AI Consulting · London & UK
I provide AI consulting for UK companies — from AI strategy and LLM & RAG architecture to agentic AI, AI & agent evaluation, production deployment and AI team enablement. I'm Yuliya Halavachova, a practising AI Engineering Manager leading 15 engineers on an enterprise B2B SaaS product, and founder of UltraScout AI, with 15+ years in IT. Engagements run remotely across the UK and in person in London.
Most AI advice comes from people who either sell a platform or have never run an AI system in production. I do this work every day: as an AI Engineering Manager at a global enterprise software company, I lead a team of 15 engineers building and operating AI agents in a B2B SaaS product used by some of the world's largest enterprises. As the founder of UltraScout AI I build LLM and agent-based products end to end.
That means you get advice grounded in what actually works at scale — architecture, evaluation, cost, security, and the team and process needed to keep it running — not a slide deck.
New to working with a consultant? Read what an AI consultant actually does — and when you don't need one.
For leadership teams deciding where AI actually pays off. I assess your product, data and team, separate high-value use cases from demos that will never reach production, and turn the shortlist into a sequenced roadmap with build-vs-buy decisions, cost estimates and risks spelled out.
Design of LLM-powered features that hold up with real users: retrieval-augmented generation over your documents and data, model and vendor selection, prompt and context design, and an evaluation framework so quality is measured rather than guessed. Suitable for greenfield builds and for rescuing a prototype that stalled.
Hands-on design of AI agents that call tools, use your APIs and complete multi-step tasks — with the orchestration, observability and human-in-the-loop controls needed to run them safely. I work with LangGraph, Google ADK, MCP and A2A, and help you decide when an agent is the right tool and when a simpler workflow is better.
Most AI features ship without a reliable way to tell whether they are getting better or worse. I design evaluation systems for LLM and RAG applications — test sets, metrics, LLM-as-judge and human review — so quality, safety and regressions are measured on every change, not discovered by customers.
Agents fail differently from chatbots: wrong tool calls, loops, lost context, unsafe actions. I evaluate agents on whole tasks, not single answers — checking tool use, trajectory, task completion, cost and failure recovery — and build the harness your team uses to test agents before and after every release.
Agent skills — reusable instructions, tools and scripts that agents load on demand — only help if the agent picks the right skill and follows it correctly. I test skills for triggering accuracy, instruction-following and output quality, compare runs with and without each skill, and help you decide which skills to keep, fix or retire.
Enterprise customers now ask hard questions before they let AI near their data. I help you design AI products that pass those reviews: guardrails against prompt injection, data leakage and harmful output, clear human oversight for agent actions, and the documentation security and procurement teams expect — aligned with UK GDPR, ICO guidance and the EU AI Act where it applies.
Taking AI from notebook to a reliable production service. I cover deployment architecture on AWS or GCP, CI/CD for models and prompts, monitoring, drift and cost control — the engineering work that decides whether an AI feature survives its first months in production.
An independent, senior review of an AI system you already have or are about to build. I examine architecture, data flow, evaluation, security and cost, then deliver a written report with prioritised findings — useful before a major release, a funding round, a vendor commitment or a scale-up in usage.
AI products fail more often on scoping than on models. I help product and delivery teams define AI features users actually adopt, set success metrics that go beyond demo quality, plan delivery around the uncertainty of AI work, and bring stakeholders along from prototype to launch — the same work I do leading AI delivery for enterprise customers.
Help building or upskilling the team that will own your AI systems. Drawing on my experience managing AI engineering teams and hiring in the UK market, I help define roles, run hiring loops, set up engineering practices for LLM work, and deliver hands-on training for existing engineers.
01
Discovery call
We discuss your goals, current systems, team and constraints. You leave with an initial view on where AI fits.
02
Scope & quote
A short written proposal: objectives, deliverables, timeline and a fixed or day-rate quote.
03
Delivery & handover
Workshops, architecture, reviews or hands-on build — ending with documentation your team can own.
Stack: LangChain, LangGraph, Google ADK, MCP, A2A, Pydantic, LangFuse, vector databases, AWS Bedrock & SageMaker, GCP Vertex AI, MLflow, Docker, Kubernetes, FastAPI, Python. Full background →
An AI consultant helps a company decide where AI creates real value and then designs how to build it. That ranges from strategy and use-case selection to technical architecture for LLM, RAG and agent systems, production deployment, and hiring or training the team that will run it.
It depends on scope. A focused piece of work such as an architecture review is quoted as a fixed engagement, while ongoing advisory or hands-on delivery is priced by the day or on a retainer. Every engagement starts with a discovery call, after which you receive a written scope and quote.
UK AI consulting prices explained →Most companies should end up owning their AI systems in-house. A consultant is most useful at the start — to avoid expensive architecture mistakes — and at key checkpoints such as a first production launch. The goal of my engagements is to leave your team able to continue without me.
AI consulting focuses on decisions: what to build, how to architect it, how to evaluate it and who should build it. AI development is the implementation itself. I do both — advisory engagements, and hands-on design and prototyping alongside your engineers where that is the faster route.
An architecture review or strategy sprint usually takes one to three weeks. Designing and prototyping an LLM or RAG system typically takes four to eight weeks. Ongoing advisory for engineering leadership runs month to month.
Yes. Engagements run remotely across the UK and internationally, with in-person workshops available in London.
Planning an AI project?
Tell me what you're building and where you're stuck and I'll get back to you personally.
Looking for career help rather than company consulting? I coach professionals moving into AI or growing with AI — see career coaching for AI careers.