Frequently asked questions.

Consider how organisations handle a new hire. Someone joins with strong general skills — analytical ability, domain training, professional experience. On day one, they can do useful work on generic tasks. But they cannot classify your expenses correctly, because they don’t know your chart of accounts. They cannot reconcile your supplier invoices, because they don’t know your contracted terms. The process of giving them that specific knowledge is onboarding. AI has the same problem: it is capable but uninformed. Onboarded AI is AI that has been given the structured business context it needs to produce the right answer for your specific organisation — not just a generically plausible one.

Every major SaaS vendor — accounting, CRM, project management, HR — is adding AI capabilities. These are real and useful, but each one can only see what lives inside that vendor’s system. Your accounting AI cannot see your payroll data, your procurement patterns, or your operational metrics. The business questions that matter most almost never live inside a single system. "What is our true cost to deliver on this project?" requires data from accounting, project management, and payroll. A structured context layer that connects information across systems makes any AI — from any vendor — more capable. The businesses that get this right will find that every tool they use becomes more effective.

Most AI feedback loops are designed for search relevance: was this result helpful? Onboarded AI learns from process accuracy: was the business logic applied correctly? When a user corrects an output — reclassifies an expense, fixes a supplier mapping, adjusts a calculation — that correction is recorded as a new rule for that specific business context. The fix applies to every future transaction matching that pattern, not just the one instance. Each correction makes the next answer more accurate, and the model of how the organisation works grows richer through use.

No. That is precisely the problem we solve. Traditional approaches to building structured business context require dedicated teams, months of work, and budgets in the millions. Our platform captures organisational knowledge automatically, as a by-product of daily operations. When business experts do their normal work — correcting classifications, mapping suppliers, confirming calculations — the platform structures that knowledge and makes it available to AI. No separate documentation effort, no consulting engagement, no knowledge engineering project.

The technology is currently in production deployment, powering business-critical operations through licensed implementations. This is Phase 1 of our three-phase strategy: structured business context delivered as semantic prompts that guide AI inference in real time. Our roadmap moves from structured prompting toward organisation-specific model adaptation, with each phase building on the context captured in the previous one.

Today, Pyxta licenses its technology to companies building applications where AI needs to produce consistently correct outputs — financial reporting, compliance, classification, reconciliation. The target is any domain where the difference between a good answer and the right answer has real consequences. As the technology matures through Phase 2 and Phase 3, the audience expands to include AI companies building the next generation of business applications, who will license the semantics-first capability directly.

No. Onboarded AI is the context layer that makes existing AI tools more effective. General-purpose models are genuinely excellent at open-ended tasks — drafting, summarising, brainstorming. The context gap only appears when AI is asked to produce a single correct answer that depends on organisation-specific knowledge. The structured business context that Onboarded AI provides can be delivered to any model, from any vendor. The investment is in the knowledge layer, not in any particular AI product.

A good answer is one that sounds plausible and lands in roughly the right range. A margin figure of 42% when the real answer is 38% will not trigger alarm bells — it’s close enough to match your intuition, formatted professionally, delivered with confidence. Without a benchmark, most people will accept it. The right answer is 38%, because the AI knew that your organisation splits certain overhead costs across projects using a method your team decided on three years ago. That knowledge is the difference, and it is exactly the kind of context that Onboarded AI captures and structures.