Source-linked support assistant and admin console

For

Support and operations teams running customer questions across websites, messaging apps and social channels

Solves

Support answers are spread across several rented chatbot tools, so content, channels, escalation and reporting do not share one source-linked record.

Delivers

Source-linked answers, reviewed escalations and one admin console

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

Reduce tool sprawl while keeping every answer tied to an approved source.

1. Build and modify chatbots in a visual no-code builder.
2. Deploy one bot across website, messaging apps and social channels.
3. Interpret customer queries with natural language processing.
4. Create custom conversation flows and paths.
5. Answer in multiple languages.
6. Show an analytics dashboard for interactions and performance.
7. Train on owned documents, tickets and website content.
8. Embed a chat widget on sites and internal portals.
9. Capture feedback and satisfaction scores.
10. Hand off complex conversations to human agents.
11. Centralize channel messages in one omni-channel inbox.
12. Run automation rules and macros.
13. Connect help centers and CRM systems.
14. Fetch real-time data through APIs.
15. Suggest replies to agents.
16. Apply customizable response templates.
17. Schedule recurring automation tasks.
18. Qualify leads and hand over prospects to sales.

Everything these tools do, in one app

• No-code chatbot builder Lets users create and modify chatbots through a visual interface without writing code.Found in Chatbase, ChatFlow, FastBots and 2 more
• Multi-channel deployment Deploys the chatbot across websites, messaging apps, and social media platforms.Found in Chatbase, Kommunicate GenAI, ConversaLink and 4 more
• Natural language processing Understands and responds to user queries accurately using NLP.Found in Chatbase, ChatFlow, Landbot
• Custom conversation flows Allows creation of tailored conversation paths to guide user interactions.Found in Chatbase, Hoory AI
• Multi-language support Enables chatbot communication in multiple languages for diverse audiences.Found in Chatbase, Hoory AI, Supportbot Pro and 1 more
• Analytics dashboard Provides insights into chatbot interactions and performance metrics.Found in Chatbase, DocsBot, Hoory AI and 5 more
• Training on custom content Trains the chatbot using your own documents, support tickets, or website content.Found in DocsBot, Kommunicate GenAI, Chatio.ai
• Embeddable chat widget Adds a chat widget to websites or internal portals for easy integration.Found in DocsBot
• Feedback and satisfaction tracking Captures user feedback and tracks customer satisfaction to improve responses.Found in DocsBot
• Human agent handoff Escalates complex conversations to human agents when needed.Found in DocsBot, Kommunicate GenAI, Landbot
• Omni-channel inbox Centralizes messages from different platforms into a single inbox for management.Found in Hoory AI
• Automation rules and macros Sets up automation rules and custom action sequences to streamline support tasks.Found in Hoory AI
• Integration with knowledge bases Connects with existing help centers or CRM systems like Zendesk and Salesforce.Found in Kommunicate GenAI
• Real-time data access via APIs Provides up-to-date information to customers through API connections.Found in Kommunicate GenAI, FastBots
• AI-driven message suggestions Suggests replies to speed up responses and maintain consistency.Found in ConversaLink
• Customizable templates Offers templates to tailor responses to specific audiences and save time.Found in ConversaLink
• Scheduled automation Runs tasks automatically at specified times.Found in FastBots
• Conversational sales representative Qualifies leads, answers questions, and hands over prospects to sales teams.Found in Landbot

What goes in, what comes out

The workflow

1. In
   Start with

   Owned help content, ticket history and product data

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect owned help content

4. 3

   Ticket history and product data

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Source-linked answers, reviewed escalations and one admin console

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate answers 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 knowledge set and channel set; final policy, refund and account decisions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Knowledge and sources, Bot and flow builder, Live inbox and escalation, Analytics and value. Use a source list with permission states, a visual flow canvas, a conversation view with cited answers, and a right-hand panel for rules, macros and reviewer notes. Let users compare draft and approved answers side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant answer. Make the task-specific outcome source-linked answers, reviewed escalations and one admin console visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, source versions, channel connections, reviewer notes, approval states, usage allowances, escalation 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

Owned help centers, CRM systems, messaging channels and product APIs. Cloud storage, ticket import/export and analytics 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: build and modify chatbots in a visual no-code builder; deploy one bot across website, messaging apps and social channels. 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 support and operations teams running customer questions across websites, messaging apps and social channels use it to solve "support answers are spread across several rented chatbot tools, so content, channels, escalation and reporting do not share one source-linked record"?
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 corrections after answer approval.
4. Measure, then decide. Track resolved conversations per support hour and corrections after answer 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 knowledge set and channel set; final policy, refund and account decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: build and modify chatbots in a visual no-code builder; deploy one bot across website, messaging apps and social channels. Support the third module with operator review: interpret customer queries with natural language processing. 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 source-linked answers, reviewed escalations and one admin console. Retain the explicit scope boundary: One approved knowledge set and channel set; final policy, refund and account decisions remain human.

What the build depends on. Source upload and preview, asynchronous answer jobs, editable version history, reviewer access and tested export formats. High-fidelity support requires specialist QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved knowledge set and channel set; final policy, refund and account decisions 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 support separately. These are test prices, not market benchmarks. Package the initial sale as one bounded source-linked answers, reviewed escalations and one admin console. 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 sprawl while keeping every answer tied to an approved source. Demonstrate a concrete source-linked answers, reviewed escalations and one admin console using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

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

Lead magnet

A reviewed sample source-linked answers, reviewed escalations and one admin console from a small authorized input set, with a transparent calculation of resolved conversations per support hour and corrections after answer approval and no promised savings.

The first 30 days

1. Week 1: interview five support and operations teams running customer questions across websites, messaging apps and social channels and inspect a recent example of support answers spread across several rented chatbot tools, so content, channels, escalation and reporting do not share one source-linked record.
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 corrections after answer 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: Resolved conversations per support hour and corrections after answer 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

Resolved conversations per support hour and corrections after answer approval; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs source-linked answers, reviewed escalations and one admin console. 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 answers, escalation 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 running customer questions across websites, messaging apps and social channels. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Chatbase, DocsBot, Hoory AI, Kommunicate GenAI, ConversaLink, ChatFlow, FastBots, Supportbot Pro, Chatio.ai and Landbot. Compare this product with the buyer's present method on resolved conversations per support hour and corrections after answer 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, channel messaging fees, storage, reviewer hours, client revision rounds and licensed source content. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of source-linked answers, reviewed escalations and one admin console. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve customer privacy, source attribution, answer accuracy and usage permissions. Support leads approve substantive changes and channel scope. One approved knowledge set and channel set; final policy, refund and account decisions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.