Generic chatbots guess
A normal chatbot can sound confident even when the source does not support the answer. That is dangerous for prices, delivery, availability, warranty, compatibility and regulated claims.
Product: Veritiana Answer Engine
Verified AI answers for business websites. No guessing. No unsupported claims.
Veritiana Answer Engine transforms website content, product data and company knowledge into a controlled answer layer that answers only inside validated business context.
Source
grounded
Validated
baseline
Refusal
when unsupported
Runtime decision
Can we answer?
Question
“Do you deliver this product to Czech Republic?”
“Do you deliver this product to Czech Republic?”
Retrieved
evidence
Delivery page + product availability + validated baseline
Delivery page + product availability + validated baseline
Status
answered
answered
Confidence
0.84
0.84
Output
Short answer with internal source traceability.
Short answer with internal source traceability.
If unsupported
The engine refuses instead of inventing warranty, price, delivery time or certification.
The engine refuses instead of inventing warranty, price, delivery time or certification.
Why this product exists
In the AI era, your website is no longer read only by humans.
Customers ask AI systems direct questions. AI systems do not browse your website patiently. They retrieve fragments, infer meaning and often answer with partial context. That creates risk: wrong answers, weak recommendations, lost sales and support load.
Search is not enough
Keyword search returns pages. Customers want direct answers. The engine must understand whether the answer is actually present and whether the business has validated that context.
Verified answers sell better
A precise answer reduces uncertainty. It helps buyers understand products, services, conditions and next steps without waiting for support or navigating many pages.
Business value
The benefit is not “AI chat”. The benefit is controlled business answering.
For sales
The engine answers practical buying questions immediately: what the product does, who it is for, what is included, how delivery works, what alternatives exist and when the customer should contact the company.
For support
Repeated questions are handled from verified source content. Unsupported questions become visible as missing topics instead of being answered incorrectly.
For management
The system shows what customers ask, which topics are covered, which topics are missing and where the website content does not support confident answers.
For agencies
It creates a concrete AI service: extract, validate, deploy, monitor and requalify client knowledge. This is more valuable than adding a generic chat widget.
Architecture
A validated baseline before any answer goes live.
The engine does not start from raw chunks alone. It first reconstructs what the business actually does, validates that baseline and uses it as the control layer for retrieval and answering.
01
Extract
Website pages, product pages, FAQ, feeds and service content.
02
Normalize
Products, services, terms, claims and structured entities.
03
Reconstruct
Business type, core offer, customers, use cases and gaps.
04
Validate
User approves or edits the business baseline before runtime.
05
Index
Evidence chunks are scoped by site and current baseline.
06
Retrieve
Candidate sources are ranked by relevance and business alignment.
07
Decide
Answerability check decides answer, clarify or refuse.
08
Answer
Precise output with internal source traceability and logging.
Core modules
Everything is built around answerability.
The key decision is not how to generate a nice sentence. The key decision is whether the system is allowed to answer at all.
Business baseline
A validated model of the business: what it sells, who it serves, what content is supported, what topics are missing and what topics must not be answered.
Query space
Likely customer questions are generated and checked against real source coverage. This exposes missing pricing, delivery, warranty, compatibility and support information.
Retrieval and reranking
The engine retrieves verified evidence and ranks it by semantic match, source reliability, freshness and alignment with the validated business profile.
Refusal logic
If content does not support the answer, the engine says so. It does not invent prices, delivery times, guarantees, certifications or availability.
Monthly requalification
The system detects website changes, updates extracted content, checks coverage again and requires approval before changing the live baseline.
Why companies should use it now
AI becomes a decision layer. Your business data must be ready.
Customers expect answers
They no longer want to search through ten pages. They ask one question and expect a usable answer.
AI needs clean context
Without a verified context layer, AI systems infer from incomplete content. That increases ambiguity and business risk.
Unsupported answers cost money
Wrong information can create support load, failed expectations, lost trust and poor conversion. Refusal is safer than hallucination.
Use cases
Where Answer Engine fits.
E-commerce
Product questions, compatibility, availability, delivery, returns and category guidance.
Service companies
Service scope, process, pricing rules, implementation steps and support questions.
Agencies
Deployable AI answer layer for client websites with auditability and monthly requalification.
Internal knowledge
Controlled answer layer for approved company knowledge, procedures and documentation.
Low-cost runtime
Lower cost comes from control, not from weaker AI.
The system narrows the problem before generation. It scopes by site, baseline, intent, retrieved evidence and answerability. That reduces unnecessary token usage and avoids broad open-ended model calls.
Scoped retrieval
Only relevant verified chunks enter the answer process.
Only relevant verified chunks enter the answer process.
Task-specific decision
The engine first decides if the question is answerable.
The engine first decides if the question is answerable.
Short final generation
The model produces concise business output, not uncontrolled conversation.
The model produces concise business output, not uncontrolled conversation.
Logged result
Every answered or refused question becomes operational feedback.
Every answered or refused question becomes operational feedback.
Deployment path
Start with one website. Build the verified answer baseline.
The first implementation should extract the site, reconstruct the business profile, validate the baseline, index evidence and launch the answer runtime with logging.
MVP scope
1. Website extraction
Collect relevant public content.
Collect relevant public content.
2. Baseline generation
Build structured business context.
Build structured business context.
3. User validation
Approve or edit before runtime.
Approve or edit before runtime.
4. Answer runtime
Answer, refuse, log and monitor.
Answer, refuse, log and monitor.
5. Monthly
requalification
Keep answers aligned with site changes.
Keep answers aligned with site changes.