Multi-channel customer support automation portal

For

Support and operations teams handling customer conversations across several channels

Solves

Support teams juggle separate tools for chat, booking, lead qualification, data checks and reporting, so context is lost and repetitive work consumes agent time.

Delivers

Reviewed support actions, qualified leads and booked appointments

Built in

about 5 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 repetitive support work while keeping customer context and human review in one owned portal.

1. Automate repetitive support tasks and workflows.
2. Create and customize AI agents for specific needs.
3. Handle SMS, web chat and social media in one inbox.
4. Book and manage appointments with calendar integration.
5. Qualify and rank leads against set criteria.
6. Collect and verify customer data.
7. Send automated notifications, updates and replies.
8. Show real-time interaction and status analytics.
9. Apply privacy and security controls to customer data.
10. Accept PDFs, web pages and text files as inputs.
11. Tailor AI instructions to support needs.
12. Give unlimited team seats without per-seat charges.
13. Provide macros, notes and chat history.
14. Generate and send PDF quotes with lead capture.
15. Help customers browse products and complete purchases.
16. Summarize long documents and articles.
17. Extract relevant information while keeping context.
18. Adjust summary length and detail level.

Everything these tools do, in one app

• AI-powered automation Automates repetitive tasks and workflows using artificial intelligence.Found in Enrol AI, TalkStack AI, KaraboAI and 3 more
• Customizable AI agents Allows users to create and customize AI agents or chatbots to fit specific needs.Found in TalkStack AI, KaraboAI, NewOaks AI and 1 more
• Multi-channel support Enables communication across various channels such as SMS, web chat, and social media.Found in TalkStack AI, KaraboAI, NewOaks AI
• Appointment scheduling Automates booking and management of appointments with calendar integration.Found in TalkStack AI, KaraboAI, NewOaks AI
• Lead qualification Evaluates and ranks potential leads based on customizable criteria.Found in Enrol AI, TalkStack AI, KaraboAI
• Data collection and verification Automates the collection and verification of user data.Found in Enrol AI, KaraboAI
• Automated communication Sends automated notifications, updates, and responses to users.Found in Enrol AI, TalkStack AI, NewOaks AI
• Analytics dashboard Provides real-time insights and analytics on interactions and statuses.Found in Enrol AI, KaraboAI
• Secure data handling Ensures data privacy and compliance with security standards.Found in Enrol AI, TalkStack AI
• Multi-format input support Accepts various document formats such as PDFs, web pages, and text files.Found in NewOaks AI, FullContext
• Custom AI instructions Allows tailoring of AI behavior and responses to specific support needs.Found in AI Actions & Unlimited Seats by Zupport
• Unlimited seats Provides unlimited team member access without per-seat charges.Found in AI Actions & Unlimited Seats by Zupport
• Productivity tools Includes features like macros, notes, and chat history to enhance agent efficiency.Found in AI Actions & Unlimited Seats by Zupport
• Quote generation Creates and sends PDF quotes while capturing lead information.Found in KaraboAI
• E-commerce assistance Helps customers browse products and complete purchases.Found in KaraboAI
• Automatic summarization Condenses lengthy documents and articles into concise summaries.Found in FullContext
• Context-aware extraction Extracts relevant information while maintaining context.Found in FullContext
• Customizable summary length Allows users to adjust the length and detail level of summaries.Found in FullContext

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted conversation data, product and policy documents and scheduling rules

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted conversation data

4. 3

   Product and policy documents and scheduling rules

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Reviewed support actions, qualified leads and booked appointments

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 channel set and product catalogue; final replies, pricing and commitments remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Conversation inbox, Agent and workflow builder, Analytics and delivery. Use a channel list for conversations, a large central thread view with AI suggestions, and a right-hand panel for customer data, documents and actions. Let users compare AI drafts with approved replies. Display open, awaiting customer and resolved states. Provide a client-facing widget preview with comments anchored to the relevant message. Make the task-specific outcome reviewed support actions, qualified leads and booked appointments visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, channel connections, agent versions, customer records, approval 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

Customer-owned conversation exports, product and policy documents and permitted scheduling systems. Cloud storage, calendar providers, e-commerce platforms and messaging channels. 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: automate repetitive support tasks and workflows; create and customize AI agents for specific needs. 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 operations teams handling customer conversations across several channels use it to solve "support teams juggle separate tools for chat, booking, lead qualification, data checks and reporting, so context is lost and repetitive work consumes agent time"?
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: Handled conversations per agent hour and corrections after customer replies.
4. Measure, then decide. Track handled conversations per agent hour and corrections after customer replies; 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 channel set and product catalogue; final replies, pricing and commitments remain human. Implement one approved input format, a bounded representative case set and the first two task modules: automate repetitive support tasks and workflows; create and customize AI agents for specific needs. Support the third module with operator review: handle SMS, web chat and social media in one inbox. 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 support actions, qualified leads and booked appointments. Retain the explicit scope boundary: One approved channel set and product catalogue; final replies, pricing and commitments remain human.

What the build depends on. Conversation upload and preview, asynchronous AI 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 channel set and product catalogue; final replies, pricing and commitments 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 workflow. 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 reviewed support actions, qualified leads and booked appointments workflow. 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 repetitive support work while keeping customer context and human review in one owned portal. Demonstrate a concrete reviewed support actions, qualified leads and booked appointments workflow using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

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

Lead magnet

A reviewed sample reviewed support actions, qualified leads and booked appointments workflow from a small authorized input set, with a transparent calculation of handled conversations per agent hour and corrections after customer replies and no promised savings.

The first 30 days

1. Week 1: interview five support and operations teams handling customer conversations across several channels and inspect a recent example of support teams juggle separate tools for chat, booking, lead qualification, data checks and reporting, so context is lost and repetitive work consumes agent time.
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 handled conversations per agent hour and corrections after customer replies, 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 conversations per agent hour and corrections after customer replies. 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 conversations per agent hour and corrections after customer replies; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed support actions, qualified leads and booked appointments. 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 reply patterns, product 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 customer conversations across several channels. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Enrol AI, TalkStack AI, KaraboAI, NewOaks AI, AI Actions & Unlimited Seats by Zupport and FullContext, plus freelancers and generic chat tools. Compare this product with the buyer's present method on handled conversations per agent hour and corrections after customer replies. 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 reviewed support actions, qualified leads and booked appointments. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve customer consent, source attribution, data accuracy and usage permissions. Support managers approve substantive replies, pricing and commitments. One approved channel set and product catalogue; final replies, pricing and commitments remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.