Agentic AI.

Custom Multi-Agent Systems

We design and build multi-agent systems where specialised agents collaborate, exchange information and coordinate complex workflows. The architecture choice matters: vertical multi-agent systems for industry-specific roles, horizontal multi-agent systems for cross-functional capabilities.

Agentic RAG

Most enterprise RAG systems are not RAG, they are search with a wrapper. Real agentic RAG retrieves, reasons, validates and iterates. We build production RAG systems using GraphRAG (knowledge-graph-grounded retrieval), multimodal LLMs (text plus image plus structured data), LLM-as-judge validation patterns, Pydantic-validated structured outputs, and feedback loops that improve retrieval quality over time.

Why this matters: a generic vector-search RAG system will give you confident-sounding wrong answers on enterprise data. Agentic RAG with proper validation and grounding will give you accurate answers with citations, and it will tell you when it does not know. The difference shows up in adoption rates and in the quality of the decisions your team makes from the system.

Custom MCP Servers

Make your APIs accessible to LLMs and natural-language interfaces by converting them to MCP (Model Context Protocol) servers. The Anthropic-published MCP standard has rapidly become the de facto way enterprises expose internal capabilities to agentic systems. We build MCP servers for your existing systems, enforcing authentication, RBAC, rate limiting and audit logging at the protocol layer.

This is the integration substrate for everything else. Once your enterprise systems speak MCP, your agents can use them, your developers can build new agentic features faster, and your governance team has a single layer at which to enforce policy. We have built MCP servers wrapping CRMs, ERPs, internal data platforms and proprietary trading systems.

Custom Utilities for Agentic AI Solutions

Agents need infrastructure beyond the model and the framework. Prompt management and versioning. Short-term and long-term memory. Tool selection and orchestration. Cost and rate-limit management. Observability tooling specifically built for agent behaviour. We build these custom utilities so your agents are not just functional but maintainable, debuggable and improvable over time.

This is the part of agentic AI that gets least attention and matters most over the medium term. The agent that demos well in week one becomes unmaintainable by month six without proper tooling underneath it. We engineer for the medium term from day one.

Agent Fine-Tuning and Feedback Loops

We refine large language models for your domain using Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF). The point is not to build a custom foundation model from scratch (almost no enterprise should do that). The point is to align an open-source foundation model to your domain, your terminology, and your decision standards.

Beyond initial fine-tuning, we build the feedback loop infrastructure that lets the agent improve from real-world outcomes. Override patterns get captured. Reasoning traces get reviewed. The model gets refined. This is how an agent goes from useful to genuinely high-performing over a six- to twelve-month deployment.

AI and AI Data Operations

The data layer underneath agentic systems is its own engineering discipline. We curate instruction, demonstration and comparison datasets to support post-training of LLMs during SFT and RLHF phases. Beyond model training, we configure high-performance inference servers (vLLM, TGI), optimise key-value caching, and select the right GPU infrastructure to host open-source models on-prem when data sovereignty requires it.

Security operations are part of the same discipline: AI red teaming, prompt injection testing, jailbreak resistance evaluation, and the audit posture regulated industries require. We do this work for our own deployments and as a standalone offering for clients with internal AI teams who need an external assessment.