
Team assistant operations coordination portal
Reduce manual coordination while keeping every automated action under named human review.
- For
- Operations and support leads running routine work across chat, calendar and CRM tools
- Solves
- Routine requests, tasks and customer messages are handled by hand across several rented tools that do not share context.
- Delivers
- Reviewed assistant actions, prioritized tasks and approved customer replies
- Built in
- about 5 weeks of creation time, MVP in 6 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
What it does
Reduce manual coordination while keeping every automated action under named human review.
- Build AI assistants for recurring team work.
- Configure assistants without engineering support.
- Train assistants on the team's own workflows and examples.
- Place assistants inside the chat and communication apps the team already uses.
- Suggest relevant assistants based on the current discussion.
- Prioritize the most important tasks each day.
- Send reminders for deadlines and follow-ups.
- Assign and share tasks between team members.
- Organize tasks with categories and tags.
- Connect calendars and communication platforms.
- Enrich customer records automatically.
- Draft personalized emails and follow-ups.
- Summarize customer inquiries.
- Propose automated replies for review.
- Generate social posts, ads, blog outlines and landing pages.
- Classify requests and support tickets.
- Sync with HubSpot.
- Suggest keywords and meta descriptions.
- Compare the reviewed result with the recorded baseline and value assumptions.
- Capture corrections and named-owner approval before consequential use.
- Export a versioned reviewed assistant action log with source references and unresolved questions.
Everything these tools do, in one app
- AI assistant creation Lets teams build AI assistants that handle work for them.Found in Runbear
- No-code setup Lets non-technical users build and deploy AI assistants without engineering support.Found in Runbear
- Team-specific training Trains AI agents on a team's own workflows so they understand how the team works.Found in Runbear
- Communication platform integration Puts AI assistants directly inside the chat and communication apps a team already uses.Found in Runbear
- AI agent recommendations Suggests relevant AI agents based on what a team is discussing.Found in Runbear
- Task prioritization Suggests the most important tasks to focus on each day.Found in DOO
- Automated reminders Sends notifications to keep users on track with deadlines.Found in DOO
- Task assignment and sharing Lets team members assign and share tasks with each other.Found in DOO
- Task categories and tags Lets users organize tasks with customizable categories and tags.Found in DOO
- Calendar and communication integration Connects with calendar and communication platforms to keep workflows smooth.Found in DOO
- Data enrichment Keeps customer information up to date automatically.Found in AIssistify
- Personalized email creation Crafts personalized emails and follow-up messages for customers.Found in AIssistify
- Inquiry summarization Summarizes customer inquiries so they can be handled faster.Found in AIssistify
- Automated responses Facilitates quick and accurate automated replies to customers.Found in AIssistify
- Content generation Generates high-quality written content such as social posts, ads, blog outlines, and landing pages.Found in AIssistify
- Request classification Classifies customer requests and support tickets automatically.Found in AIssistify
- HubSpot integration Connects with HubSpot so automation fits into existing customer workflows.Found in Runbear, AIssistify
- SEO and keyword tools Helps optimize content for search visibility with keyword and meta description generators.Found in AIssistify
What goes in, what comes out
- Team chat
- Task lists
- Calendar entries
- CRM records
- Customer messages
AI drafts, people review. Operational coordination portal.
- Reviewed assistant actions
- Prioritized tasks
- Approved customer replies
How it works
The workflow
- InStart with
Team chat, task lists, calendar entries, CRM records and customer messages
- 1
Confirm the buyer's problem and scope
- 2
Collect team chat
- 3
Task lists
- 4
Calendar entries
- 5
CRM records and customer messages
- 6
Then follow this sequence: 1
- OutFinish with
Reviewed assistant actions, prioritized tasks and approved customer replies
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 connected workspace and approved assistant set; final customer replies, task priorities and record changes remain human-approved. A model suggestion is never a verified fact, professional decision or authorization to act.
What your team sees
Primary screens: Assistant builder and training, Live work queue, Review and approval. Use a list of assistants and connected tools, a central queue of suggested tasks, replies and enrichments, and a right-hand panel for sources, confidence and comments. Let users compare suggested and edited versions side by side. Display draft, changes requested and approved states. Provide a shared team view with comments anchored to the relevant request or task. Make the task-specific outcome reviewed assistant actions, prioritized tasks and approved customer replies visible beside its evidence, review state and value baseline.
Accounts and administration
Workspace ownership, assistant versions, connected tool permissions, review states, usage allowances, revision limits, export 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 platforms, calendars, task boards, HubSpot and email. 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.
How we build it
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
Scoping call
Day 1Thirty 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
MVP
6 daysOne buyer segment, one recurring use case; first modules: build AI assistants for recurring team work; configure assistants without engineering support. Manual review in the loop. Built by our AI software factory.
- 3
Paid pilot
7 daysAccounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.
- 4
Full product
3 weeksSelf-serve onboarding, billing, monitoring and the wider integration set.
- 5
Run and improve
MonthlyWe 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.
- Pick the riskiest assumption. Here: will operations and support leads running routine work across chat, calendar and CRM tools use it to solve "routine requests, tasks and customer messages are handled by hand across several rented tools that do not share context"?
- Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
- Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Handled requests per operations hour and corrections after assistant output.
- Measure, then decide. Track handled requests per operations hour and corrections after assistant output; 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 connected workspace and approved assistant set; final customer replies, task priorities and record changes remain human-approved. Implement one approved input format, a bounded representative case set and the first two task modules: build AI assistants for recurring team work; configure assistants without engineering support. Support the remaining modules with operator review: train assistants on the team's own workflows and examples; place assistants inside the chat and communication apps the team already uses; suggest relevant assistants based on the current discussion; prioritize the most important tasks each day; send reminders for deadlines and follow-ups; assign and share tasks between team members; organize tasks with categories and tags; connect calendars and communication platforms; enrich customer records automatically; draft personalized emails and follow-ups; summarize customer inquiries; propose automated replies for review; generate social posts, ads, blog outlines and landing pages; classify requests and support tickets; sync with HubSpot; suggest keywords and meta descriptions. 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 assistant actions, prioritized tasks and approved customer replies. Retain the explicit scope boundary: One connected workspace and approved assistant set; final customer replies, task priorities and record changes remain human-approved.
What the build depends on. Workspace connection and preview, asynchronous assistant jobs, editable version history, reviewer access and tested export formats. High-fidelity operations require specialist review. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One connected workspace and approved assistant set; final customer replies, task priorities and record changes remain human-approved.
Investment
A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.
- Phase 1
MVP
One buyer segment, one recurring use case; first modules: build AI assistants for recurring team work; configure assistants without engineering support. Manual review in the loop.
- Phase 2
Paid pilot
Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.
- Phase 3
Full product
Self-serve onboarding, billing, monitoring and the wider integration set.
Indicative total, MVP to full product$46,000about 5 weeks of creation time · start with the MVP from $13,500
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.
| Stage | Hosting and infrastructure | AI usage | Total per month |
|---|---|---|---|
| MVP and paid pilotabout 3 customers | $30–$60 | $40–$90 | $70–$150 |
| Full productabout 50 customers | $110–$210 | $280–$560 | $390–$770 |
Run it or resell it
For your own team
Operations and support leads running routine work across chat, calendar and CRM tools run it inside the business: team chat, task lists, calendar entries, CRM records and customer messages in, reviewed assistant actions, prioritized tasks and approved customer replies out, reviewed by your people.
As part of your offer
Agencies, consultancies and software companies can offer it to their own clients under their brand. We build and maintain it; you sell and deliver it.
Your brand, or this one
Run it under your own brand, or start from this concept style.
- primary
#432791 - accent
#97c954 - surface
#e8e4f1 - ink
#22201e
- Headings
- Fraunces
- Text
- Inter
- Voice
- Calm, reliable, step-by-step
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 assistant package. Offer a monthly operations allowance after repeat demand. Quote complex multi-workspace or specialist integrations separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed assistant action log. 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 manual coordination while keeping every automated action under named human review. Demonstrate a concrete reviewed assistant action log using the buyer's approved example and show the baseline, corrections and actual delivery effort.
Where to find buyers
Operations and support leads professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.
Lead magnet
A reviewed sample assistant action log from a small authorized input set, with a transparent calculation of handled requests per operations hour and corrections after assistant output and no promised savings.
The first 30 days
- Week 1: interview five operations and support leads running routine work across chat, calendar and CRM tools and inspect a recent example of routine requests, tasks and customer messages handled by hand across several rented tools that do not share context.
- Week 2: prepare a consented or synthetic demonstration of the task modules.
- Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
- Week 4: measure handled requests per operations hour and corrections after assistant output, 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: Handled requests per operations hour and corrections after assistant output. 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
Handled requests per operations hour and corrections after assistant output; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.
Retention and expansion
Repeat the workflow when the buyer again needs reviewed assistant actions, prioritized tasks and approved customer replies. 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 assistants, connected tool 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 and support leads running routine work across chat, calendar and CRM tools. Repeatable delivery and useful integrations matter more than access to a base model.
Alternatives and positioning
Runbear, DOO and AIssistify, plus manual task lists and generic chat assistants. Compare this product with the buyer's present method on handled requests per operations hour and corrections after assistant output. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.
Main delivery costs
Model calls, message and CRM processing, storage, reviewer hours, client revision rounds and connected tool licenses. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed assistant actions, prioritized tasks and approved customer replies. Track cost per accepted output, including correction work, unsuccessful cases and support.
Safeguards
Preserve customer data rights, source attribution, message accuracy and usage permissions. Named owners approve customer replies, task priorities and record changes. One connected workspace and approved assistant set; final customer replies, task priorities and record changes remain human-approved. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.