AI Engineering
Applied AI and LLM integration, from proof of concept to production MLOps.
- LLM and generative AI integration
- Applied ML for existing products
- MLOps and production monitoring
CoralCloud’s AI engineering practice focuses on applied, production-grade outcomes for government and enterprise clients — not demos that never leave a notebook. We help organisations work out where AI actually pays off, build it properly under the same governance discipline as our GRC practice, and keep it running once it’s live.
What we deliver
- LLM and generative AI integration — retrieval-augmented generation, AI-assisted workflows and copilots built into existing systems, with guardrails around cost, accuracy and data handling.
- Applied machine learning — classification, forecasting and recommendation models built for a specific operational problem, not a generic model off the shelf.
- MLOps — versioning, evaluation, monitoring and retraining pipelines so a model’s performance doesn’t quietly decay after launch.
- AI opportunity assessment — a structured review of where AI is worth the investment, and just as importantly, where it isn’t yet.
Engagement models
- Discovery sprint — a fixed, short engagement to validate an AI use case before committing to a build.
- Fixed-price build for a defined AI feature or workflow.
- Staff augmentation — an AI/ML engineer embedded in your product team.
Who this is for
Government and enterprise teams with a specific AI use case to validate or ship, who want an engineering-led view rather than a vendor pitch.
Ready to explore an AI use case?
We'll give you an honest, engineering-led view of what's worth building.