Source-linked website support assistant console

For

Support and operations teams answering customer questions from their own website and documents

Solves

Customer questions are answered inconsistently across channels, and teams cannot show which document supported an answer or hand a conversation to a person with context.

Delivers

Source-linked answers, pinned replies and human 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

Reduce repeated support answering while keeping every reply traceable to an approved source.

1. Crawl approved website pages and sync documents.
2. Train answers on PDFs, FAQs and documents.
3. Interpret customer questions in everyday language.
4. Generate answers with source citations.
5. Enforce pinned replies for sensitive questions.
6. Apply brand voice and industry wording rules.
7. Support multiple languages.
8. Embed the widget on a website by code snippet.
9. Design custom conversation flows and logic.
10. Capture leads when the assistant is offline.
11. Hand off to a human agent with transcript and summary.
12. Run on website, social and messaging channels.
13. Re-crawl and re-sync on a schedule.
14. Show interaction and user-behavior analytics.
15. Fine-tune on approved domain data.
16. Compare the reviewed result with the recorded baseline and value assumptions.
17. Capture corrections and named-owner approval before consequential use.
18. Export a versioned source-linked answer set with source references and unresolved questions.

Everything these tools do, in one app

• Website and document training Trains the chatbot on content from websites, PDFs, FAQs, and documents so it can answer using your own information.Found in Owlish, Brainybear.ai, Dhibot and 3 more
• Natural language understanding Interprets user questions in everyday language to provide relevant answers.Found in ChatWebby AI, Dhibot, Chatboat and 1 more
• Easy website integration Embeds the chatbot on a website with minimal technical effort, often via a code snippet.Found in ChatWebby AI, Dhibot, Norby AI and 2 more
• Customizable responses Lets businesses adjust the chatbot's replies to match their brand voice, industry, or specific use cases.Found in ChatWebby AI, Dhibot, Chatboat and 1 more
• Analytics dashboard Provides insights into chatbot interactions and user behavior to monitor performance.Found in ChatWebby AI, Chatboat
• Multilingual support Enables the chatbot to communicate with users in multiple languages.Found in Wonderchat, Lemchat
• Source citations Shows the exact pages or documents used to form each answer, increasing transparency.Found in Owlish
• Human handoff Transfers conversations to a human agent when needed, providing context like transcript and summary.Found in Owlish
• Pinned replies Allows teams to enforce exact wording for high-stakes or sensitive questions.Found in Owlish
• Automatic content syncing Regularly updates the chatbot's knowledge by re-crawling websites or syncing documents.Found in Owlish, Wonderchat
• No-code setup Enables users to build and deploy a chatbot without programming knowledge.Found in Norby AI, Brainybear.ai, Wonderchat
• Free message credits Offers initial free interactions to test the chatbot before committing to a paid plan.Found in Brainybear.ai, Lemchat
• Lead capture Automatically collects contact information from users when the chatbot is offline or during interactions.Found in Lemchat
• Multi-channel support Allows the chatbot to operate on websites, social media, and messaging apps.Found in Chatboat, Norby AI
• Customizable conversation flows Lets businesses design specific paths and logic for chatbot interactions.Found in Chatboat
• Fine-tuning Supports training the model further on domain-specific data to improve accuracy.Found in DialogGPT
• Open-source availability Provides access to the model's code for customization and integration.Found in DialogGPT
• Security measures Protects data with encryption and access controls.Found in Dhibot

What goes in, what comes out

The workflow

1. In
   Start with

   Approved website pages, PDFs, FAQs, documents, brand voice rules and escalation policies

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect approved website pages

4. 3

   PDFs

5. 4

   FAQs

6. 5

   Documents and brand voice rules

7. 6

   Then follow this sequence: 1

8. Out
   Finish with

   Source-linked answers, pinned replies and human handoffs

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 source set and brand voice guide; final policy wording 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: Knowledge sources and training, Editable answer preview, Live conversations and handoff. Use a source list with crawl and sync status, a central answer canvas showing the cited passage, and a right-hand panel for brand voice, pinned replies and escalation rules. Let users compare draft and approved answers side by side. Display draft, changes requested and approved states. Provide a client-facing widget preview with comments anchored to the relevant answer. Make the task-specific outcome source-linked answers, pinned replies and human handoffs visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, source versions, crawl and sync schedules, client comments, approval states, usage allowances, message 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

Company-owned websites, document stores, help desks and messaging channels. Cloud storage, CMS import/export and support inbox 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: crawl approved website pages and sync documents; train answers on PDFs, FAQs and documents. 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 operations teams answering customer questions from their own website and documents use it to solve "customer questions are answered inconsistently across channels, and teams cannot show which document supported an answer or hand a conversation to a person with context"?
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: Accepted answers per support hour and corrections after publication.
4. Measure, then decide. Track accepted answers per support hour and corrections after publication; 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 source set and brand voice guide; final policy wording and escalation decisions remain human. Implement one approved input format, a bounded representative question set and the first two task modules: crawl approved website pages and sync documents; train answers on PDFs, FAQs and documents. Support the remaining modules with operator review: interpret customer questions, generate answers with source citations, enforce pinned replies. 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 question volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around source-linked answers, pinned replies and human handoffs. Retain the explicit scope boundary: One approved source set and brand voice guide; final policy wording and escalation decisions remain human.

What the build depends on. Source upload and preview, asynchronous crawl and sync jobs, editable version history, reviewer access and tested export formats. High-fidelity support requires specialist policy QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved source set and brand voice guide; final policy wording 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 source package. Offer a monthly message allowance after repeat demand. Quote complex multi-channel or fine-tuning work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded source-linked answer set with pinned replies and handoff rules. 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 support answering while keeping every reply traceable to an approved source. Demonstrate a concrete source-linked answer set with pinned replies and handoff rules using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Support and operations teams 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 answer set with pinned replies and handoff rules from a small authorized input set, with a transparent calculation of accepted answers per support hour and corrections after publication and no promised savings.

The first 30 days

1. Week 1: interview five support and operations teams answering customer questions from their own website and documents and inspect a recent example of inconsistent answers and missing source evidence.
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 accepted answers per support hour and corrections after publication, 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: Accepted answers per support hour and corrections after publication. 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

Accepted answers per support hour and corrections after publication; 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, pinned replies and human 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, pinned wording, escalation rules and review examples, together with reliable delivery for a narrow support niche. Build a permissioned library of representative question cases, reviewer corrections and verified operating constraints for support and operations teams answering customer questions from their own website and documents. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

ChatWebby AI, thinkstack.AI, Owlish, Brainybear.ai, Dhibot, Wonderchat, Norby AI, DialogGPT, Chatboat and Lemchat, plus generic website chat widgets and manual support inboxes. Compare this product with the buyer's present method on accepted answers per support hour and corrections after publication. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Crawl and sync runs, model calls, storage, reviewer hours, client revision rounds and licensed source documents. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of source-linked answers, pinned replies and human handoffs. Track cost per accepted answer, including correction work, unsuccessful cases and support.

Preserve source attribution, quotation accuracy and usage permissions. Named owners approve substantive answer changes and publication scope. One approved source set and brand voice guide; final policy wording 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.