Is it secure? Is it governed? Can our infrastructure run it? Can our facility power it?
Kinetique answers all four — as one accountable partner.
Stitching together specialists for each layer of AI leaves the gaps no one owns. We close them.
Every layer of an AI deployment, owned end-to-end. Sequenced by where most enterprise programmes break first.
We protect the models, the data pipelines, the inference layer, and the integrations — using AI-native security tooling from the world's leading OEMs, deployed across cloud and on-premise environments.
We design, fine-tune, and implement AI systems built on your data, your infrastructure, and your risk appetite. Use-case prioritisation, model selection, RAG pipelines, and end-to-end deployment — owned by one team.
GPU cluster design, on-premise inference infrastructure, cloud vs. on-prem trade-off analysis, and end-to-end deployment. We spec it, source it, and stand it up — with no OEM bias.
ISO 42001, the EU AI Act, NIST AI RMF, and POPIA/GDPR intersections with AI data use. We translate frameworks into practical controls, audit-ready documentation, and board-level reporting — before a regulator asks.
Before you deploy, we audit your power topology, identify harmonic distortion risks, assess ride-through capacity, and model viability for BESS, solar PV, and microgrid options. Find out before it fails — not after.
A repeatable four-stage engagement. Each step owned by Kinetique, end-to-end, with measurable handoffs.
AI readiness assessment, risk mapping, infrastructure audit, regulatory exposure review.
Vendor-neutral solution design across security, hardware, and governance. Independent OEM selection.
Implementation across cloud or on-premise. Integration, hardening, change management.
Ongoing GRC, model monitoring, compliance reporting, and executive-level AI risk dashboards.
A live, anonymized view of the adversarial patterns our team is responding to across customer environments.
Adversary chained an instruction-override payload through a sanctioned third-party plugin into a customer-facing LLM. Mitigated via SDK-layer plugin-output sanitization.
Tainted records inserted upstream of a customer fine-tune job. Detected on hash-baseline drift; pipeline halted before model emission.
Multi-turn probing pattern designed to extract training-data PII. Detected on egress; redaction and rate-limit applied at the gateway.
Unmanaged LLM endpoint deployed inside a business unit, processing customer PII outside the governance perimeter. Inventoried and onboarded.
Book a 30-minute call with an architect, or tell us about your environment and we'll come back to you within one business day.