Unified AI customer conversation and support portal

For

Support leads and operations managers running customer conversations across chat, email, SMS and social channels

Solves

Customer conversations are split across several rented tools, so context, handoffs and reporting break at the channel boundary.

Delivers

One owned support portal with reviewed AI replies and human handoff

Built in

about 4 weeks of creation time, MVP in 5 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 and keep one conversation record while preserving human control of escalations.

1. Answer routine inquiries with an AI chatbot.
2. Build and deploy bots without code.
3. Reply in multiple languages.
4. Track bot performance and user interactions.
5. Manage chat, email, SMS and social conversations in one queue.
6. Escalate to a human agent with full context.
7. Ground replies in an approved knowledge base.
8. Customize the chat widget to match brand identity.
9. Connect Facebook Messenger, WhatsApp, Slack and Microsoft Teams.
10. Manage tasks with reminders and priority settings.
11. Schedule appointments and manage calendars.
12. Sort email and suggest replies.
13. Handle support tickets with states and owners.
14. Keep customer records and interaction history.
15. Anticipate and address issues before they escalate.
16. Design and integrate multi-step support workflows.
17. Start video chat when a conversation needs it.
18. Assist human agents with insights and suggested replies.
19. Compare the reviewed result with the recorded baseline and value assumptions.
20. Capture corrections and named-owner approval before consequential use.
21. Export a versioned conversation and support record with source references and unresolved questions.

Everything these tools do, in one app

• AI-Powered Chatbot Provides automated responses to customer inquiries using artificial intelligence.Found in AI Desk, Automatic Chat, Gustabot and 7 more
• No-Code Builder Allows users to create and deploy chatbots without writing any code.Found in Automatic Chat, Gustabot, Quickchat AI and 1 more
• Multilingual Support Enables the chatbot to communicate in multiple languages.Found in Automatic Chat, Gustabot, Crisp and 1 more
• Analytics and Reporting Tracks chatbot performance and user interactions to provide insights.Found in Automatic Chat, Gustabot, Social Intents
• Omnichannel Support Manages customer conversations across multiple channels like chat, email, SMS, and social media.Found in Crisp, Social Intents, Sendbird AI agent for customer service
• Human Handoff Escalates conversations from the AI to human agents when needed.Found in Social Intents, Quickchat AI, WeConnect.chat
• Knowledge Base Provides a repository of information for the AI to generate accurate responses.Found in Crisp, Zendesk AI, Quickchat AI
• Customizable Chat Widget Allows customization of the chat interface to match brand identity.Found in Automatic Chat, Social Intents
• Integration with Messaging Apps Connects with popular messaging platforms like Facebook Messenger, WhatsApp, Slack, and Microsoft Teams.Found in Gustabot, Social Intents
• Task Management Helps users manage tasks with reminders and priority settings.Found in AI Desk
• Smart Scheduling Manages calendars and appointments automatically.Found in AI Desk
• Email Automation Sorts emails and suggests responses to improve communication efficiency.Found in AI Desk
• Ticketing System Handles support tickets efficiently.Found in Crisp
• CRM Manages customer data and interactions.Found in Crisp
• Proactive Customer Service Anticipates and addresses customer issues before they escalate.Found in Sendbird AI agent for customer service
• Workflow Designer Creates and integrates complex customer service workflows.Found in Sendbird AI agent for customer service
• Video Chat Enables real-time video communication with customers.Found in WeConnect.chat
• Agent Copilot Assists human agents with insights and suggested replies.Found in Zendesk AI

What goes in, what comes out

The workflow

1. In
   Start with

   Approved knowledge, brand rules, channel permissions and escalation policies

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect approved knowledge

4. 3

   Brand rules

5. 4

   Channel permissions and escalation policies

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   One owned support portal with reviewed AI replies and human handoff

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate replies and workflow steps for the stated task modules. Use deterministic code for routing rules, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One approved knowledge set and channel permission list; final escalation, 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: Inbox and conversation queue, Bot and workflow builder, Knowledge base, Analytics and reporting, Admin and channel settings. Use a shared inbox with channel labels, a right-hand panel for customer history, suggested replies and escalation, and a builder canvas for flows and no-code bot logic. Display draft, needs review, escalated and resolved states. Provide a client-facing widget preview and a permissioned agent view. Make the task-specific outcome one owned support portal with reviewed AI replies and human handoff visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, knowledge versions, channel connections, agent roles, approval states, usage allowances, escalation 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

Customer-owned knowledge bases, authorized conversation logs and permitted channel accounts. Cloud storage, messaging platforms and helpdesk 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

   5 days

   One buyer segment, one recurring use case; first modules: answer routine inquiries with an AI chatbot; build and deploy bots without code. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   6 days

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

4. 4

   Full product

   2 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 leads and operations managers running customer conversations across chat, email, SMS and social channels use it to solve "customer conversations are split across several rented tools, so context, handoffs and reporting break at the channel boundary"?
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: First-response time, containment rate and human correction rate.
4. Measure, then decide. Track first-response time and containment rate and human correction rate; 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 permission list; final escalation, refund and account decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: answer routine inquiries with an AI chatbot; build and deploy bots without code. Support the third module with operator review: ground replies in an approved knowledge base. 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 channel integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around one owned support portal with reviewed AI replies and human handoff. Retain the explicit scope boundary: One approved knowledge set and channel permission list; final escalation, refund and account decisions remain human.

What the build depends on. Asset upload and preview, asynchronous generation jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist support QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved knowledge set and channel permission list; final escalation, 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 owned support portal with reviewed AI replies and human handoff. 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 and keep one conversation record while preserving human control of escalations. Demonstrate a concrete owned support portal with reviewed AI replies and human handoff using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Support leads and operations managers running customer conversations across chat, email, SMS and social channels professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample owned support portal with reviewed AI replies and human handoff from a small authorized input set, with a transparent calculation of first-response time, containment rate and human correction rate and no promised savings.

The first 30 days

1. Week 1: interview five support leads and operations managers running customer conversations across chat, email, SMS and social channels and inspect a recent example of customer conversations split across several rented tools, so context, handoffs and reporting break at the channel boundary.
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 first-response time, containment rate and human correction rate, 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: First-response time, containment rate and human correction rate. 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

First-response time, containment rate and human correction rate; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs one owned support portal with reviewed AI replies and human handoff. 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, 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 leads and operations managers running customer conversations across chat, email, SMS and social channels. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

AI Desk, Automatic Chat, Gustabot, Crisp, Social Intents, Zendesk AI, Quickchat AI, WeConnect.chat, GroqChat and Sendbird AI agent for customer service. Compare this product with the buyer's present method on first-response time, containment rate and human correction rate. 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 message fees, 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 one owned support portal with reviewed AI replies and human handoff. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve customer privacy, source attribution, consent and usage permissions. Support leads approve escalation rules and account actions. One approved knowledge set and channel permission list; final escalation, 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.