Source-linked content chatbot builder and console

For

Marketing and content teams that need a branded chatbot answering from their own material

Solves

Audience questions go unanswered or get generic web answers because content sits in sites, documents and archives with no owned assistant.

Delivers

A deployed assistant with reviewable answers, lead capture and engagement reports

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

Turn existing content into a branded, source-linked assistant without renting several chatbot subscriptions.

1. Ingest approved websites, documents and archives.
2. Build a custom chatbot from that content without code.
3. Define and adjust conversation flows and responses.
4. Answer only from the user's content, with source links.
5. Support multiple languages in conversation.
6. Apply brand color, tone and appearance.
7. Capture leads through email forms in chat.
8. Track sessions, repeat visits and engagement.
9. Handle subscription payments and creator payouts.
10. Enable paid access, subscriptions or advertising.
11. Offer pre-designed templates for content types.
12. Check content for grammar and originality.
13. Let team members collaborate on one project.
14. Adjust output length and formatting.
15. Provide debugging views of how answers are generated.
16. Export generated content to document formats.
17. Encrypt and store content on secure servers.

Everything these tools do, in one app

• AI chatbot creation Enables users to build custom AI chatbots from their content.Found in Direqt, Bookshelf, chatWise and 2 more
• No-code setup Allows users to create and deploy chatbots without any coding.Found in Bookshelf, Mottle, ChatShape
• Content integration Ingests existing content such as websites, documents, or archives to train the chatbot.Found in Direqt, Bookshelf, chatWise and 1 more
• Customizable conversation flows Lets users define and adjust the dialogue paths and responses of the chatbot.Found in Direqt
• Context-aware question answering Provides answers based solely on the user's content, not general web data.Found in Bookshelf, chatWise, Mottle and 1 more
• Multilingual support Supports interactions in multiple languages.Found in CustomGPT.ai Researcher, Mottle
• Analytics and engagement tracking Monitors user engagement metrics such as session length and repeat visits.Found in Direqt
• Subscription management Handles subscription payments and automatic payouts to creators.Found in chatWise
• Monetization features Enables revenue generation through advertisements, subscriptions, or paid access.Found in Direqt, chatWise
• Lead generation Captures leads via email input forms during chatbot conversations.Found in ChatShape
• Brand customization Allows adjustments to color, tone, and appearance to match brand identity.Found in ChatShape
• Debugging tools Provides features to track and debug how responses are generated.Found in Mottle
• Export options Enables exporting generated content to various document formats.Found in CustomGPT.ai Researcher
• Template library Offers pre-designed templates for various content types.Found in CreatorMind
• Grammar and plagiarism checking Checks content for grammatical errors and originality.Found in CreatorMind
• Collaboration features Allows multiple team members to work on projects simultaneously.Found in CreatorMind
• Customizable output length and formatting Lets users adjust the length and formatting of generated content.Found in CreatorMind
• Secure storage Encrypts and stores content on secure servers.Found in ChatShape

What goes in, what comes out

The workflow

1. In
   Start with

   Approved websites, documents and archives; brand profile and tone rules; approved conversation flows and disclosure text

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect approved websites

4. 3

   Documents and archives

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   A deployed assistant with reviewable answers, lead capture and engagement 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 content set and brand profile; final claims, tone and disclosure checks remain editorial. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Content sources and ingestion, Assistant builder and conversation flows, Review and analytics console. Use a source list with ingestion status, a central builder for flows, tone and branding, and a right-hand panel for answer evidence, review state and comments. Let users compare draft and approved answers side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant answer. Make the task-specific outcome a deployed assistant with reviewable answers visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, source versions, client 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

Buyer-owned websites, document stores and archives; brand asset libraries; email and CRM destinations for captured leads. 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: ingest approved websites, documents and archives; build a custom chatbot from that content without code. 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 marketing and content teams that need a branded chatbot answering from their own material use it to solve "audience questions go unanswered or get generic web answers because content sits in sites, documents and archives with no owned assistant"?
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: Answer acceptance rate per reviewed conversation and repeat visitor sessions.
4. Measure, then decide. Track answer acceptance rate per reviewed conversation and repeat visitor sessions; accepted-answer 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 content set and brand profile; final claims, tone and disclosure checks remain editorial. Implement one approved input format, a bounded representative case set and the first two task modules: ingest approved websites, documents and archives; build a custom chatbot from that content without code. Support the remaining modules with operator review: define and adjust conversation flows and responses; answer only from the user's content, with source links. 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 a deployed assistant with reviewable answers. Retain the explicit scope boundary: One approved content set and brand profile; final claims, tone and disclosure checks remain editorial.

What the build depends on. Source upload and preview, asynchronous ingestion jobs, editable version history, reviewer access and tested export formats. High-fidelity deployment requires specialist content QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved content set and brand profile; final claims, tone and disclosure checks remain editorial.

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 content set. Offer a monthly production allowance after repeat demand. Quote complex multi-language or monetized deployments separately. These are test prices, not market benchmarks. Package the initial sale as one bounded deployed assistant with reviewable answers. 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

Turn existing content into a branded, source-linked assistant without renting several chatbot subscriptions. Demonstrate a concrete deployed assistant with reviewable answers using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Marketing and content team professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample deployed assistant with reviewable answers from a small authorized content set, with a transparent calculation of answer acceptance rate per reviewed conversation and repeat visitor sessions and no promised savings.

The first 30 days

1. Week 1: interview five marketing and content teams that need a branded chatbot answering from their own material and inspect a recent example of audience questions going unanswered or getting generic web answers.
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 answer acceptance rate per reviewed conversation and repeat visitor sessions, 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: Answer acceptance rate per reviewed conversation and repeat visitor sessions. 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

Answer acceptance rate per reviewed conversation and repeat visitor sessions; accepted-answer rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs a deployed assistant with reviewable answers. 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 sources, brand rules and reviewed answer examples, together with reliable delivery for a narrow content niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for marketing and content teams that need a branded chatbot answering from their own material. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

CreatorMind, Direqt, Bookshelf, chatWise, CustomGPT.ai Researcher, Mottle and ChatShape are what buyers use today, each covering part of the job. Compare this product with the buyer's present method on answer acceptance rate per reviewed conversation and repeat visitor sessions. Offer one owned, source-linked workflow instead of renting several subscriptions, so content, brand and audience data stay with the buyer. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, ingestion and storage, reviewer hours, client revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of a deployed assistant with reviewable answers. Track cost per accepted answer, including correction work, unsuccessful cases and support.

Preserve brand voice, source attribution, quotation accuracy and usage permissions. Content owners approve substantive answers and publication scope. One approved content set and brand profile; final claims, tone and disclosure checks remain editorial. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.