AI team and human task coordination portal

For

Small teams and solo entrepreneurs running recurring business and administrative work

Solves

Business and administrative tasks are split across rented AI assistants, human errand services and back-office tools, so context, approvals and records live in separate places.

Delivers

Reviewed task outcomes with named-owner approval

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$13,000 for the MVP, $44,000 for the full product

Run it

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

Reduce coordination overhead while keeping one owned record of tasks, approvals and spend.

1. Create AI assistant roles for editorial, programming and business strategy.
2. Let assistants join team chats and coordinate tasks.
3. Keep long-term memory across projects and communications.
4. Search the web and attach sources to decisions.
5. Draft and send emails on the user's behalf.
6. Book humans for pickups, meetings, errands and research.
7. Expose an MCP server for agent task requests.
8. Provide a REST API for task creation, tracking and payment orchestration.
9. Support flexible payment flows between agents and humans.
10. Automate compliance, tax and filing steps.
11. Connect to e-commerce platforms such as Shopify and Amazon.
12. Support business formation and bank account setup steps.
13. Run 24/7 administrative queues.
14. Adapt task templates to country-specific requirements.
15. Compare the reviewed result with the recorded baseline and value assumptions.
16. Export a versioned reviewed task record with source references and unresolved questions.

Everything these tools do, in one app

• AI virtual assistants Provides AI-powered assistants that act as team members to help with business tasks.Found in Olympia, doola: AI Co-Founder
• Team collaboration Lets AI assistants participate in team chats and coordinate tasks with users.Found in Olympia
• Long-term memory Maintains context across projects and communications for better continuity.Found in Olympia
• Web search Gathers relevant information from the web to enhance decision-making.Found in Olympia
• Email handling Sends emails and manages communications on behalf of the user.Found in Olympia
• Specialized AI roles Offers AI assistants focused on areas like editorial, programming, and business strategy.Found in Olympia
• User-friendly interface Provides an easy-to-use interface for interacting with the AI assistants.Found in Olympia
• Human task booking Allows AI agents to hire humans for physical tasks such as pickups, meetings, errands, and research.Found in RentAHuman.ai
• MCP server integration Standardizes how AI agents request human assistance via MCP server support.Found in RentAHuman.ai
• REST API Enables programmatic task creation, tracking, and payment orchestration.Found in RentAHuman.ai
• Flexible payment options Allows funds to flow between agents and humans with support for different payment models.Found in RentAHuman.ai
• Back-office automation Automates administrative tasks such as compliance, taxes, and filings.Found in doola: AI Co-Founder
• E-commerce platform integration Connects with major e-commerce platforms like Shopify and Amazon.Found in doola: AI Co-Founder
• Business formation support Assists with business setup processes from formation to bank account creation.Found in doola: AI Co-Founder
• 24/7 availability Provides continuous assistance for ongoing administrative needs at any time.Found in doola: AI Co-Founder
• Global adaptation Adapts to the unique challenges faced by entrepreneurs in over 175 countries.Found in doola: AI Co-Founder

What goes in, what comes out

The workflow

1. In
   Start with

   Team requests, business documents, platform data and task rules

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect team requests

4. 3

   Business documents

5. 4

   Platform data and task rules

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed task outcomes with named-owner approval

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One approved task taxonomy and one payment model; final compliance, legal and financial decisions remain with qualified humans. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Task intake and roles, Editable task board, Approval and delivery. Use a task list for projects, a central board for assigned AI and human work, and a right-hand panel for memory, sources and comments. Let users compare draft and approved outputs side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant task. Make the task-specific outcome reviewed task outcomes with named-owner approval visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, task 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.

Integrations and data access

Team chat, email, e-commerce platforms, payment providers and business filing destinations. Cloud document storage, calendar and task import/export. Start with file exchange and validate destination specifications before promising direct filing or payment execution. 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

   6 days

   One buyer segment, one recurring use case; first modules: create AI assistant roles for editorial, programming and business strategy; let assistants join team chats and coordinate tasks. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   7 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 small teams and solo entrepreneurs running recurring business and administrative work use it to solve "business and administrative tasks are split across rented AI assistants, human errand services and back-office tools, so context, approvals and records live in separate places"?
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: Tasks completed per operator hour and corrections after approval.
4. Measure, then decide. Track tasks completed per operator 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 approved task taxonomy and one payment model; final compliance, legal and financial decisions remain with qualified humans. Implement one approved input format, a bounded representative case set and the first two task modules: create AI assistant roles for editorial, programming and business strategy; let assistants join team chats and coordinate tasks. Support the third module with operator review: keep long-term memory across projects and communications. 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 task outcomes with named-owner approval. Retain the explicit scope boundary: One approved task taxonomy and one payment model; final compliance, legal and financial decisions remain with qualified humans.

What the build depends on. Task upload and preview, asynchronous job queues, editable version history, reviewer access and tested export formats. High-fidelity operations require specialist compliance QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved task taxonomy and one payment model; final compliance, legal and financial decisions remain with qualified humans.

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 task package. Offer a monthly production allowance after repeat demand. Quote complex compliance, legal or physical operations separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed task outcome with named-owner approval. 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 coordination overhead while keeping one owned record of tasks, approvals and spend. Demonstrate a concrete reviewed task outcome with named-owner approval using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Small teams and solo entrepreneurs professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed task outcome with named-owner approval from a small authorized input set, with a transparent calculation of tasks completed per operator hour and corrections after approval and no promised savings.

The first 30 days

1. Week 1: interview five small teams and solo entrepreneurs running recurring business and administrative work and inspect a recent example of business and administrative tasks split across rented AI assistants, human errand services and back-office tools.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure tasks completed per operator 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: Tasks completed per operator 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

Tasks completed per operator 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 task outcomes with named-owner approval. 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 task templates, country rules and review examples, together with reliable delivery for a narrow operations niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for small teams and solo entrepreneurs running recurring business and administrative work. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Olympia, RentAHuman.ai and doola: AI Co-Founder, plus freelancers and manual back-office work. Compare this product with the buyer's present method on tasks completed per operator 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

Model calls, human task payments, 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 task outcomes with named-owner approval. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve task ownership, source attribution, payment accuracy and usage permissions. Named owners approve substantive changes and external actions. One approved task taxonomy and one payment model; final compliance, legal and financial decisions remain with qualified humans. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.