AI technology background

AI & Automation

AI & Agentic Services

We design and deploy AI agents and automation systems tailored to your business — intelligent assistants, autonomous workflows, and LLM integrations that actually deliver.

Platform-Agnostic AI

The right AI for your use case

We work across the full AI ecosystem — cloud-hosted models from Microsoft, OpenAI, Anthropic, and Nvidia, as well as locally-deployed open-weight models running entirely within your own infrastructure. We fine-tune models on your proprietary data, build custom knowledge layers, and design multi-agent systems — selecting the right approach for each use case based on data sensitivity, sovereignty, latency, cost, and capability.

We have no platform allegiance. Our only allegiance is to what works for you.

Cloud AI infrastructure

What We Do

AI Capabilities

Architecture

Agentic Orchestration

We architect multi-agent systems where specialised agents collaborate on complex, multi-step tasks — including the control plane that routes tasks, resolves conflicts, handles retries, and ensures predictable behaviour. Built on LangGraph, AutoGen, CrewAI, and custom orchestration layers.

LangGraph AutoGen CrewAI

Data & RAG

Knowledge Layers

We build RAG pipelines, vector stores, and structured knowledge bases that give your agents accurate, up-to-date context — grounded in your organisation’s own data, documents, and processes. Agents are only as good as the knowledge they can access.

RAG pipelines Vector stores Weaviate

Governance

AI Governance & Control Planes

We define and implement policies governing how your agents behave — what they can access, what actions they take autonomously, when they escalate to humans, and how they log decisions. Governance built in from the start, aligned with the EU AI Act.

EU AI Act Audit logging Policy controls

Deployment

Cloud & Local AI

We deploy AI where it makes sense — cloud-hosted models from OpenAI, Anthropic, Azure AI, or Nvidia NIM for scale, and locally-run models (Ollama, vLLM, LM Studio) where data sovereignty, latency, or cost demands it. Many clients run hybrid setups.

Azure AI Nvidia NIM Ollama

Systems

Cohesive Multi-Agent Systems

We design coordination and constraint layers that keep all agents working towards the same goals — shared memory, consistent guardrails, unified logging, and clear escalation paths when edge cases arise. No conflicting outputs or policy violations.

Shared memory Guardrails Unified logging

Strategy

AI Strategy & Roadmap

Before building, we help you identify the highest-value AI opportunities in your organisation and the order to pursue them. We assess data maturity, infrastructure, and team readiness — mapping a practical path from first use cases to a fully governed multi-agent environment.

AI readiness Use case mapping Roadmap

Private · Controlled · Yours

Local AI, Fine-Tuning & Knowledge Architecture

Not every AI workload belongs on a public cloud. We build AI systems that run entirely within your infrastructure — customised to your domain, trained on your data, with no external dependency.

On-Premises

Local AI Deployment

We deploy open-weight large language models (Llama, Mistral, Qwen, Phi, Gemma) to run entirely within your own infrastructure — on-premises servers, private cloud, or air-gapped environments. Your data never leaves your network. No API keys, no usage logs, no third-party exposure.

We handle hardware sizing, containerisation (Docker, Kubernetes), inference optimisation, and integration into your existing workflows and applications.

On-premises Data sovereign Ollama vLLM TGI

Fine-Tuning

Model Fine-Tuning

A general-purpose model knows a lot about everything. A fine-tuned model knows your domain deeply. We fine-tune foundation models on your proprietary documents, terminology, processes, and examples — producing a model that reasons in your language, understands your context, and makes fewer errors on your specific tasks.

We use LoRA, QLoRA, and full fine-tuning depending on compute constraints and performance requirements. Models remain yours — deployable anywhere, forever.

LoRA QLoRA Domain-specific Custom training data

Knowledge

Knowledge Layer Architecture

We design and build the knowledge infrastructure that makes your AI actually useful — RAG pipelines, vector databases, structured knowledge graphs, and document processing pipelines that continuously ingest, index, and surface your organisation’s institutional knowledge.

AI agents are only as good as the context they can access. We build knowledge layers that scale from hundreds of documents to millions of records, with semantic search, metadata filtering, and access controls built in.

RAG pipelines Vector stores Weaviate Qdrant pgvector

Sensitive data? On-premise AI might be the right call.

We help you assess whether local AI deployment makes sense for your workload — technically and commercially.

Discuss your requirements

We Work With

Microsoft Azure AI OpenAI Anthropic Claude Nvidia NIM Ollama Hugging Face LangChain / LangGraph AutoGen CrewAI Custom solutions

Ready to explore AI?

Let’s map out what AI can realistically do for your organisation — no hype, just honest advice.

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