AI and Machine Discoverability Foundation
Give search engines and AI systems a clearer map of your business.
We improve the machine-readable foundations of your website. Depending on the site, this can include structured data, organisation information, service information, product information, geographic information, policies, canonical URLs, XML sitemaps, crawler controls and machine-oriented site guidance such as llms.txt.
The AI and Machine Discoverability Foundation from Sentry Digital Services gives search engines and AI systems a clear, machine-readable map of a business (structured data, canonical URLs, XML sitemaps, crawler controls and llms.txt) so they can tell important, authoritative pages from secondary or outdated ones.
What AI and Machine Discoverability Foundation delivers
Clearer machine understanding
Improved information discovery and a stronger definition of each service and product.
Less duplication and ambiguity
Authoritative pages are marked as such, so outdated or duplicate content is not mistaken for the real answer.
Ready for AI agent actions
The structure that WebMCP forms and tools are built on.
Complex estates under control
Improved management of large and changing website estates.
Primary tools
Astro
Cloudflare
SanitySecondary tools
Sentry.io
GitHub
Looker
Anthropic Claude
OpenAI
n8nOne data layer · measured & owned
Every service creates data that is captured, measured and owned.
AI and Machine Discoverability Foundation doesn’t just run. It generates signal. We land that data in one warehouse and turn it into live KPIs tied to a named owner, so performance is attributable and acted on, never trapped inside a single tool.
Data warehouse
Supabase
Snowflake
Keboola
BigQuery KPIs, dashboards & apps
Plotly
Retool
Apache Superset Platform-agnostic: we connect what you already use and add a warehouse only where it earns its place.
Who it suits
Which websites should consider it
More than 50 important pages or more than 50 products or services; several locations, brands, divisions, customer groups or sectors; extensive technical documentation or article libraries; multiple languages; or a large or changing catalogue.
Why it pays: The more there is to understand, the more structure matters.
Manufacturers, engineering businesses, technical distributors, technology and SaaS companies, multi-division organisations, professional services, and businesses with extensive specifications or technical content.
Why it pays: Specifications and capabilities must be read as facts, not guessed.
Ecommerce, travel, hospitality, property, healthcare, education, franchises and multi-location consumer services.
Why it pays: Products, prices and locations must be unambiguous to be recommended.
Where this sits
Level 2 · Understandable on the six-level Agent-Ready Web maturity model. See all six levels →
How we select technology
We choose technology last, for the outcomes it has to earn.
Never tech for its own sake. A tool joins the stack only where it measurably protects value, cuts cost or speeds delivery. It must fit a designed system, be owned where it counts, and be measurable from day one. How we select technology →
Speed
Modern, edge-served stacks are sub-second. Slow surfaces cost conversion and SEO every single day.
Cost
Cut licence, hosting and per-task SaaS fees. Pay for capability, not lock-in or idle enterprise tiers.
Security
Flat, edge architecture with almost nothing left to attack: fewer moving parts, smaller surface.
Ownership & no lock-in
Composable and self-hosted where it counts, so you own the system, not rent someone else’s.
Measurability
Clean data and KPIs built in. You cannot improve, automate or apply AI to what you cannot measure.
Fit to process
The right tool for a designed system, selected against process readiness, so it amplifies, not adds friction.
How we deliver · DMAIC
Every AI and Machine Discoverability Foundation engagement runs on DMAIC.
Define the goal and its value, measure the baseline, analyse the real constraint, improve with a proven build, then control the gains, so results are predictable, repeatable and defensible, not down to luck.
Agree the goal, value, budget & timescale up front.
Baseline the KPIs above: current state, not guesswork.
Diagnose the real constraint and the solution needed.
Build the chosen solution; prove the uplift.
Lock in the gains; monitor and sustain them.
FAQ
Common questions
What is llms.txt?
llms.txt is a plain-text guide at the root of a website that tells AI systems what the business does and which pages matter. It is one part of the AI and Machine Discoverability Foundation, alongside structured data, canonical URLs, XML sitemaps and crawler controls.
When does a website need the AI and Machine Discoverability Foundation?
When it has more than 50 important pages or more than 50 products or services, several locations, brands or sectors, extensive technical documentation, multiple languages, or a large or changing catalogue. The more there is to understand, the more structure matters.
How does this differ from the AI Discoverability Audit?
The AI Discoverability Audit finds out how clearly AI systems understand your business and where the gaps are. The AI and Machine Discoverability Foundation is the build work that closes those gaps in the website's machine-readable layer.
Start here · the free first step
AI and Machine Discoverability Foundation starts the same way every engagement does: with Discovery.
Two distinct moves: a free, no-obligation Discovery Session to find your value at stake, then the Discovery & Blueprint: 14 structured outputs (value model, roadmap and business case) before a pound of delivery is committed.
Engaged alone or as one engine