Key concepts.
Common questions.
Definitions of the terms we use and answers to the questions buyers most commonly ask.
Key terms
Definitions, not marketing copy.
Forward-Deployed AI Engineer
An engineer who embeds directly inside a client's environment — working within their production systems, data infrastructure, and organisational constraints — to build and ship AI in live environments. Distinct from traditional consulting, which delivers specifications from a distance. The term originated at Palantir and has become the defining model for enterprise AI deployment.
RAG Pipeline
Retrieval-Augmented Generation combines a large language model with a retrieval system — typically a vector database — so the model answers from a specific, controlled knowledge base rather than general training data. RAG pipelines prevent hallucination, enable source citation, and keep AI current without retraining the underlying model.
AI Readiness Audit
A structured diagnostic engagement — typically 2–3 weeks, fixed price — that maps the expertise inside a business, audits existing data assets, identifies where AI creates the most measurable value, and produces a prioritised action plan. It precedes any build and requires no commitment to implementation.
Common questions
Answers to what buyers ask first.
What is a forward-deployed AI engineer?
A forward-deployed AI engineer embeds directly inside a client's environment — working within their systems, alongside their team — to build and ship AI in production. Unlike traditional consulting, we don't build from a distance or hand off a specification. We navigate your actual infrastructure, legacy systems, and organisational constraints to deliver working AI in production.
What is an AI Readiness Audit?
A fixed-price, 2–3 week diagnostic that maps the expertise inside your business, audits your data assets, and identifies where AI creates the most value. Output is a one-page scorecard and a prioritised action plan. No build commitment required. The audit covers three phases: Domain & Expertise Mapping (who holds the knowledge, what processes it underpins), Data Audit (what data assets exist, what condition they are in, what gaps would block an AI build), and Use Case Prioritisation (a costed action plan ranking AI opportunities by value and feasibility). You keep the output regardless of whether you proceed with a build.
How long does an AI implementation take?
Discovery and expertise mapping takes 1–3 weeks. The forward-deployed build phase — delivering a working AI system in your environment — takes 4–8 weeks. A functional system is typically live within 8 weeks of starting. Expansion and optimisation continues from month 3 onwards.
Who owns the AI system after you build it?
You do. All IP, source code, and systems belong exclusively to you on completion. No lock-in, no proprietary black box, no ongoing dependency on Nomicore. You have read access to the version-controlled source code throughout the project and full access at each milestone.
What industries do you work with?
We have delivered AI engineering across three verticals: niche B2B manufacturing, specialist e-commerce, and AI platform development for technical documentation. We have pre-built Industry Accelerator packages for each — meaning you don't pay for us to learn your industry.
Technologies & systems