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. Definitions that hold across systems can be put to work by any capable AI model, from any vendor. The businesses that get this right will find that every tool they use becomes more effective.

Most AI feedback loops are built for search: was this result helpful? Here the question is whether the business logic was applied correctly. When a user corrects an output — reclassifies an expense, fixes a supplier mapping, adjusts a calculation — the correction is applied to the definition that produced it, as a reviewed change that can be undone. From then on, every transaction of that kind is worked out the corrected way, not just the one instance, and the model of how your organisation works grows 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 knowledge-engineering project.

The platform is in commercial deployment; the first deployment runs beside a client’s existing system, checking and completing what that system produces. This is Phase 1 of a three-phase strategy: your organisation’s definitions, supplied to any capable AI model at each decision. From there the roadmap runs toward adapting models to each organisation, each phase built on the definitions the previous one captured.

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. We also work directly with organisations — businesses and government agencies — that use AI for tasks with one right answer, where the answer has to survive review. As the technology matures through Phase 2 and Phase 3, the aim is to license that capability directly to AI companies building the next generation of business applications.

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 supplied to any capable AI 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.