Built for two readers: people and models.
Human visitors judge design, speed and clarity. Machine readers judge structure, consistency and accessibility of the underlying data. Most stacks optimize for the first and leak value through the second. Our architecture principles close that gap.
Semantics before styling
Content models describe what things are — entities, attributes, relationships — before templates decide how they look. Presentation becomes a projection of structured data.
Render parity
Every crawler gets complete server-rendered HTML. Many AI user-agents don't execute JavaScript, so client-only content is effectively invisible to them.
Composable by default
Headless CMS, PIM, DAM, search and commerce connected through APIs, so one source of truth feeds the site, structured data, product feeds and agents simultaneously.
Observable end to end
Server logs, crawler analytics, real-user performance and a governed event layer reveal exactly how humans and bots consume what you publish.
Say it in a language machines don't have to guess.
Structured data isn't a rich-snippet trick. Implemented as a single connected JSON-LD @graph with stable @id URIs, it becomes an authoritative, first-party knowledge graph of your organization — the facts a model can resolve without inference.
We model every meaningful entity — Organization, Person, Service, Product, Offer, Article, FAQ, Event, Place — and link them with explicit properties: founder, provider, about, mentions, areaServed, knowsAbout. Then we reconcile identity outward with sameAs references to Wikidata, professional profiles and authoritative directories, so the entity on your site and the entity in the model's training data converge into one.
The layers we design, build and govern.
Engagements range from a focused architecture audit to a full re-platform. Each capability below can be delivered on its own or as part of a phased roadmap.
Information architecture & taxonomy
Topic hierarchies, controlled vocabularies and URL structures derived from prompt-space research, so every important question maps to one canonical, authoritative destination. Hub-and-spoke internal linking expresses topical relationships explicitly.
Composable content platforms
Headless CMS selection and content modeling, with structured fields replacing rich-text blobs. One content graph publishes to the web, JSON-LD, product and merchant feeds, internal search and agent-facing APIs without duplication.
Rendering strategy
Server-side rendering, static generation and incremental revalidation chosen per template, delivered from edge networks. Hydration is minimized and critical content never depends on client-side execution.
Performance engineering
Core Web Vitals engineered against field data, not lab scores: image pipelines, font strategy, script budgets, third-party tag governance and long-task elimination for responsive interaction.
Data layer & tagging
A governed event schema, consent-mode integration and server-side tag management that improves data quality, reduces client weight and preserves measurement as browsers restrict third-party tracking.
Crawler & AI access governance
Deliberate robots.txt, edge rules and bot management that separate retrieval visibility from training consent — plus log-file analysis to confirm which agents actually reach your content.
One source of truth. Every surface, human or machine.
Retrieval and training are different decisions.
Blocking every AI user-agent can remove you from answers; allowing everything may hand training rights you'd rather keep. We map each agent to its documented purpose, align policy with your business goals, then verify behavior in server logs. User-agent names and purposes change — we review them on a standing cadence.
| USER-AGENT / TOKEN | ROLE | TYPICAL STANCE |
|---|---|---|
| Googlebot | Search index, incl. AI Overviews | Allow |
| Google-Extended | Control token for Gemini model use | Business decision |
| Bingbot | Bing index, grounding for Copilot | Allow |
| OAI-SearchBot | ChatGPT search results | Allow |
| ChatGPT-User | User-initiated page fetches | Allow |
| GPTBot | OpenAI model training | Business decision |
| PerplexityBot | Perplexity answer index | Allow |
| ClaudeBot | Anthropic web crawler | Business decision |
Start with an architecture review.
Crawl simulation as search and AI user-agents, render-parity diffing, log-file analysis, schema graph extraction and Core Web Vitals field review.
Target content model, entity graph specification, rendering plan per template, data-layer schema and crawler policy — documented as an implementable blueprint.
Phased build alongside your team or ours, with automated validation in CI so structure doesn't decay after launch.