Source-linked multi-channel support assistant and console

For

Support leads and operations managers handling customer questions across phone, chat, email and SMS

Solves

Customer questions arrive on several channels at once, routine requests wait for staff, and answers drift from approved policy and live order data.

Delivers

Source-linked replies, bookings and handoffs

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

Answer routine questions automatically across channels while keeping every reply tied to an approved source and a named human owner.

1. Answer common customer questions from approved sources.
2. Handle phone, chat, email and SMS in one queue.
3. Run around the clock without added staffing.
4. Set up and customize without programming.
5. Book appointments into connected calendars.
6. Retrieve live order status and details.
7. Suggest products from store data.
8. Trigger return, feedback and intake forms.
9. Hand off to a human agent with full context.
10. Adjust tone, voice and appearance to the brand.
11. Train responses from uploaded documents and FAQs.
12. Start from industry-specific templates.
13. Run workflows that update spreadsheets and send emails.
14. Keep conversation context across turns.
15. Read store data for accurate answers.
16. Let several staff work the same queue at once.
17. Draft articles, scripts and marketing copy.
18. Give real-time editing feedback on written 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 source-linked reply set with source references and unresolved questions.

Everything these tools do, in one app

• Answer customer questions Provides automated responses to common customer inquiries.Found in Solvea, Jotform AI Agents for Shopify
• Multi-channel support Handles interactions across phone, chat, email, and SMS.Found in Solvea
• 24/7 availability Offers round-the-clock assistance without human staffing.Found in Solvea, Jotform AI Agents for Shopify
• No-code setup Allows launch and customization without programming skills.Found in Solvea, Jotform AI Agents for Shopify
• Appointment booking Schedules appointments directly into calendars.Found in Solvea
• Order tracking Retrieves live order status and details.Found in Solvea, Jotform AI Agents for Shopify
• Product recommendations Suggests products to shoppers based on store data.Found in Jotform AI Agents for Shopify
• Form triggering Launches forms for returns, feedback, or additional input.Found in Jotform AI Agents for Shopify
• Live chat handoff Transfers conversations to human agents when needed.Found in Solvea, Jotform AI Agents for Shopify
• Customizable persona Adjusts tone, voice, and appearance to match brand.Found in Solvea, Jotform AI Agents for Shopify
• Document training Uploads documents and FAQs to refine responses.Found in Solvea
• Prebuilt templates Provides industry-specific templates for quick setup.Found in Solvea, Assembly by MindPal
• Actionable workflows Performs tasks like updating spreadsheets and sending emails.Found in Solvea
• Context memory Remembers conversation context for coherent interactions.Found in Solvea
• Store data integration Connects directly to Shopify data for accurate responses.Found in Jotform AI Agents for Shopify
• Collaborative workspace Enables multiple users to work on projects simultaneously.Found in Assembly by MindPal
• AI content generation Assists with writing articles, scripts, and marketing copy.Found in Assembly by MindPal
• Real-time editing Provides feedback tools to improve writing quality.Found in Assembly by MindPal

What goes in, what comes out

The workflow

1. In
   Start with

   Approved FAQs, policy documents, product and order data and brand rules

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect approved FAQs

4. 3

   Policy documents

5. 4

   Product and order data and brand rules

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Source-linked replies, bookings and handoffs

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate replies for the stated task modules. Use deterministic code for order lookups, calendar arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One approved knowledge set and one connected store; refunds, escalations and policy exceptions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Knowledge and persona setup, Live conversation queue, Administrator console. Use a channel list with open, waiting and handed-off states, a large central conversation view with the source cited beside each reply, and a right-hand panel for order lookup, booking, forms and reviewer notes. Let supervisors compare draft and approved replies side by side. Display draft, changes requested and approved states. Provide a client-facing widget preview with comments anchored to the relevant reply. Make the task-specific outcome source-linked replies, bookings and handoffs visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, knowledge versions, channel connections, client comments, approval states, usage allowances, escalation limits, conversation 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, helpdesk and ticketing tools, calendars, store and order systems, email and SMS gateways. 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: answer common customer questions from approved sources; handle phone, chat, email and SMS in one queue. 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 leads and operations managers handling customer questions across phone, chat, email and SMS use it to solve "customer questions arrive on several channels at once, routine requests wait for staff, and answers drift from approved policy and live order data"?
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 contacts per support hour and corrections after reply.
4. Measure, then decide. Track resolved contacts per support hour and corrections after reply; accepted-reply 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 one connected store; refunds, escalations and policy exceptions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: answer common customer questions from approved sources; handle phone, chat, email and SMS in one queue. Support the remaining modules with operator review: book appointments, retrieve order status, trigger forms and hand off to a human agent. 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 channels and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around source-linked replies, bookings and handoffs. Retain the explicit scope boundary: One approved knowledge set and one connected store; refunds, escalations and policy exceptions remain human.

What the build depends on. Knowledge upload and preview, asynchronous reply 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 one connected store; refunds, escalations and policy exceptions 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 telephony, multi-store or specialist integration work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded source-linked reply set. 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

Answer routine questions automatically across channels while keeping every reply tied to an approved source and a named human owner. Demonstrate a concrete source-linked reply set using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Support leads and operations managers handling customer questions across phone, chat, email and SMS 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 reply set from a small authorized input set, with a transparent calculation of resolved contacts per support hour and corrections after reply and no promised savings.

The first 30 days

1. Week 1: interview five support leads and operations managers handling customer questions across phone, chat, email and SMS and inspect a recent example of questions arriving on several channels at once, routine requests waiting for staff, and answers drifting from approved policy and live order data.
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 contacts per support hour and corrections after reply, 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 contacts per support hour and corrections after reply. 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 contacts per support hour and corrections after reply; accepted-reply rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs source-linked replies, bookings and handoffs. 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 leads and operations managers handling customer questions across phone, chat, email and SMS. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Solvea, Jotform AI Agents for Shopify and Assembly by MindPal, plus in-house staff working the same channels. Compare this product with the buyer's present method on resolved contacts per support hour and corrections after reply. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, telephony and messaging 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 source-linked replies, bookings and handoffs. Track cost per accepted reply, including correction work, unsuccessful cases and support.

Preserve customer privacy, source attribution, answer accuracy and usage permissions. Support leads approve substantive policy changes and escalation scope. One approved knowledge set and one connected store; refunds, escalations and policy exceptions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.