Source-linked conversation agent operations console

For

Support and revenue teams deploying chatbots and voice agents on their own systems

Solves

Conversation agents are rented from several tools, so data, workflow and brand stay split across vendors.

Delivers

Source-linked agent replies and completed tasks

Built in

about 4 weeks of creation time, MVP in 4 days

Investment

$13,000 for the MVP, $44,000 for the full product

Run it

Inside your business, or as part of your offer to clients

Own one console for chatbots, voice agents and autonomous tasks instead of renting several subscriptions.

1. Build agents in a no-code visual builder.
2. Deploy custom chatbots for customer interactions.
3. Run voice agents for calls and voice interactions.
4. Let agents complete tasks autonomously within set limits.
5. Train on the buyer's own documents and transcripts.
6. Support multiple languages.
7. Deploy across web, mobile, WhatsApp and Slack.
8. Integrate with existing business systems.
9. Show real-time performance analytics.
10. Start from pre-built templates.
11. Connect documents, websites and media as knowledge sources.
12. Customize agent personality, behavior and visuals.
13. Provide console and API access for developers.
14. Read webpage context for tailored replies.
15. Schedule appointments inside conversations.
16. Keep call history and a message center for follow-ups.
17. Apply enterprise security and compliance controls.

Everything these tools do, in one app

• No-code builder Allows users to create AI agents without programming skills using visual interfaces.Found in Dante AI, FlowHunt, Juji and 2 more
• Custom AI chatbots Enables building and deploying tailored chatbots for customer interactions.Found in Dante AI, FlowHunt, Juji and 3 more
• AI voice agents Provides voice-based agents to handle calls and voice interactions.Found in Dante AI, PolyAI, Paka AI and 1 more
• Autonomous AI agents Agents that can perform tasks independently without human intervention.Found in FlowHunt, Saleforce Agentforce, WorkGPT and 1 more
• Train on own data Allows training AI models with proprietary data for personalized responses.Found in Dante AI, FlowHunt, WorkGPT
• Multi-language support Supports multiple languages for global customer interactions.Found in Dante AI, PolyAI
• Multi-channel deployment Deploys agents across various platforms like web, mobile, WhatsApp, Slack.Found in Dante AI, Saleforce Agentforce, Paka AI
• Integration with systems Integrates with existing business systems and platforms.Found in Dante AI, Saleforce Agentforce, PolyAI and 1 more
• Real-time analytics Provides live performance data and insights for optimization.Found in Juji, PolyAI, Paka AI
• Pre-built templates Offers ready-to-use templates for common tasks and workflows.Found in FlowHunt, WorkGPT, MindStudio
• Knowledge sources Connects to documents, websites, and media to keep AI answers relevant.Found in FlowHunt
• Customizable interactions Allows personalization of agent personalities, behaviors, and visuals.Found in Role Model AI, Juji
• Developer tools Provides console and API support for custom deployments.Found in Role Model AI
• Context-aware agents Agents understand the webpage context for tailored responses.Found in MindStudio
• Appointment scheduling Enables scheduling appointments through customer interactions.Found in Paka AI
• Call history and message center Saves call history and provides a message center for follow-ups.Found in Paka AI
• Enterprise security Ensures security and compliance for enterprise deployments.Found in MindStudio, Saleforce Agentforce, PolyAI

What goes in, what comes out

The workflow

1. In
   Start with

   Approved knowledge sources, system connections and brand rules

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect approved knowledge sources

4. 3

   System connections and brand rules

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Source-linked agent replies and completed tasks

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate replies and task steps for the stated 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 one deployment channel per pilot; final policy, pricing and escalation decisions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Agent builder and knowledge sources, Live conversation console, Admin and analytics. Use a thumbnail gallery for agents, a large central canvas for dialogue and task steps, and a right-hand panel for sources, integrations and comments. Let users compare agent versions side by side. Display draft, in review and live states. Provide a client preview link with comments anchored to the relevant reply or call. Make the task-specific outcome source-linked agent replies and completed tasks visible beside its evidence, review state and value baseline.

Accounts and administration

Agent ownership, source versions, conversation logs, approval states, usage allowances, escalation 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

Buyer-owned knowledge bases, help desks, CRMs and telephony. Cloud storage, messaging channels and scheduling systems. Start with file exchange and validate destination specifications before promising direct deployment. 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: build agents in a no-code visual builder; deploy custom chatbots for customer interactions. 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 and revenue teams deploying chatbots and voice agents on their own systems use it to solve "conversation agents are rented from several tools, so data, workflow and brand stay split across vendors"?
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 corrections after agent replies.
4. Measure, then decide. Track resolved conversations per support hour and corrections after agent 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 knowledge set and one deployment channel per pilot; final policy, pricing and escalation decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: build agents in a no-code visual builder; deploy custom chatbots for customer interactions. Support the third module with operator review: run voice agents for calls and voice interactions. 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 system integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around source-linked agent replies and completed tasks. Retain the explicit scope boundary: One approved knowledge set and one deployment channel per pilot; final policy, pricing and escalation decisions remain human.

What the build depends on. Source upload and preview, asynchronous agent jobs, editable version history, reviewer access and tested export formats. High-fidelity voice and multi-channel work 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 deployment channel per pilot; final policy, pricing and escalation 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 agent package. Offer a monthly production allowance after repeat demand. Quote complex voice, multi-channel or specialist integration work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded source-linked agent replies and completed tasks. 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

Own one console for chatbots, voice agents and autonomous tasks instead of renting several subscriptions. Demonstrate a concrete source-linked agent replies and completed tasks using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Support and revenue teams deploying chatbots and voice agents on their own systems 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 agent replies and completed tasks from a small authorized input set, with a transparent calculation of resolved conversations per support hour and corrections after agent replies and no promised savings.

The first 30 days

1. Week 1: interview five support and revenue teams deploying chatbots and voice agents on their own systems and inspect a recent example of conversation agents rented from several tools, so data, workflow and brand stay split across vendors.
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 corrections after agent 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: Resolved conversations per support hour and corrections after agent 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

Resolved conversations per support hour and corrections after agent replies; 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 agent replies and completed tasks. 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 knowledge sources, system connections 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 revenue teams deploying chatbots and voice agents on their own systems. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Dante AI, FlowHunt, Juji, Salesforce Agentforce, WorkGPT, MindStudio, PolyAI, Paka AI and Role Model AI. Compare this product with the buyer's present method on resolved conversations per support hour and corrections after agent 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, voice minutes, 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 agent replies and completed tasks. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve customer data rights, source attribution, consent and usage permissions. Named owners approve substantive replies, task actions and escalation scope. One approved knowledge set and one deployment channel per pilot; final policy, pricing and escalation decisions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.