Custom AI solutions

Our approach

We don't start with technology — we start with your business objectives. Every AI initiative we deliver is tied to a clear ROI, whether it's cutting costs, accelerating revenue, or transforming operations.

• Business first, not technology first — we start with your business objectives (cost reduction, revenue growth, or operational efficiency) and work backwards to the right AI architecture, not the other way around.
• Enterprise-grade security by design — security, responsible AI ethics and compliance are built into every system, with custom guardrails, private model hosting and full data governance from day one.
• Scalable from day one — cloud-native architectures on AWS, Google Cloud and Azure that stay maintainable enterprise assets, not innovative prototypes that stall at scale.

Agentic AI we engineer

Agentic AI represents a significant advancement beyond conventional AI. Unlike reactive models that respond to individual prompts, agentic AI systems understand high-level objectives, decompose them into actionable steps, leverage external tools and data sources, and execute end-to-end workflows with a high degree of autonomy. The architecture is determined by the nature of the business problem, the complexity of the workflow and the compliance requirements — we design and deploy six primary types.

• Single-task autonomous agents — execute one well-defined, high-frequency business task from trigger to completion without human intervention at each step; optimised for reliability, repeatability and rapid deployment, the most accessible entry point.
• ReAct agents (reasoning + acting) — alternate between structured reasoning (evaluating the current state, identifying the appropriate action) and execution; well-suited to tasks with variable inputs where judgment and contextual interpretation are required at each step.
• Multi-agent systems — multiple specialised agents operating collaboratively, each with a defined role, goal and scope, with a central orchestrator managing delegation, sequencing and quality verification; suited to complex, end-to-end processes that would otherwise need cross-functional human teams.
• Memory-augmented agents — persistent, structured memory across interactions on vector databases and knowledge graphs, so the agent refines its understanding of business context, client preferences and domain knowledge for progressively more accurate outputs over time.
• Agentic process automation — an intelligent successor to traditional RPA that applies language understanding and contextual reasoning to interpret dynamic inputs, handle exceptions autonomously and adapt to process variations, requiring far less maintenance and delivering higher accuracy.
• Enterprise governance agents — purpose-built for regulated industries, operating within a governance framework of comprehensive audit trails, role-based access controls, human approval checkpoints for high-stakes decisions and private model hosting, to meet compliance needs in financial services, healthcare and public-sector environments.

Agentic AI frameworks we deploy

Framework selection is determined by the operational requirements of each deployment, including task complexity, latency constraints, governance obligations and existing infrastructure.

• LangGraph — preferred for stateful, multi-step agent workflows; a graph-based execution model with deterministic state management and configurable human-in-the-loop approval nodes, well-suited to workflows requiring auditability and controlled branching logic.
• AWS Bedrock Agents — preferred for enterprise deployments on AWS; fully managed, privately hosted agent execution within existing AWS infrastructure, offering native compliance controls, IAM integration and minimal data-exposure risk.
• Meta Llama 3.3 (70B) — preferred for high-performance multilingual text generation; a pretrained and instruction-tuned generative model (text in / text out).
• Gemma-2 Instruct (27B) — preferred for lightweight open-model inference and instruction following; a family of lightweight, state-of-the-art open models from Google built from the same research and technology as Gemini.
• Phi-3 — preferred for efficient small-model text and content generation; an advanced generative model for creating text, images and other content with high accuracy.
• Mixtral — preferred for scalable content creation with a Mixture-of-Experts architecture; a pretrained generative Sparse Mixture of Experts that excels at content creation, design and data-driven insights.

Measurable outcomes

Built on the world's leading AI infrastructure

Strategic partnerships with industry leaders let us build and deploy high-performance AI on a secure, scalable foundation — accelerating your time to value.

• Cloud — AWS, Google Cloud Platform and Microsoft Azure.
• Foundation models — OpenAI (GPT-4 & DALL-E), Meta Llama (open-source LLMs) and Anthropic (Claude).
• Data & vector infrastructure — Databricks, Pinecone (vector database) and Hugging Face (model hub).