The Technical Requirements for AI Discovery Points: Building Structured Endpoints That Speak to AI

Think of /ai/query, /ai/products, and /ai/company as your AI sitemap — not for humans, but for the next generation of digital assistants that will increasingly shape discovery, decisions, and demand.

As conversational AI becomes the new search layer of the web, brands are realizing that visibility now depends on being “AI-discoverable.” In other words, your business needs to be readable, structured, and queryable by systems like ChatGPT, Gemini, Claude, and Perplexity — not just indexed by Google.

To achieve this, forward-thinking companies are implementing AI Discovery Points — structured API endpoints designed to deliver verified, machine-readable information directly to AI assistants. These endpoints create a transparent, dynamic bridge between your data and AI models, ensuring that when users ask questions about your business, the answers are accurate, up-to-date, and sourced directly from you.

Below, we’ll break down the technical requirements for three of the core Discovery Points:

• /ai/query — General business questions
• /ai/products — Product catalog and pricing
• /ai/company — Brand story and credentials
  

/ai/query — Answering General Business Questions

The /ai/query endpoint acts as your AI-accessible FAQ. It’s designed to handle broad questions about your company, services, policies, and operations — the same types of questions customers might ask on Google or a chatbot, but optimized for AI retrieval.

Core Technical Requirements

• Input Schema:
  A query string parameter (e.g., "What are your business hours?") that can accept natural language input.
• Response Format:
  Responses should be returned in a structured JSON object that includes:
  { "question": "What are your business hours?", "answer": "We’re open Monday to Friday, 9am–6pm SAST.", "source": "https://yourdomain.com/contact" }
  
• Consistency and Versioning:
  Ensure the endpoint returns consistent fields with predictable keys. Versioning your API (e.g., /v1/ai/query) helps future-proof integrations.
• Data Origin Transparency:
  Include a "source" or "reference" field linking back to your verified page. This is essential for citation-friendly AI answers, as many models prioritize sources with traceable origins.
• Latency:
  Keep response times under 300ms. AI crawlers and agents typically deprioritize slow endpoints.

Pro Tip:

Enrich the answers with contextual tags (e.g., category: "support", intent: "customer service") to help AI models infer the nature of your content faster.

2. /ai/products — Product Catalog and Pricing

If /ai/query handles the “who” and “why,” then /ai/products delivers the “what.” This endpoint exposes your product data — names, descriptions, specifications, prices, availability — in a structured, AI-readable format.

Core Technical Requirements

• Endpoint Behavior:
  Returns a list of products or a single product by ID or query string.
  
  { "products": [ { "id": "p123", "name": "YOUR URL", "description": "YOUR VALUE PROPOSITION AND SERVICES.", "price": 49.00, "currency": "USD", "availability": "In stock", "category": "Your Niche", "url": "https://yourdomain.com/products/your-product" } ] }
  
• Structure:
  Stick to consistent key names (name, description, price, currency, etc.) so that AI assistants can map them easily.
• Dynamic Data:
  Use real-time or frequently updated data, ideally cached for efficiency. Static or stale pricing data can mislead AIs and hurt credibility.
• Metadata for AI Crawlers:
  Include last_updated timestamps and structured schema.org markup (if the same data powers your site). These signals improve discoverability and trust.
• Scalability:
  If your catalog is large, implement pagination or cursor-based queries (/ai/products?page=2) to avoid timeouts.
  

Pro Tip:

AI assistants prioritize clarity and precision. Avoid embedding marketing fluff inside product descriptions — keep these factual and specification-driven. Save storytelling for /ai/company.

3. /ai/company — Brand Story and Credentials

While the previous endpoints are transactional, /ai/company is relational. It communicates your brand’s story, credentials, and authority — the credibility layer of your AI presence.

Core Technical Requirements

• Static yet Verifiable Data:
  This endpoint should return verified company details: { "name": "LightSite", "founded": "2023", "headquarters": "Cape Town, South Africa", "mission": "To make businesses discoverable and measurable inside AI ecosystems.", "credentials": [ "Google Cloud Partner", "ISO 27001 Compliant" ], "leadership": [ {"name": "Theunis Stoffberg", "role": "CEO"} ], "website": "https://yourdomain.com", "contact": "support@yourdomain.com" }
• Human and Machine Readable:
  Keep fields short, declarative, and supported by verifiable links where possible (e.g., to certification pages or press releases).
• Update Cadence:
  Refresh data quarterly or when credentials change — AI models increasingly rely on timestamp freshness to assess accuracy.
• Trust and Traceability:
  Include your domain as the canonical source. A verified JSON endpoint hosted under your domain signals authenticity to AI crawlers.

Pro Tip:

Include an optional "narrative" field with a short brand paragraph. This gives AI models more context to frame who you are — useful for brand summaries and bio-style responses.

Bringing It All Together

Implementing these endpoints isn’t just about technical compliance — it’s about owning your narrative in the AI economy. When conversational agents pull information directly from your verified data, you:

• Reduce misinformation or outdated citations.
• Increase visibility in AI-powered search.
• Build trust by being the authoritative source of truth.