Why an AI-native platform has to be designed that way from the foundation: one data model, one context surface, one set of guarantees.
9 min read
Licences are the smallest line. Here is an open, assumption-by-assumption model of what it costs to run sales, support, docs, projects and finance in five separate systems — including the line that has only existed since AI arrived.
Every vendor now says their platform is AI-native. Seven questions separate the ones where that is an architectural fact from the ones where it is a feature line — each with a concrete answer you can ask for in a single demo.
Generative AI answers questions; agentic AI takes actions — and actions are where trust is lost. What makes them safe in Cyril isn’t a guardrail prompt: it’s permission checks on the server, approval before anything runs, and a record of every run.
Cyril gives its core records an ai_context serializer — a deterministic, schema-aware view built for the model, not the database. It’s the unglamorous core of what makes AI grounding actually work.
Bolted-on AI looks impressive in demos and falls apart in workflows. The reason is structural. Here’s the engineering decision that separates AI-native from AI-added-on.
What has to be true underneath before software can safely take actions on your behalf: permissions, an audit trail, and a way back.
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How to evaluate a business platform, and what a stack of disconnected tools actually costs. Written for the person who has to decide.
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Notes from building Cyril: the decisions, the failures, and what they taught us about software an agent can operate.
LatestWhat changes when your development team is an AI
For consulting firms: winning engagements, delivering them on budget and getting paid, and what the software underneath has to do at each step.
LatestHow to run a consultancy on one system: from proposal to paid invoice
One platform for sales, projects, support, docs and financials, with the AI underneath rather than bolted on. Join the waitlist to be among the first to use it.
