Source-linked support chat and automation console

For

Support and operations teams handling chat, repetitive tasks and staff encouragement

Solves

Support chat, task automation and motivational messaging sit in separate rented tools, so data and workflows stay split.

Delivers

Reviewed assistant replies and workflow runs

Built in

about 4 weeks of creation time, MVP in 4 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 tool switching while keeping answers source-linked and reviewed.

1. Provide an easy-to-use interface for interaction and management.
2. Create and adjust distinct AI personalities and conversation styles.
3. Embed the assistant into websites via widgets.
4. Process user input in real time for coherent conversations.
5. Support multiple languages for diverse audiences.
6. Monitor engagement, conversation trends and automation performance.
7. Connect with business applications for data flow.
8. Build workflows with drag-and-drop setup.
9. Synchronize data across platforms in real time.
10. Tailor workflows with specific triggers and actions.
11. Deliver tailored daily motivational messages.
12. Provide interactive AI cheerleading that responds to user inputs.
13. Apply positive reinforcement for emotional well-being.
14. Schedule and execute repetitive actions automatically.
15. Offer context-aware suggestions for documents and data entry.
16. Integrate with common productivity tools.
17. Tailor automation rules to specific needs.
18. Receive regular updates based on user feedback.
19. Capture corrections and named-owner approval before consequential use.
20. Export a versioned reviewed assistant replies and workflow runs with source references and unresolved questions.

Everything these tools do, in one app

• User-friendly interface Provides an easy-to-use interface that simplifies interaction and management.Found in Dippy Widget - AI Character Chat, Unitor.ai, Cheerleader AI and 1 more
• Customizable AI personalities Allows users to create and adjust distinct AI characters with unique personalities and conversation styles.Found in Dippy Widget - AI Character Chat
• Website widget embedding Enables quick integration of the chat tool into websites via embeddable widgets.Found in Dippy Widget - AI Character Chat
• Real-time natural language processing Processes user input in real time to enable smooth and coherent conversations.Found in Dippy Widget - AI Character Chat
• Multi-language support Supports multiple languages to reach a diverse audience.Found in Dippy Widget - AI Character Chat
• Analytics dashboard Monitors user engagement, conversation trends, and automation performance to identify bottlenecks.Found in Dippy Widget - AI Character Chat, Unitor.ai
• Business application integration Connects with a wide range of business applications and services for seamless data flow.Found in Unitor.ai
• Visual workflow builder Enables drag-and-drop automation setup for creating workflows without coding.Found in Unitor.ai
• Real-time data synchronization Synchronizes data across different platforms in real time.Found in Unitor.ai
• Customizable triggers and actions Allows tailoring of workflows with specific triggers and actions to meet particular needs.Found in Unitor.ai
• Daily motivational messages Delivers tailored daily messages to encourage and uplift users.Found in Cheerleader AI
• Interactive AI cheerleading Provides an interactive AI-driven cheerleading experience that responds to user inputs.Found in Cheerleader AI
• Emotional well-being focus Uses positive reinforcement techniques to support emotional well-being.Found in Cheerleader AI
• Automated task management Helps schedule and execute repetitive actions automatically.Found in Dot by New Computer
• Context-aware suggestions Provides suggestions to assist with document creation, data entry, and other workflows.Found in Dot by New Computer
• Productivity tool integration Integrates with common software tools to enhance productivity without switching platforms.Found in Dot by New Computer
• Customizable automation settings Allows users to tailor automation rules to specific needs.Found in Dot by New Computer
• Regular updates Receives regular updates that introduce new features and improve stability based on user feedback.Found in Unitor.ai, Cheerleader AI

What goes in, what comes out

The workflow

1. In
   Start with

   Approved knowledge sources, workflow rules, brand voice and escalation policies

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect approved knowledge sources

4. 3

   Workflow rules

5. 4

   Brand voice and escalation policies

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed assistant replies and workflow runs

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 knowledge set and brand voice; final escalation and sensitive replies remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Knowledge and voice setup, Assistant and workflow console, Review and delivery. Use a thumbnail gallery for assistants and workflows, a large central conversation and workflow canvas, and a right-hand panel for sources, rules and comments. Let users compare assistant replies and workflow versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant reply or step. Make the task-specific outcome reviewed assistant replies and workflow runs visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, 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.

Integrations and data access

Buyer-owned knowledge bases, helpdesk and productivity tools. Cloud storage, website widgets and messaging 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

   4 days

   One buyer segment, one recurring use case; first modules: provide an easy-to-use interface for interaction and management; create and adjust distinct AI personalities and conversation styles. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   5 days

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

4. 4

   Full product

   10 days

   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 support and operations teams handling chat, repetitive tasks and staff encouragement use it to solve "support chat, task automation and motivational messaging sit in separate rented tools, so data and workflows stay split"?
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: Resolved conversations per support hour and automation steps completed without correction.
4. Measure, then decide. Track resolved conversations per support hour and automation steps completed without correction; 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 knowledge set and brand voice; final escalation and sensitive replies remain human. Implement one approved input format, a bounded representative case set and the first two task modules: provide an easy-to-use interface for interaction and management; create and adjust distinct AI personalities and conversation styles. Support the third module with operator review: embed the assistant into websites via widgets. 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 replies and workflow runs. Retain the explicit scope boundary: One approved knowledge set and brand voice; final escalation and sensitive replies remain human.

What the build depends on. Source upload and preview, asynchronous processing jobs, editable version history, reviewer access and tested export formats. High-fidelity support requires specialist review. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved knowledge set and brand voice; final escalation and sensitive replies 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 support package. Offer a monthly production allowance after repeat demand. Quote complex integrations or specialist review separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed assistant replies and workflow runs. 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 while keeping answers source-linked and reviewed. Demonstrate a concrete reviewed assistant replies and workflow runs using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Support and operations teams handling chat, repetitive tasks and staff encouragement professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed assistant replies and workflow runs from a small authorized input set, with a transparent calculation of resolved conversations per support hour and automation steps completed without correction and no promised savings.

The first 30 days

1. Week 1: interview five support and operations teams handling chat, repetitive tasks and staff encouragement and inspect a recent example of support chat, task automation and motivational messaging sit in separate rented tools, so data and workflows stay split.
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 resolved conversations per support hour and automation steps completed without correction, 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: Resolved conversations per support hour and automation steps completed without correction. 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

Resolved conversations per support hour and automation steps completed without correction; 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 replies and workflow runs. 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 replies, workflow rules and review examples, together with reliable delivery for a narrow support niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for support and operations teams handling chat, repetitive tasks and staff encouragement. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Dippy Widget - AI Character Chat, Unitor.ai, Cheerleader AI and Dot by New Computer. Compare this product with the buyer's present method on resolved conversations per support hour and automation steps completed without correction. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

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 assistant replies and workflow runs. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve brand voice, source attribution, quotation accuracy and usage permissions. Named owners approve substantive changes and external actions. One approved knowledge set and brand voice; final escalation and sensitive replies remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.