Multi-channel support assistant operations portal

For

Support and marketing teams running customer conversations and website tasks

Solves

Customer conversations, website tasks and campaign work sit in separate rented tools, so teams copy data between them and lose context.

Delivers

Reviewed assistant replies, tickets and campaign updates

Built in

about 4 weeks of creation time, MVP in 5 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

Run support conversations, website tasks and campaign coordination in one owned portal instead of several subscriptions.

1. Build an AI chatbot from approved knowledge and templates.
2. Interpret user intent with natural language processing.
3. Handle email, chat and social conversations in one queue.
4. Deploy the assistant to websites, mobile apps and social channels.
5. Add a website widget with a short embed snippet.
6. Assist browsing tasks inside the web browser.
7. Synchronize data across connected business applications.
8. Automate routine support and campaign workflows.
9. Create, prioritize and route support tickets automatically.
10. Hand off conversations to a human agent with full context.
11. Audit websites for performance and SEO issues.
12. Summarize long web articles into key points.
13. Match brands with relevant content creators.
14. Track campaign progress and communication.
15. Show performance and interaction data in real time.
16. Apply pre-built templates, brand styling and multilingual replies.
17. Connect CRMs and business tools through a no-code interface.

Everything these tools do, in one app

• AI chatbot creation Lets users build an AI-powered chatbot to handle conversations.Found in NexxtSupport, Chatbot Whisperer, ShoppingBotAI and 2 more
• Natural language processing Uses NLP to understand user intent and generate responses.Found in CuServly, Chatbot Whisperer, White Label AI Chatbot
• Multi-channel support Handles conversations across email, chat, and social media in one place.Found in CuServly, NexxtSupport
• Multi-platform deployment Deploys the chatbot on websites, mobile apps, and social media channels.Found in White Label AI Chatbot
• Website integration Adds the chatbot widget to a website quickly for instant responses.Found in ShoppingBotAI
• Browser integration Works inside the web browser to assist with browsing tasks.Found in Browsebuddy
• Data synchronization Keeps data aligned across multiple business applications automatically.Found in Ivah.io Sync Your Business
• Workflow automation Automates routine processes and reduces manual coordination.Found in Ivah.io Sync Your Business, CuServly, NexxtSupport and 1 more
• Automated ticketing Organizes and prioritizes support requests automatically.Found in NexxtSupport
• Human agent handoff Transfers conversations from the AI to a human agent smoothly.Found in XO Platform
• Website audits Scans a website to find performance and SEO issues.Found in SiteCompanion
• Content summarization Summarizes lengthy web articles into key points.Found in Browsebuddy
• Influencer discovery Matches brands with relevant content creators using AI.Found in Chtrbx
• Campaign management Tracks progress and communication for marketing campaigns.Found in Chtrbx
• Analytics dashboard Shows performance and interaction data in real time.Found in SiteCompanion, CuServly, NexxtSupport and 3 more
• Customizable templates Provides pre-built templates to speed up setup and deployment.Found in CuServly, Chatbot Whisperer, XO Platform
• Brand customization Adjusts the chatbot’s look and responses to match the brand.Found in ShoppingBotAI, White Label AI Chatbot
• Multilingual support Supports multiple languages for a global audience.Found in ShoppingBotAI, Browsebuddy
• No-code interface Allows non-technical users to set up and manage the tool easily.Found in Ivah.io Sync Your Business, XO Platform
• Third-party integrations Connects with popular business tools, CRMs, and platforms.Found in Ivah.io Sync Your Business, SiteCompanion, CuServly and 2 more

What goes in, what comes out

The workflow

1. In
   Start with

   Approved knowledge, brand rules, channel settings and workflow definitions

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect approved knowledge

4. 3

   Brand rules

5. 4

   Channel settings and workflow definitions

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed assistant replies, tickets and campaign updates

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 rulebook; final replies, escalations and campaign decisions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Assistant builder and knowledge, Live conversation queue, Website and campaign workspace, Analytics and controls. Use a channel list on the left, a central conversation or task canvas, and a right-hand panel for knowledge sources, brand rules and review state. Let users compare draft and approved replies side by side. Display open, waiting on human, resolved and escalated states. Provide a client preview link for the website widget with comments anchored to the relevant page. Make the task-specific outcome reviewed assistant replies, tickets and campaign updates visible beside its evidence, review state and value baseline.

Accounts and administration

Workspace ownership, knowledge versions, channel connections, reviewer roles, approval states, usage allowances, conversation retention 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 website sources. Cloud storage, CRM and helpdesk import/export and channel 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: build an AI chatbot from approved knowledge and templates; interpret user intent with natural language processing. 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 and marketing teams running customer conversations and website tasks use it to solve "customer conversations, website tasks and campaign work sit in separate rented tools, so teams copy data between them and lose context"?
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 manual handoffs per resolved case.
4. Measure, then decide. Track resolved conversations per support hour and manual handoffs per resolved case; 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 rulebook; final replies, escalations and campaign decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: build an AI chatbot from approved knowledge and templates; interpret user intent with natural language processing. Support the third module with operator review: handle email, chat and social conversations in one queue. 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, tickets and campaign updates. Retain the explicit scope boundary: One approved knowledge set and brand rulebook; final replies, escalations and campaign decisions remain human.

What the build depends on. Knowledge upload and preview, asynchronous generation jobs, editable version history, reviewer access and tested export formats. High-fidelity channel coverage 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 brand rulebook; final replies, escalations and campaign 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 and website package. Offer a monthly production allowance after repeat demand. Quote complex integrations or specialist campaign work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed assistant replies, tickets and campaign updates. 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

Run support conversations, website tasks and campaign coordination in one owned portal instead of several subscriptions. Demonstrate a concrete reviewed assistant replies, tickets and campaign updates using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Support and marketing teams running customer conversations and website tasks 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, tickets and campaign updates from a small authorized input set, with a transparent calculation of resolved conversations per support hour and manual handoffs per resolved case and no promised savings.

The first 30 days

1. Week 1: interview five support and marketing teams running customer conversations and website tasks and inspect a recent example of customer conversations, website tasks and campaign work sit in separate rented tools, so teams copy data between them and lose context.
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 manual handoffs per resolved case, 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 manual handoffs per resolved case. 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 manual handoffs per resolved case; 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, tickets and campaign updates. 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 and marketing niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for support and marketing teams running customer conversations and website tasks. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Ivah.io Sync Your Business, SiteCompanion, CuServly, NexxtSupport, Chatbot Whisperer, ShoppingBotAI, White Label AI Chatbot, Chtrbx, Browsebuddy and XO Platform are what buyers use today as separate rented subscriptions. Compare this product with the buyer's present method on resolved conversations per support hour and manual handoffs per resolved case. 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 volume, storage, reviewer hours, client revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed assistant replies, tickets and campaign updates. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve brand voice, source attribution, reply accuracy and usage permissions. Support leads approve substantive replies and escalation scope. One approved knowledge set and brand rulebook; final replies, escalations and campaign decisions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.