Source-linked support assistant and admin console

For

Support leads and operations managers running multi-channel customer service

Solves

Customer queries arrive across chat, email and social channels, and teams rent separate tools for AI answers, human escalation, agent building and analytics.

Delivers

Source-linked answers, escalations and reports

Built in

about 4 weeks of creation time, MVP in 4 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

Reduce repeated customer questions and manual data tasks while keeping a human backup for uncertain cases.

1. Interpret customer queries using natural language understanding.
2. Handle chat, email and social messages in one queue.
3. Build tailored automation workflows without code.
4. Show interaction and agent performance analytics.
5. Sync with CRM and helpdesk records.
6. Route uncertain cases to verified human experts.
7. Publish agents through a no-code builder.
8. Hand off session context so customers do not repeat details.
9. Manage subscriptions and expert payouts.
10. Allocate a disclosed share of revenue to AI safety research.
11. Compare answers from multiple AI engines side by side.
12. Create agents with a drag-and-drop builder.
13. Share and discover agents in a permissioned store.
14. Support future creator monetization.
15. Process large ticket and message datasets quickly.
16. Produce charts and reports for support analysis.
17. Import and export from approved data sources.
18. Let team members work in the same workspace in real time.
19. Capture corrections and named-owner approval before consequential use.
20. Export a versioned source-linked answer set with references and unresolved questions.

Everything these tools do, in one app

• Natural language understanding Interprets customer queries accurately using advanced NLP.Found in agent.ai
• Multi-channel support Handles interactions across chat, email, and social media platforms.Found in agent.ai
• Customizable workflows Allows users to automate repetitive tasks with tailored workflows.Found in agent.ai, Colossal
• Analytics dashboard Provides insights into customer interactions and agent performance.Found in agent.ai
• CRM and helpdesk integration Seamlessly integrates with popular CRM and helpdesk software.Found in agent.ai
• Human-backed agents Every AI agent can be supported by verified human experts who are bookable and earn a share of revenue.Found in Konfide
• No-code agent builder Lets creators publish agents without writing code or managing infrastructure.Found in Konfide
• Contextual escalation and handoff Transfers session context to a human expert so users don't need to repeat details.Found in Konfide
• Integrated payments and payouts Manages subscriptions and pay-per-session flows with platform-managed payouts to experts.Found in Konfide
• Governance and giving Allocates a portion of platform revenue to independent AI safety research and lets subscribers direct those funds.Found in Konfide
• Multi-AI answer aggregation Receives responses from multiple top AI engines simultaneously to compare and find the most accurate answers.Found in Internet.io
• Low-code agent builder Creates custom AI agents using an intuitive drag-and-drop interface without heavy programming knowledge.Found in Internet.io
• Agent store Shares and explores AI agents created by the community, fostering collaboration and innovation.Found in Internet.io
• Future monetization Upcoming features will allow users to monetize their custom agents, opening opportunities for creators.Found in Internet.io
• Free beta access Provides full access to features at no cost during the beta phase.Found in Internet.io
• High-performance data processing Handles massive datasets quickly with a high-performance engine.Found in Colossal
• Visual analytics tools Creates detailed charts and reports for data analysis.Found in Colossal
• Data source integration Integrates with popular data sources and platforms for seamless data import/export.Found in Colossal
• Collaborative environment Enables team members to work together in real time.Found in Colossal

What goes in, what comes out

The workflow

1. In
   Start with

   Approved help content, past tickets, CRM records and channel messages

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect approved help content

4. 3

   Past tickets

5. 4

   CRM records and channel messages

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Source-linked answers, escalations and reports

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate answers for the 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 help corpus and channel set; final policy, 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: Assistant console, Agent builder, Analytics and admin. Use a queue view for open conversations, a large central thread with source citations, and a right-hand panel for customer context, escalation and review state. Let users compare multi-model answers side by side. Display draft, escalated, resolved and approved states. Provide a client-facing chat widget with a handoff link. Make the task-specific outcome source-linked answers, escalations and reports visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, asset versions, customer comments, approval states, usage allowances, revision limits, download 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 help content, authorized ticket exports and permitted CRM sources. Cloud storage, 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

   4 days

   One buyer segment, one recurring use case; first modules: interpret customer queries using natural language understanding; handle chat, email and social messages 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

   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 running multi-channel customer service use it to solve "customer queries arrive across chat, email and social channels, and teams rent separate tools for AI answers, human escalation, agent building and analytics"?
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 tickets per support hour and repeat contacts per issue.
4. Measure, then decide. Track resolved tickets per support hour and repeat contacts per issue; 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 help corpus and channel set; final policy, refund and account decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: interpret customer queries using natural language understanding; handle chat, email and social messages in one queue. Support the third module with operator review: build tailored automation workflows without code. 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 source-linked answers, escalations and reports. Retain the explicit scope boundary: One approved help corpus and channel set; final policy, refund and account decisions remain human.

What the build depends on. Asset upload and preview, asynchronous answer jobs, editable version history, reviewer access and tested export formats. High-fidelity support requires specialist review. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved help corpus and channel set; final policy, 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 review separately. These are test prices, not market benchmarks. Package the initial sale as one bounded source-linked answers, escalations and reports 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

Reduce repeated customer questions and manual data tasks while keeping a human backup for uncertain cases. Demonstrate a concrete source-linked answers, escalations and reports 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 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 answers, escalations and reports set from a small authorized input set, with a transparent calculation of resolved tickets per support hour and repeat contacts per issue and no promised savings.

The first 30 days

1. Week 1: interview five support leads and operations managers running multi-channel customer service and inspect a recent example of customer queries arriving across chat, email and social channels, and teams renting separate tools for AI answers, human escalation, agent building and analytics.
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 tickets per support hour and repeat contacts per issue, 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 tickets per support hour and repeat contacts per issue. 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 tickets per support hour and repeat contacts per issue; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs source-linked answers, escalations and reports. 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 running multi-channel customer service. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

agent.ai, Konfide, Internet.io and Colossal, plus freelancers and generic chatbot tools. Compare this product with the buyer's present method on resolved tickets per support hour and repeat contacts per issue. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, message processing, storage, reviewer hours, client revision rounds and licensed source content. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of source-linked answers, escalations and reports. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve customer privacy, source attribution, answer accuracy and usage permissions. Support leads approve substantive changes and external actions. One approved help corpus and channel set; final policy, 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.