Source-linked website assistant and action console

For

Small sales and support teams that run a website and want visitors to book or buy without adding staff

Solves

Website visitors ask questions and leave without booking or buying because no one answers in time and the site cannot act on their behalf.

Delivers

Source-linked answers and completed visitor actions

Built in

about 5 weeks of creation time, MVP in 6 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

Turn website visits into booked or purchased outcomes with an assistant the team owns.

1. Embed the assistant with one script line.
2. Hold natural-language conversations with visitors.
3. Train answers on site content, docs, FAQs and past conversations.
4. Read page context and conversation history.
5. Apply custom prompts and instructions.
6. Qualify visitor intent, fit and engagement.
7. Book meetings during the conversation.
8. Execute page actions such as typing, clicking, form filling and checkout.
9. Sync conversations to CRM, calendar and payment tools.
10. Deploy beyond the website to other channels.
11. Serve visitors in multiple languages.
12. Track engagement and performance in a dashboard.
13. Run on existing or legacy website code.
14. Guide visitors through tours and multi-step tasks.
15. Generate FAQ content from site data and queries.
16. Generate articles, posts and social updates.
17. Compare the reviewed result with the recorded baseline and value assumptions.
18. Capture corrections and named-owner approval before consequential use.
19. Export a versioned source-linked answers and completed visitor actions record with source references and unresolved questions.

Everything these tools do, in one app

• One-line embed Add the assistant to a website with a single line of code or script tag.Found in Foyer, Rover by rtrvr.ai, WebAssistants.ai
• Conversational visitor engagement Starts and holds natural-language conversations with website visitors.Found in Foyer, zookish, Rover by rtrvr.ai and 3 more
• Knowledge base training Builds or is trained on your site content, docs, FAQs, and past conversations so answers fit your business.Found in Foyer, Clara AI SDR, Lumro and 1 more
• Context awareness Reads page content and keeps track of the conversation so responses stay relevant.Found in Foyer, zookish, WebAssistants.ai
• Custom prompts and instructions Lets you give the assistant specific tasks, prompts, or instructions to tailor its behavior.Found in Foyer, WebAssistants.ai
• Lead qualification Asks questions and judges visitor intent, fit, and engagement to qualify leads.Found in Clara AI SDR, Lumro
• Meeting booking Books appointments or meetings automatically during the conversation.Found in Clara AI SDR, Lumro
• Action execution Performs real actions in the page such as typing, clicking, filling forms, and completing checkout.Found in Rover by rtrvr.ai, Lumro
• CRM and tool integrations Connects conversations to systems like CRMs, calendars, and payment tools to update records and trigger workflows.Found in Clara AI SDR, Lumro
• Multi-channel deployment Places the assistant where customers already engage, beyond just the website embed.Found in Rover by rtrvr.ai, Lumro
• Multi-language support Serves visitors in multiple languages.Found in Clara AI SDR, FAQWidget
• Analytics dashboard Tracks performance and engagement metrics for the assistant or content.Found in Neexa AI, FAQWidget
• Legacy compatibility Works with existing or legacy website code and infrastructure.Found in WebAssistants.ai
• Guided workflows Runs conversational tours and multi-step task automation to reduce visitor friction.Found in Rover by rtrvr.ai
• Automated FAQ generation Creates FAQ content automatically from website data and user queries.Found in FAQWidget
• Content generation Generates written content such as articles, blog posts, and social media updates.Found in Neexa AI

What goes in, what comes out

The workflow

1. In
   Start with

   Site content, documents, FAQs, past conversations, page context and visitor messages

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect site content

4. 3

   Documents

5. 4

   FAQs and past conversations

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Source-linked answers and completed visitor actions

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three 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 website and one booking or checkout flow; final pricing, commitments and refunds remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Assistant setup and knowledge, Live conversation console, Analytics and handoff. Use a thumbnail gallery for assistants and sites, a large central conversation canvas, and a right-hand panel for sources, actions and visitor context. Let users compare assistant versions side by side. Display draft, live and paused states. Provide a client preview link with comments anchored to the relevant answer or action. Make the task-specific outcome source-linked answers and completed visitor actions visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, assistant versions, conversation logs, approval states, usage allowances, action 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 site content, documents and permitted conversation logs. Cloud storage, CRM, calendar, payment and messaging 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

   6 days

   One buyer segment, one recurring use case; first modules: embed the assistant with one script line; hold natural-language conversations with visitors. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   7 days

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

4. 4

   Full product

   2 weeks

   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 small sales and support teams that run a website and want visitors to book or buy without adding staff use it to solve "website visitors ask questions and leave without booking or buying because no one answers in time and the site cannot act on their behalf"?
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: Qualified conversations per week and completed bookings or purchases per conversation.
4. Measure, then decide. Track qualified conversations per week and completed bookings or purchases per conversation; 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 website and one booking or checkout flow; final pricing, commitments and refunds remain human. Implement one approved input format, a bounded representative case set and the first two task modules: embed the assistant with one script line; hold natural-language conversations with visitors. Support the third module with operator review: train answers on site content, docs, FAQs and past conversations. 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 and completed visitor actions. Retain the explicit scope boundary: One website and one booking or checkout flow; final pricing, commitments and refunds remain human.

What the build depends on. Site embed and preview, asynchronous conversation jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist sales QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One website and one booking or checkout flow; final pricing, commitments and refunds 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 assistant package. Offer a monthly production allowance after repeat demand. Quote complex multi-channel or specialist integrations separately. These are test prices, not market benchmarks. Package the initial sale as one bounded source-linked answers and completed visitor actions record. 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 website visits into booked or purchased outcomes with an assistant the team owns. Demonstrate a concrete source-linked answers and completed visitor actions record using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Small sales and support teams that run a website and want visitors to book or buy without adding staff 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 and completed visitor actions record from a small authorized input set, with a transparent calculation of qualified conversations per week and completed bookings or purchases per conversation and no promised savings.

The first 30 days

1. Week 1: interview five small sales and support teams that run a website and want visitors to book or buy without adding staff and inspect a recent example of website visitors ask questions and leave without booking or buying because no one answers in time and the site cannot act on their behalf.
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 qualified conversations per week and completed bookings or purchases per conversation, 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: Qualified conversations per week and completed bookings or purchases per conversation. 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

Qualified conversations per week and completed bookings or purchases per conversation; 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 and completed visitor 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 answers, action flows and review examples, together with reliable delivery for a narrow sales niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for small sales and support teams that run a website and want visitors to book or buy without adding staff. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Foyer, zookish, Rover by rtrvr.ai, Clara AI SDR, Lumro, WebAssistants.ai, Neexa AI and FAQWidget. Compare this product with the buyer's present method on qualified conversations per week and completed bookings or purchases per conversation. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, conversation processing, 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 source-linked answers and completed visitor actions. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve brand voice, source attribution, answer accuracy and usage permissions. Buyers approve substantive changes and publication scope. One website and one booking or checkout flow; final pricing, commitments and refunds remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.