Evidence-backed investment and inventory decision workspace

For

Independent investors and small retail operators who manage both a portfolio and physical stock

Solves

Investment picks, portfolio changes and inventory reorders are decided in separate tools with no shared evidence trail, so decisions are hard to explain or audit.

Delivers

Reviewed recommendations, reorder alerts and narrative reports linked to their evidence

Built in

about 6 weeks of creation time, MVP in 7 days

Investment

$13,500 for the MVP, $46,000 for the full product

Run it

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

Reduce scattered decision-making while keeping a reviewable record for every investment and inventory action.

1. Generate AI investment suggestions for stocks and portfolio adjustments.
2. Construct and manage portfolios under stated rules.
3. Create portfolios from plain-language investment theses.
4. Place trades through connected brokerage accounts after approval.
5. Connect external brokerage platforms for trading and account access.
6. Tailor models and portfolios to personal preferences or restrictions.
7. Pull real-time market news for selected holdings.
8. Answer financial queries in a conversational advisor interface.
9. Run investing competitions with defined rules and scoring.
10. Produce narrative explanations of investment decisions.
11. Apply ethical or thematic exclusion filters.
12. Monitor and manage portfolios continuously in a hands-off mode.
13. Share portfolios with named viewers.
14. Track inventory levels with automatic updates.
15. Forecast future stock needs and demand.
16. Send reorder alerts before shortages occur.
17. Connect e-commerce and point-of-sale platforms for inventory data.
18. Report performance through analytics dashboards.
19. Compare the reviewed result with the recorded baseline and value assumptions.
20. Capture corrections and named-owner approval before consequential use.
21. Export a versioned reviewed recommendation set with source references and unresolved questions.

Everything these tools do, in one app

• AI-driven investment recommendations Provides users with AI-generated suggestions for stocks or portfolio adjustments.Found in Candlestick, Candlestick AI
• Automated portfolio management Automatically constructs and manages investment portfolios on behalf of the user.Found in Candlestick AI
• Natural language portfolio creation Allows users to describe investment theses in plain language and generates a corresponding portfolio.Found in Supertake
• Automated trade execution Places trades automatically through connected brokerage accounts.Found in Supertake
• Brokerage integrations Connects to external brokerage platforms to enable trading and account access.Found in Supertake
• Customizable investment models Lets users tailor the AI model or portfolio based on personal preferences or restrictions.Found in Candlestick, Candlestick AI
• Real-time market news Provides up-to-date news and updates related to selected stocks or investments.Found in Candlestick
• Conversational financial advisor An interactive chat interface that explains stock picks, analyzes earnings, and answers financial queries.Found in Candlestick
• Investing competitions Gamified contests where users can challenge themselves and others in investment scenarios.Found in Candlestick
• Narrative-style investment reports Generates clear, narrative explanations of investment decisions and reasoning.Found in Candlestick AI
• Ethical or thematic investing filters Allows users to set custom restrictions to exclude certain industries or align with ethical themes.Found in Candlestick AI
• Hands-off investing Provides a 'set it and forget it' experience with continuous monitoring and management.Found in Candlestick AI
• Shareable portfolios Enables users to share their created portfolios with others.Found in Supertake
• Real-time inventory tracking Tracks inventory levels in real time with automatic updates.Found in STOCKED
• Demand forecasting Uses AI algorithms to predict future stock needs and demand.Found in STOCKED
• Automated reorder alerts Sends alerts to reorder inventory to prevent shortages.Found in STOCKED
• E-commerce and POS integrations Integrates with popular e-commerce and point-of-sale platforms for inventory management.Found in STOCKED
• Reporting and analytics dashboards Provides detailed reports and dashboards for monitoring performance and making strategic decisions.Found in STOCKED

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted market data, brokerage positions, inventory records, sales history and stated constraints

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted market data

4. 3

   Brokerage positions

5. 4

   Inventory records

6. 5

   Sales history and stated constraints

7. 6

   Then follow this sequence: 1

8. Out
   Finish with

   Reviewed recommendations, reorder alerts and narrative reports linked to their evidence

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 and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Trading authority, suitability judgments and final inventory commitments remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Mandate and constraints setup, Analysis and recommendation workspace, Review and approval queue, Reporting and delivery. Use a thumbnail gallery for portfolios and stock lists, a large central analysis canvas, and a right-hand panel for sources, constraints and comments. Let users compare recommendation versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant holding or SKU. Make the task-specific outcome reviewed recommendations, reorder alerts and narrative reports linked to their evidence visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, data 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 trade placement.

Integrations and data access

Brokerage platforms, e-commerce and point-of-sale systems, market data feeds and cloud storage. Start with file exchange and validate destination specifications before promising direct trading or reordering. 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: generate AI investment suggestions; construct and manage portfolios under stated rules. 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 investors and small retail operators who manage both a portfolio and physical stock use it to solve "investment picks, portfolio changes and inventory reorders are decided in separate tools with no shared evidence trail, so decisions are hard to explain or audit"?
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 recommendations per review hour and corrections after approval.
4. Measure, then decide. Track accepted recommendations per review hour and corrections after approval; 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 brokerage connection and one inventory source; trading authority, suitability judgments and final inventory commitments remain human. Implement one approved input format, a bounded representative case set and the first two task modules: generate AI investment suggestions; construct and manage portfolios under stated rules. 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 inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewed recommendations, reorder alerts and narrative reports linked to their evidence. Retain the explicit scope boundary: One brokerage connection and one inventory source; trading authority, suitability judgments and final inventory commitments 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 trading and inventory work requires specialist review. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One brokerage connection and one inventory source; trading authority, suitability judgments and final inventory commitments 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 portfolio and inventory package. Offer a monthly production allowance after repeat demand. Quote complex multi-broker or multi-store setups separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed recommendation set. 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 scattered decision-making while keeping a reviewable record for every investment and inventory action. Demonstrate a concrete reviewed recommendation set using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Independent investor and small retail operator professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample recommendation set from a small authorized input set, with a transparent calculation of accepted recommendations per review hour and corrections after approval and no promised returns.

The first 30 days

1. Week 1: interview five independent investors and small retail operators and inspect a recent example of investment picks, portfolio changes and inventory reorders being decided in separate tools with no shared evidence trail.
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 recommendations per review hour and corrections after approval, 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 recommendations per review hour and corrections after approval. 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 recommendations per review hour and corrections after approval; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed recommendations, reorder alerts and narrative reports linked to their evidence. 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 mandates, exclusion rules and review examples, together with reliable delivery for a narrow finance and retail niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for independent investors and small retail operators. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Candlestick, STOCKED, Supertake and Candlestick AI. Compare this product with the buyer's present method on accepted recommendations per review hour and corrections after approval. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Market data access, model calls, storage, reviewer hours, client revision rounds and licensed source data. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed recommendations, reorder alerts and narrative reports linked to their evidence. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve data rights, source attribution, suitability constraints and usage permissions. Named owners approve trades, reorders and publication scope. One brokerage connection and one inventory source; trading authority, suitability judgments and final inventory commitments remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.