Agent-run administrative work coordination portal

For

Operations leads and administrative teams coordinating research, analysis and finished deliverables across departments

Solves

Administrative knowledge work is split across several rented agent tools, so context, files and approvals do not carry from research to finished deliverable.

Delivers

Reviewed finished deliverables with a full session record

Built in

about 5 weeks of creation time, MVP in 6 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 switching and rework while keeping every agent action reviewable.

1. Accept plain-language task commands.
2. Assign and manage multiple agents at once.
3. Route work to function-specific agents for research, analysis or presentations.
4. Run a live agent in an isolated environment with terminal, browser and file system.
5. Share files, context and output between agents.
6. Chain research, analysis and deliverable steps in one workflow.
7. Coordinate tasks across sales, marketing, research and design.
8. Produce polished decks and formatted reports.
9. Generate research, charts, decks, web apps, images and video.
10. Create files, images and articles on request.
11. Schedule automated reports and reminders.
12. Run deep research and deliver regular updates.
13. Connect to approved external software platforms.
14. Use customer-controlled MCP servers for sensitive systems.
15. Rewind, restore and audit past session steps.
16. Show every action, file and decision in the session.
17. Isolate agent activity in sandboxed sessions.
18. Centralize databases, dashboards, forms and documents.
19. Build workflows without programming knowledge.
20. Keep the codebase inspectable, modifiable and self-hostable.

Everything these tools do, in one app

• Natural language task automation Automate repetitive administrative tasks by giving commands in plain language.Found in Saidar 2.0
• Multi-agent coordination Chat with and manage multiple AI agents simultaneously to handle different tasks.Found in Bika.ai, MagiCrew
• Specialized agents for functions Use agents built for specific functions like research, presentations, or data analysis.Found in MagiCrew
• Live agent with isolated computer Watch and interact with an agent running in an isolated environment with terminal, browser, and file system.Found in Raccoon AI
• Unified workspace and memory Agents share files, context, and output so work from one feeds directly into another.Found in MagiCrew
• Multi-step workflows Support chained tasks like research, analysis, and deliverable creation without switching tools.Found in Raccoon AI
• Automation workflows Coordinate tasks across business areas such as sales, marketing, research, and design.Found in Bika.ai
• Deliverable-ready outputs Produce finished work like polished decks and formatted reports ready for use.Found in MagiCrew
• Multiple output modalities Generate research, data analysis with charts, pitch decks, web apps, images, and video.Found in Raccoon AI
• File, image, and article generation Create files, images, and articles based on user requests.Found in Saidar 2.0
• Scheduled reports and reminders Schedule and send automated reports and reminders.Found in Saidar 2.0
• Deep research with updates Conduct deep research on specific topics and deliver regular updates.Found in Saidar 2.0
• Wide app integrations Connect with many popular software platforms to extend functionality.Found in Raccoon AI, Saidar 2.0, Bika.ai
• Custom MCP servers Use user-controlled custom MCP servers for sensitive systems.Found in Raccoon AI
• Rewind and session history Inspect, restore, or audit past steps and recover deleted files.Found in Raccoon AI
• Transparent session visibility See every action, file, and decision in the session for trust and debugging.Found in Raccoon AI
• Sandboxed sessions Isolate agent activity to reduce accidental access to unrelated systems.Found in Raccoon AI
• Integrated databases and documents Centralize organization with databases, dashboards, forms, and documents.Found in Bika.ai
• No-code workflow building Build and customize AI-powered workflows without programming knowledge.Found in Bika.ai
• Open-source codebase Inspect, modify, or self-host the software with publicly available source code.Found in MagiCrew

What goes in, what comes out

The workflow

1. In
   Start with

   Plain-language task requests, shared files and approved sources

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect plain-language task requests

4. 3

   Shared files and approved sources

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Reviewed finished deliverables with a full session record

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. One approved task category and one deliverable format; final accuracy, compliance and external-send checks remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Task intake and agent assignment, Live session view, Deliverable review and delivery. Use a task board for requests, a central session canvas showing agent steps, files and decisions, and a right-hand panel for sources, approvals and comments. Let users compare draft and approved versions side by side. Display queued, running, changes requested and approved states. Provide a client preview link with comments anchored to the relevant deliverable. Make the task-specific outcome reviewed finished deliverables with a full session record visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, agent roles, file 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

Customer-owned files, approved internal systems and permitted research sources. Cloud file storage, office and design-file import/export, and approved delivery destinations. Start with file exchange and validate destination specifications before promising direct 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

   6 days

   One buyer segment, one recurring use case; first modules: accept plain-language task commands; assign and manage multiple agents at once. 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 operations leads and administrative teams coordinating research, analysis and finished deliverables across departments use it to solve "administrative knowledge work is split across several rented agent tools, so context, files and approvals do not carry from research to finished deliverable"?
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 deliverables per administrative hour and rework after approval.
4. Measure, then decide. Track accepted deliverables per administrative hour and rework 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 category and one deliverable format; final accuracy, compliance and external-send checks remain human. Implement one approved input format, a bounded representative case set and the first two task modules: accept plain-language task commands; assign and manage multiple agents at once. Support the third module with operator review: route work to function-specific agents for research, analysis or presentations. 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 finished deliverables with a full session record. Retain the explicit scope boundary: One approved task category and one deliverable format; final accuracy, compliance and external-send checks remain human.

What the build depends on. File upload and preview, asynchronous agent jobs, editable version history, reviewer access and tested export formats. High-fidelity deliverables require specialist review. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved task category and one deliverable format; final accuracy, compliance and external-send checks 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 task package. Offer a monthly production allowance after repeat demand. Quote complex multi-agent or sensitive-system work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed finished deliverables with a full session record. 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 switching and rework while keeping every agent action reviewable. Demonstrate a concrete reviewed finished deliverables with a full session record using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Operations leads and administrative 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 finished deliverables with a full session record from a small authorized input set, with a transparent calculation of accepted deliverables per administrative hour and rework after approval and no promised savings.

The first 30 days

1. Week 1: interview five operations leads and administrative teams coordinating research, analysis and finished deliverables across departments and inspect a recent example of administrative knowledge work split across several rented agent 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 accepted deliverables per administrative hour and rework 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 deliverables per administrative hour and rework 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 deliverables per administrative hour and rework 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 finished deliverables with a full session record. 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 patterns, agent configurations 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 operations leads and administrative teams coordinating research, analysis and finished deliverables across departments. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Raccoon AI, Saidar 2.0, Bika.ai and MagiCrew, plus manual coordination by staff. Compare this product with the buyer's present method on accepted deliverables per administrative hour and rework after approval. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Agent runtime, 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 finished deliverables with a full session record. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, quotation accuracy and usage permissions. Named owners approve substantive changes and external sends. One approved task category and one deliverable format; final accuracy, compliance and external-send checks remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.