AI build lifecycle
We build the modern way — Agile, made continuous by MLOps: machine learning, development, and operations as one loop, with continuous integration, delivery, and training (CI · CD · CT). Rooted in decades of systems analysis and design — not a one-pass waterfall. And a pipeline is only as good as what it delivers — used and tested in the field.
CI/CD/CT is integration, delivery, and training at the micro, system level — our Integration and Impact layers take the same discipline macro, with their own differences and specialties.
Find the real opportunity
Where AI earns its keep — a new revenue stream, or a cost and bottleneck worth removing. And the candour to say where it doesn't.
Decide where it lives
Cloud, on-prem, and/or edge — settled by a cost-benefit analysis across cost, security, and IP before a line of code is written.
Build it in-house
Domain vector databases, RAG, agents, and real-time pipelines — built on the knowledge, on the right infrastructure.
Prove the result
Impact tracked to the bottom line — with real-time measurement embedded, not bolted on. To a researcher, measurement is a given.
Used and tested in the field
A pipeline is only as good as what it ships. Not every build is custom — sometimes the wisest move is adopting the right off-the-shelf AI and delivering it well. We did exactly that with an AI-enabled security-awareness program at a large mining enterprise: deployed, used, and measured head-to-head against the conventional approach.
Explainable AI reduces phishing risk
Does explaining why help people resist a threat? Our IEEE study tested explainable AI (XAI) as the mediator between AI security training and phishing susceptibility — empirically, with 132 email users. Both AI-SETA and XAI significantly cut susceptibility.
Masialeti & Wang (2025). IEEE TPS-ISA (invited paper).