AI engineering, in plain English.
The terms that come up in every scoping call, defined the way we’d explain them out loud, not the way a vendor glossary usually does.
AI Engineering Company
A software engineering firm that specialises in building, deploying, and maintaining production-grade AI systems. Distinct from AI consultancies, which stop at strategy or a pilot, and from systems integrators, which wire together other vendors' products without owning the outcome.
Read the full definition →Production AI
An AI system that runs continuously inside a live business process, serving real users on real data, with the monitoring, governance, and reliability a business depends on. The opposite of a pilot: not a demo, not a proof of concept, and no longer waiting on a green light.
AI Pilot
A time-boxed proof of concept that tests whether an AI use case works under controlled conditions, usually with a small user group and no production commitments. Most enterprise GenAI pilots never move past this stage into production — the gap Inference Group exists to close.
Vibe Coding
Building software quickly and informally with an AI coding assistant, optimised for a working demo rather than production requirements like testing, security, and monitoring. Vibe coding is how good prototypes get born; engineering is how they grow up.
How Build takes a prototype to production →MCP (Model Context Protocol)
An open standard that lets an AI application connect securely to external tools, data sources, and systems, so it can act on live information rather than a fixed prompt. Inference Group's architects build on MCP so a system integrates with a client's existing stack instead of duplicating it.
Token Cost Governance
Tracking and controlling what a production AI system costs to run in tokens, alongside the value it delivers and the risk it carries. Without it, AI spend stays invisible until the invoice arrives. With it, cost becomes a number leadership can check every month, not a surprise at renewal.
How Scale & Govern tracks run cost →AI Readiness Assessment
A structured review of which AI use cases in a business are worth building, scored by ROI, risk, and delivery cost against the organisation's actual data and environment. Inference Group's version of this is the AI Opportunity Assessment: a paid engagement that produces a costed, sequenced roadmap in days.
How Assess works →Data Foundations
The state of an organisation's data: its accessibility, quality, and governance, that determines whether a given AI use case can actually be built. A data foundations assessment surfaces what's missing before a production system is scoped, so the build plan doesn't stall on data that was never fit for purpose.
ROI-Modelled Roadmap
A costed, sequenced plan of AI use cases, each scored for projected return, risk, and delivery cost, so a business can decide what to build first on numbers rather than enthusiasm. The primary deliverable of an AI readiness assessment.