Screenshot of the Source-linked support assistant and admin console interactive demo
Screenshot of the interactive demo, on sample data

Source-linked support assistant and admin console

Reduce repeated handling while keeping every answer traceable to an approved source.

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For
Support leads and operations managers handling multi-channel customer conversations and internal tasks
Solves
Conversations, tasks and knowledge sit in separate rented tools, so context is lost and routine work is repeated.
Delivers
Reviewed replies, prioritized tasks and confirmed actions
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
01

What it does

Reduce repeated handling while keeping every answer traceable to an approved source.

  1. Maintain coherent context across multi-turn conversations.
  2. Adjust response style and behavior per channel or brand.
  3. Connect messaging platforms and CRM systems.
  4. Translate conversations in real time.
  5. Track conversation metrics and engagement.
  6. Prioritize and organize tasks with AI.
  7. Connect productivity platforms to coordinate workflows.
  8. Send automated reminders and notifications.
  9. Suggest next actions from user behavior and preferences.
  10. Support team communication and project tracking.
  11. Customize chatbot design and behavior from a dashboard.
  12. Use the buyer's own model API key for message scaling.
  13. Build knowledge-domain chatbots from approved sources.
  14. Run an always-on agent with retained context across sessions.
  15. Adjust preferences and workflows from feedback over time.
  16. Execute low-risk recurring tasks and request confirmation on higher-risk items.
  17. Build no-code workflows for common use cases.
  18. Share community-validated workflows while keeping private data isolated by default.

Everything these tools do, in one app

What goes in, what comes out

What the customer puts in
  • Permitted conversation history
  • Knowledge documents
  • Task lists
  • Platform events

AI drafts, people review. Source-linked assistant and administrator console.

What the customer gets
  • Reviewed replies
  • Prioritized tasks
  • Confirmed actions
02

How it works

The workflow

  1. In
    Start with

    Permitted conversation history, knowledge documents, task lists and platform events

  2. 1

    Confirm the buyer's problem and scope

  3. 2

    Collect permitted conversation history

  4. 3

    Knowledge documents

  5. 4

    Task lists and platform events

  6. 5

    Then follow this sequence: 1

  7. Out
    Finish with

    Reviewed replies, prioritized tasks and confirmed actions

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate replies, task priorities and action proposals for the stated task modules. Use deterministic code for routing rules, schema validation, confidence thresholds and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Final customer commitments, refunds, account changes 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: Assistant console, Conversation workspace, Admin and knowledge. Use a conversation list with channel and status, a central thread view with source citations, and a right-hand panel for tasks, suggestions and confidence state. Let reviewers compare draft and approved replies side by side. Display draft, changes requested and approved states. Provide an admin view for knowledge sources, workflow rules, confidence thresholds and audit history. Make the task-specific outcome reviewed replies, prioritized tasks and confirmed actions visible beside its evidence, review state and value baseline.

Accounts and administration

Organization ownership, channel connections, knowledge source versions, workflow rules, confidence thresholds, reviewer roles, usage allowances, retention limits, export logs and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls and explicit approval for external actions.

Integrations and data access

Buyer-owned conversation history, knowledge documents, task lists and permitted platform events. Messaging platforms, CRM systems, productivity platforms and the buyer's own model API key. Start with file exchange and validate destination specifications before promising direct platform actions. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

03

How we build it

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: maintain coherent context across multi-turn conversations; adjust response style and behavior per channel or brand. 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

    9 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 multi-channel customer conversations and internal tasks use it to solve "conversations, tasks and knowledge sit in separate rented tools, so context is lost and routine work is repeated"?
  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-contact resolution, reviewer correction time and confirmed actions per support hour.
  4. Measure, then decide. Track first-contact resolution and reviewer correction time and confirmed actions per support hour; 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 support channel, one knowledge domain and one task workflow; final customer commitments and policy exceptions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: maintain coherent context across multi-turn conversations; adjust response style and behavior per channel or brand. Support the remaining modules with operator review: connect messaging platforms and CRM systems; translate conversations in real time; track conversation metrics and engagement; prioritize and organize tasks with AI; connect productivity platforms to coordinate workflows; send automated reminders and notifications; suggest next actions from user behavior and preferences; support team communication and project tracking; customize chatbot design and behavior from a dashboard; use the buyer's own model API key for message scaling; build knowledge-domain chatbots from approved sources; run an always-on agent with retained context across sessions; adjust preferences and workflows from feedback over time; execute low-risk recurring tasks and request confirmation on higher-risk items; build no-code workflows for common use cases; share community-validated workflows while keeping private data isolated by default. 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 reviewed replies, prioritized tasks and confirmed actions. Retain the explicit scope boundary: One support channel, one knowledge domain and one task workflow; final customer commitments and policy exceptions remain human.

What the build depends on. Conversation upload and preview, asynchronous job processing, editable version history, reviewer access and tested export formats. High-fidelity support requires qualified human review. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One support channel, one knowledge domain and one task workflow; final customer commitments and policy exceptions remain human.

04

Investment

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

  1. Phase 1

    MVP

    One buyer segment, one recurring use case; first modules: maintain coherent context across multi-turn conversations; adjust response style and behavior per channel or brand. Manual review in the loop.

    $13,500 · about 4 days of creation time

  2. Phase 2

    Paid pilot

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

    $13,500 · about 5 days of creation time

  3. Phase 3

    Full product

    Self-serve onboarding, billing, monitoring and the wider integration set.

    $19,000 · about 9 days of creation time

Indicative total, MVP to full product$46,000about 4 weeks of creation time · start with the MVP from $13,500

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.

StageHosting and infrastructureAI usageTotal per month
MVP and paid pilotabout 3 customers$30–$60$60–$120$90–$180
Full productabout 50 customers$110–$210$530–$1,050$640–$1,260
05

Run it or resell it

Internally

For your own team

Support leads and operations managers handling multi-channel customer conversations and internal tasks run it inside the business: permitted conversation history, knowledge documents, task lists and platform events in, reviewed replies, prioritized tasks and confirmed actions out, reviewed by your people.

For your clients

As part of your offer

Agencies, consultancies and software companies can offer it to their own clients under their brand. We build and maintain it; you sell and deliver it.

Your brand, or this one

Run it under your own brand, or start from this concept style.

  • primary#917127
  • accent#547fc9
  • surface#f1ede4
  • ink#22201e
Headings
Libre Baskerville
Text
IBM Plex Sans
Voice
Warm, clear, calm under pressure
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 multi-brand deployments separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed replies, prioritized tasks and confirmed actions 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 repeated handling while keeping every answer traceable to an approved source. Demonstrate a concrete reviewed replies, prioritized tasks and confirmed actions workflow using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

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

Lead magnet

A reviewed sample of reviewed replies, prioritized tasks and confirmed actions from a small authorized input set, with a transparent calculation of first-contact resolution, reviewer correction time and confirmed actions per support hour and no promised savings.

The first 30 days

  1. Week 1: interview five support leads and operations managers handling multi-channel customer conversations and internal tasks and inspect a recent example of conversations, tasks and knowledge sitting in separate rented tools, so context is lost and routine work is repeated.
  2. Week 2: prepare a consented or synthetic demonstration of the task modules.
  3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
  4. Week 4: measure first-contact resolution, reviewer correction time and confirmed actions per support hour, 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-contact resolution, reviewer correction time and confirmed actions per support hour. 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-contact resolution, reviewer correction time and confirmed actions per support hour; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed replies, prioritized tasks and confirmed actions. 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, workflow 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 multi-channel customer conversations and internal tasks. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

GaliChat AI, Helpedby AI, Ghostly Chat and MuleRun are what buyers use today, each covering part of the job. Compare this product with the buyer's present method on first-contact resolution, reviewer correction time and confirmed actions per support hour. Offer one owned workflow with combined features, buyer-held data and the buyer's own brand instead of renting several subscriptions. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, translation processing, 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 replies, prioritized tasks and confirmed actions. Track cost per accepted output, including correction work, unsuccessful cases and support.

06

Safeguards

Preserve customer privacy, source attribution, consent records and usage permissions. Named reviewers approve substantive replies, account changes and external actions. One support channel, one knowledge domain and one task workflow; final customer commitments 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.

Get this solution built

Built for you by our AI software factory, MVP in about 4 days. Tell us about your business and how you want to run it: inside your company, or as part of what you offer your clients. We reply within one working day.

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