM gatewayCalls several models on the same snapshot and records each answer and its cost.
RisksAI inference
- RegulatoryAutomated trading and software that acts on users' funds may fall under financial rules in some countries. Legal review is needed before live trading and before any share-of-trades pricing.
- CommercialUsers may lose money and blame the product. Marketing must not promise returns, and paper mode must come first. Willingness to pay is unproven beyond GitHub interest.
- AIModels can give confident but wrong analysis, and several models may share the same bias. Agreement between them is not proof of a good trade.
- TechnicalAlways-on loops, exchange outages, rate limits and partial fills can leave positions in odd states. The kill switch and safe defaults need heavy testing.
- DataExchange keys are high-value secrets. A breach could cause real losses, so encryption, trade-only scopes and key rotation are essential.
Everything marked AI proposal or AI inference was written by a model from the evidence on the opportunity page. The proposed name must be checked against trademark registers before it is used.