Mach Lilies takes artificial intelligence from idea to impact: AI strategy, LLM and machine-learning systems, and the MLOps to run them. It's the engineering bench behind our agentic operations work — generative AI, RAG and agents, taken to production and operated, not stalled as a demo. Mach speed. Lily craft.
AI consulting turns artificial intelligence into outcomes — strategy, build and run, not just slideware.
Most AI projects die between the proof of concept and production. We exist to close that gap. As a senior, founder-led AI and machine-learning practice, Mach Lilies helps teams find the use cases that pay off, build generative-AI, LLM and ML systems that work under real load, and operate them with proper MLOps. Increasingly, that work leads into agentic operations — designing, governing and operating AI agents that run real workflows, which is now the core of what we do. Every engagement is staffed by principals and handed off as clean, documented systems you own.
From generative AI and LLM apps to custom machine learning and MLOps — and the agents that put them to work.
Production LLM applications — copilots, assistants, and document and content workflows — built on Claude, GPT and open-source models.
Retrieval-augmented generation over your own data: vector search, grounding, evaluation and guardrails that keep answers accurate.
Tool-using agents and workflow automation that take real actions safely — the building blocks of agentic operations.
Bespoke ML where it beats an LLM: forecasting, recommendation, classification, computer vision and NLP.
The backbone that keeps AI working: pipelines, feature stores, training, deployment, evaluation, monitoring and cost control.
Where AI actually pays off: opportunity mapping, data readiness, build-vs-buy, roadmaps and technical due diligence.
Four movements that take AI from a hypothesis to a system that keeps earning its keep.
Identify the highest-value, lowest-risk AI use cases and validate data and feasibility.
A working proof of concept in weeks, evaluated against real metrics — not a slideshow.
Engineer it to production: tested, observable, secure, scalable and documented.
MLOps: monitoring, evaluation, retraining and cost control, so quality holds over time.
We ship AI for regulated, high-stakes domains — for both venture-backed startups and enterprises.
The principals who scope your AI are the ones who build it. No hand-off to juniors.
We optimise for systems that survive real users and real load — not demo-day theatre.
Claude, GPT or open-source — we pick on cost, privacy and fit, never vendor loyalty.
Clean, documented, no lock-in. We engineer our own hand-off and your team inherits it.
AI taken to production — strategy, build and MLOps, measured against outcomes.
AI consulting is the practice of helping an organisation identify, design, build and operate artificial-intelligence and machine-learning systems that deliver measurable business value. A good AI consultancy covers the full path — from strategy and feasibility through to production engineering and ongoing MLOps — not just slideware or one-off demos.
AI consulting is the engineering foundation; agentic operations is where it pays off. The LLM, RAG, agent and MLOps work here is what we use to build and operate the governed agent workflows in our Agentic Operations and AgentOps services.
It depends on scope. Most engagements begin with a free consultation and a short, fixed-fee discovery to validate the use case, followed by milestone-based delivery priced to outcomes rather than headcount. Contact us for a tailored quote.
A working prototype is usually weeks, not months. Production-grade deployment depends on data readiness, integration and compliance, but our model is to ship something real early and harden it iteratively.
We are model-agnostic. We build with leading frontier models such as Anthropic Claude and OpenAI GPT, and with open-source models like Llama and Mistral when they fit better on cost, privacy or control. We recommend the right tool for the job rather than a single vendor.
Most use cases that need current, proprietary or factual knowledge are best served by retrieval-augmented generation (RAG), which grounds the model in your data. Fine-tuning helps for fixed style, format or narrow tasks. We often combine both — and will advise based on your data and goals.
Yes. We design for data privacy and security from the start — including private deployments, isolation of sensitive data, and provider options that do not train on your data. We work to enterprise-grade standards and align with frameworks such as ISO 27001.
Tell us what you're building. We reply to every serious enquiry within one business day — and the first consultation is free.
