Evidence-backed crypto research and trading workspace

For

Independent crypto traders, analysts and small research teams who publish or act on market views

Solves

Crypto research, on-chain checks, sentiment reading, strategy testing and content drafting sit in separate subscriptions, so evidence, decisions and published claims drift apart.

Delivers

Reviewed analysis, tested strategy signals and source-linked drafts

Built in

about 6 weeks of creation time, MVP in 7 days

Investment

$14,000 for the MVP, $47,500 for the full product

Run it

Inside your business, or as part of your offer to clients

Reduce tool sprawl and keep every trading or publishing claim tied to its evidence.

1. Answer user questions against live on-chain and market data.
2. Pull transaction-level data across supported networks.
3. Score real-time social sentiment for tracked assets.
4. Generate plain-English reports with source links.
5. Execute trades and manage assets from plain-language commands.
6. Aggregate multiple wallets into one portfolio view.
7. Trigger threshold and activity alerts.
8. Show mobile charts and summaries.
9. Resolve addresses against a labeled dataset.
10. Translate plain-English rules into deterministic strategy code.
11. Run instant backtests with full trade histories.
12. Adjust fee and slippage assumptions.
13. Summarize short-term market drivers in chat.
14. Encrypt strategy chats and logic with opt-in training only.
15. Draft articles, blogs and marketing copy.
16. Match tone and style to a brand voice.
17. Check grammar and spelling in real time.
18. Offer templates for common content formats.
19. Support multi-user collaboration on drafts.
20. Compare the reviewed result with the recorded baseline and value assumptions.
21. Capture corrections and named-owner approval before consequential use.
22. Export a versioned reviewed analysis, tested strategy signals and source-linked drafts with source references and unresolved questions.

Everything these tools do, in one app

• AI research agent Answers user questions and runs prompts against live data to provide analysis.Found in Nansen AI, HeyTraders, Surf
• On-chain data access Provides transaction-level blockchain data across multiple networks.Found in Nansen AI, Surf
• Real-time social sentiment Analyzes social media sentiment in real time to gauge market mood.Found in Surf
• AI-generated reports Creates plain-English summaries that explain complex crypto topics with sources.Found in Surf
• Natural language trading Lets users execute trades and manage assets using simple commands.Found in Surf
• Portfolio tracking Aggregates multiple wallets or assets into a single view.Found in Surf, Nansen AI
• Smart alerts Monitors activity and thresholds, notifying users of important changes.Found in Nansen AI
• Mobile dashboards Displays clear charts and summaries on mobile devices for on-the-go monitoring.Found in Nansen AI
• Labeled address dataset Uses a large set of labeled addresses to identify on-chain behavior and signals.Found in Nansen AI
• Natural language strategy translation Converts plain-English trading rules into deterministic strategy code and signals.Found in HeyTraders
• Instant backtesting Tests trading strategies quickly and provides full trade histories for verification.Found in HeyTraders
• Configurable execution assumptions Allows adjustment of fee and slippage settings for more realistic backtests.Found in HeyTraders
• Conversational market research Provides summaries of short-term market drivers through a chat interface.Found in HeyTraders
• Privacy-minded storage Encrypts strategy chats and logic, and does not use them for training unless opted in.Found in HeyTraders
• AI content generation Generates articles, blogs, and marketing copy using AI.Found in Next Alpha
• Tone and style customization Adjusts the tone and style of generated content to match brand voices.Found in Next Alpha
• Grammar and spell checking Checks grammar and spelling in real time during writing.Found in Next Alpha
• Template library Provides templates for common content formats.Found in Next Alpha
• Collaboration tools Enables multiple users to work on content simultaneously.Found in Next Alpha

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted on-chain data, social sentiment, wallet holdings and user questions

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted on-chain data

4. 3

   Social sentiment

5. 4

   Wallet holdings and user questions

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed analysis, tested strategy signals and source-linked drafts

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints, backtest math and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Supported networks and data providers are fixed for the pilot; final trading decisions and publication remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Research question and sources, Editable analysis and strategy preview, Review and publication or execution. Use a watchlist gallery for assets and wallets, a large central analysis canvas, and a right-hand panel for sources, alerts and comments. Let users compare strategy versions and backtest runs side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant claim or signal. Make the task-specific outcome reviewed analysis, tested strategy signals and source-linked drafts visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, data-source versions, client comments, approval states, usage allowances, revision limits, download history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions such as trades or publication.

Integrations and data access

Authorized on-chain data providers, social sentiment sources, wallet addresses and user documents. Cloud storage, exchange or brokerage connections for execution, and publishing destinations. Start with file exchange and validate destination specifications before promising direct execution or publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   7 days

   One buyer segment, one recurring use case; first modules: answer user questions against live on-chain and market data; pull transaction-level data across supported networks. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   8 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   3 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will independent crypto traders, analysts and small research teams who publish or act on market views use it to solve "crypto research, on-chain checks, sentiment reading, strategy testing and content drafting sit in separate subscriptions, so evidence, decisions and published claims drift apart"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Accepted research notes per analyst hour and corrections after publication or execution.
4. Measure, then decide. Track accepted research notes per analyst hour and corrections after publication or execution; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Pilot scope: One supported network set and one data provider; final trading decisions and publication remain human. Implement one approved input format, a bounded representative case set and the first two task modules: answer user questions against live on-chain and market data; pull transaction-level data across supported networks. Support the remaining modules with operator review. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported networks and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewed analysis, tested strategy signals and source-linked drafts. Retain the explicit scope boundary: One supported network set and one data provider; final trading decisions and publication remain human.

What the build depends on. Data upload and preview, asynchronous analysis jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist research QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One supported network set and one data provider; final trading decisions and publication remain human.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined research package. Offer a monthly production allowance after repeat demand. Quote complex data integrations or multi-team deployments separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed analysis, tested strategy signals and source-linked drafts. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Reduce tool sprawl and keep every trading or publishing claim tied to its evidence. Demonstrate a concrete reviewed analysis, tested strategy signals and source-linked drafts using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Independent crypto traders, analysts and small research teams professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed analysis, tested strategy signals and source-linked drafts from a small authorized input set, with a transparent calculation of accepted research notes per analyst hour and corrections after publication or execution and no promised savings.

The first 30 days

1. Week 1: interview five independent crypto traders, analysts and small research teams who publish or act on market views and inspect a recent example of crypto research, on-chain checks, sentiment reading, strategy testing and content drafting sitting in separate subscriptions.
2. Week 2: prepare a consented or synthetic demonstration of the stated task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure accepted research notes per analyst hour and corrections after publication or execution, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Accepted research notes per analyst hour and corrections after publication or execution. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Accepted research notes per analyst hour and corrections after publication or execution; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed analysis, tested strategy signals and source-linked drafts. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved sources, strategy assumptions and review examples, together with reliable delivery for a narrow research niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for independent crypto traders, analysts and small research teams who publish or act on market views. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Surf, Next Alpha, HeyTraders and Nansen AI, plus spreadsheets and manual research. Compare this product with the buyer's present method on accepted research notes per analyst hour and corrections after publication or execution. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Data-provider access, model calls, storage, reviewer hours, client revision rounds and licensed source material. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed analysis, tested strategy signals and source-linked drafts. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, data permissions and trading risk disclosures. Named humans approve substantive claims, trades and publication scope. One supported network set and one data provider; final trading decisions and publication remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.