Screenshot of the Source-linked website answer and explanation console interactive demo
Screenshot of the interactive demo, on sample data

Source-linked website answer and explanation console

Reduce repeat support contacts while keeping every answer traceable to a named source.

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For
Support and content teams running a public website, help centre or product dataset
Solves
Visitors cannot find answers in scattered pages and datasets, and support staff cannot see which sources were used or where the assistant failed.
Delivers
Source-linked answers, explanations and walkthroughs
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
01

What it does

Reduce repeat support contacts while keeping every answer traceable to a named source.

  1. Index approved pages, documents and dataset records.
  2. Answer visitor questions with source-linked citations.
  3. Retrieve by vector similarity across large datasets.
  4. Combine text and semantic search for retrieval.
  5. Use a knowledge graph to improve relevance.
  6. Train the assistant on company-specific content and tone.
  7. Generate simplified explanations with follow-up questions.
  8. Deploy hosted chat agents without coding.
  9. Build drag-and-drop walkthroughs without coding.
  10. Track visitor behaviour and engagement in real time.
  11. Support multiple languages for diverse visitors.
  12. Expose APIs, SDKs and widgets for integration.
  13. Tailor the interface to match branding.
  14. Sync source data into the index continuously.
  15. Process batches of queries or records at once.
  16. Hand off to a human agent with the conversation context.
  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 set of source-linked answers, explanations and walkthroughs with source references and unresolved questions.

Everything these tools do, in one app

What goes in, what comes out

What the customer puts in
  • Approved pages
  • Documents
  • Dataset records

AI drafts, people review. Source-linked assistant and administrator console.

What the customer gets
  • Source-linked answers
  • Explanations
  • Walkthroughs
02

How it works

The workflow

  1. In
    Start with

    Approved pages, documents and dataset records

  2. 1

    Confirm the buyer's problem and scope

  3. 2

    Collect approved pages

  4. 3

    Documents and dataset records

  5. 4

    Then follow this sequence: 1

  6. Out
    Finish with

    Source-linked answers, explanations and walkthroughs

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate answers, explanations and walkthroughs 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 one supported language pair at pilot; final accuracy and tone checks remain editorial. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Source library and permissions, Assistant and walkthrough builder, Live answer review and handoff. Use a thumbnail gallery for sources and agents, a large central editing canvas, and a right-hand panel for sources, constraints and comments. Let users compare answer versions 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 source-linked answers, explanations and walkthroughs visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, source versions, visitor comments, approval states, usage allowances, answer 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 website pages, help centre articles and permitted dataset records. Cloud storage, content management systems, ticketing and chat 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.

03

How we build it

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: index approved pages, documents and dataset records; answer visitor questions with source-linked citations. 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 content teams running a public website, help centre or product dataset use it to solve "visitors cannot find answers in scattered pages and datasets, and support staff cannot see which sources were used or where the assistant failed"?
  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: Self-served resolved questions per support hour and escalation rate after an answer.
  4. Measure, then decide. Track self-served resolved questions per support hour and escalation rate after an answer; 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 source set and one supported language pair; final accuracy and tone checks remain editorial. Implement one approved input format, a bounded representative question set and the first two task modules: index approved pages, documents and dataset records; answer visitor questions with source-linked citations. Support the remaining modules with operator review: vector similarity search, hybrid search, knowledge graph, custom training, explanations, no-code agents, drag-and-drop walkthroughs, analytics, multi-language, integrations, branding, syncing, batch processing and live handoff. 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, explanations and walkthroughs. Retain the explicit scope boundary: One approved source set and one supported language pair; final accuracy and tone checks remain editorial.

What the build depends on. Source upload and preview, asynchronous indexing jobs, editable version history, reviewer access and tested export formats. High-fidelity support requires specialist content QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved source set and one supported language pair; final accuracy and tone checks remain editorial.

04

Investment

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

  1. Phase 1

    MVP

    One buyer segment, one recurring use case; first modules: index approved pages, documents and dataset records; answer visitor questions with source-linked citations. Manual review in the loop.

    $13,000 · about 4 days of creation time

  2. Phase 2

    Paid pilot

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

    $13,000 · about 5 days of creation time

  3. Phase 3

    Full product

    Self-serve onboarding, billing, monitoring and the wider integration set.

    $18,000 · about 10 days of creation time

Indicative total, MVP to full product$44,000about 4 weeks of creation time · start with the MVP from $13,000

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.

StageHosting and infrastructureAI usageTotal per month
MVP and paid pilotabout 3 customers$30–$60$60–$120$90–$180
Full productabout 50 customers$110–$210$530–$1,050$640–$1,260
05

Run it or resell it

Internally

For your own team

Support and content teams running a public website, help centre or product dataset run it inside the business: approved pages, documents and dataset records in, source-linked answers, explanations and walkthroughs out, reviewed by your people.

For your clients

As part of your offer

Agencies, consultancies and software companies can offer it to their own clients under their brand. We build and maintain it; you sell and deliver it.

Your brand, or this one

Run it under your own brand, or start from this concept style.

  • primary#915f27
  • accent#5477c9
  • surface#f1ebe4
  • ink#22201e
Headings
Sora
Text
Work Sans
Voice
Warm, clear, calm under pressure
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 set. Offer a monthly production allowance after repeat demand. Quote complex multi-language, high-volume or specialist integration work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded set of source-linked answers, explanations and walkthroughs. 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 repeat support contacts while keeping every answer traceable to a named source. Demonstrate a concrete set of source-linked answers, explanations and walkthroughs using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Support and content teams running a public website, help centre or product dataset professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample set of source-linked answers, explanations and walkthroughs from a small authorized input set, with a transparent calculation of self-served resolved questions per support hour and escalation rate after an answer and no promised savings.

The first 30 days

  1. Week 1: interview five support and content teams running a public website, help centre or product dataset and inspect a recent example of visitors unable to find answers in scattered pages and datasets.
  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 self-served resolved questions per support hour and escalation rate after an answer, 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: Self-served resolved questions per support hour and escalation rate after an answer. 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

Self-served resolved questions per support hour and escalation rate after an answer; accepted-answer rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs source-linked answers, explanations and walkthroughs. 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, answer patterns and review examples, together with reliable delivery for a narrow support niche. Build a permissioned library of representative questions, reviewer corrections and verified operating constraints for support and content teams running a public website, help centre or product dataset. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Trieve Sitesearch, Trieve Vector Inference, Overlay by Crisp, Inkeep, Explainit and Vectorize 2.0, plus the buyer's present mix of site search, chat widgets and manual support replies. Compare this product with the buyer's present method on self-served resolved questions per support hour and escalation rate after an answer. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Indexing and embedding runs, 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 answers, explanations and walkthroughs. Track cost per accepted answer, including correction work, unsuccessful cases and support.

06

Safeguards

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

Get this solution built

Built for you by our AI software factory, MVP in about 4 days. Tell us about your business and how you want to run it: inside your company, or as part of what you offer your clients. We reply within one working day.

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