From data to deployment

Six capabilities built as intellectual property. Each one started as a problem we solved for ourselves, then turned into a repeatable, licensable asset.

Data Ingestion

RAG Architecture

Model Selection

Data Ingestion & Knowledge Engineering

Most organisations are sitting on enormous pools of unstructured data spread across documents, databases, emails, PDFs, and legacy systems. The problem is that none of it is usable by AI in its current form. Getting from raw organisational knowledge to something a model can actually work with requires serious engineering.

We build ingestion pipelines that parse, clean, chunk, and embed data from over 60 formats into vector-ready datasets. This includes document extraction, entity recognition, schema mapping, and the kind of data quality work that determines whether your AI system performs well or falls apart. It is unglamorous work, and it is the foundation that everything else depends on.

Built with: Argo Workflows, Milvus

RAG System Architecture

Off-the-shelf language models have no knowledge of your organisation, your documents, or your processes. Retrieval-Augmented Generation solves this by connecting models to your actual data, so that answers are grounded in real information rather than generated from the model’s training data alone.

We architect RAG systems from the ground up, covering the full pipeline from data ingestion through to retrieval, ranking, generation, and evaluation. This includes embedding strategy, vector database design and management using platforms like Milvus, reranking layers for precision, and the evaluation frameworks needed to measure whether your system is actually returning accurate, useful results. A RAG system that retrieves the wrong context is worse than no system at all, so we spend considerable effort on retrieval quality before we ever think about generation.

Built with: Milvus, Hugging Face

Open Source Model Selection & Deployment

The open source model ecosystem has matured rapidly. Models from the Llama, Mistral, Qwen, and DeepSeek families now rival proprietary offerings across a wide range of tasks, at a fraction of the cost, and with full control over your data and infrastructure.

We evaluate and benchmark open source models against your actual requirements, not synthetic leaderboards. The right model for your problem depends on a careful balance of accuracy, latency, throughput, and cost, and the answer is rarely the biggest or most recent release. Once we’ve identified the right fit, we handle deployment on infrastructure you control, whether that is cloud GPU instances, private clusters, or on-premises hardware. There is no vendor lock-in, no ongoing API dependency, and no requirement to send your data to a third party.

Built with: vLLM, SGLang, NVIDIA NIM, Hugging Face

Fine-Tuning & Domain Adaptation

Foundation models are generalists by design. They know a lot about everything and not quite enough about anything specific. Fine-tuning is the process of taking a strong base model and training it further on domain-specific data so that it performs with the precision and reliability that production use demands.

We fine-tune open source models using LoRA, QLoRA, and full fine-tuning approaches on cloud GPU infrastructure, working with your domain-specific data to produce models that understand your industry’s language, terminology, and context. When real-world training data is limited, scarce, or too sensitive to use directly, we combine fine-tuning with our synthetic data generation capability to build high-quality training sets that fill the gaps. The result is a model you own, running on your infrastructure, trained on your knowledge.

Built with: NVIDIA NeMo, PyTorch, Weights & Biases, RunPod, AWS

Model Evaluation & Benchmarking

Knowing whether your AI system is actually performing well is a harder problem than most people realise. Generic benchmarks tell you how a model performs on academic tasks, but they say very little about how it will handle your specific use cases, your data, and your edge cases.

We build custom evaluation pipelines that measure what actually matters for your deployment. This includes accuracy against domain-specific test sets, hallucination rates, latency under realistic load, cost per query, and regression testing to catch degradation over time. We construct evaluation datasets from real-world scenarios and synthetic data, and we use them throughout development to ensure that every model earns its way into production through rigorous, quantified testing rather than subjective assessment.

Built with: Weights & Biases

Secure & Private AI Deployment

There are many organisations that cannot, or should not, send sensitive data to third-party AI providers. Regulated industries, government agencies, and any organisation handling confidential information need AI systems that operate entirely within their own infrastructure.

We deploy open source models on private cloud, on-premises, or air-gapped environments with encrypted training pipelines and full data sovereignty. No data leaves your network, no API calls are made to external providers, and you retain complete control over your models, your data, and your infrastructure. This approach is made possible by the maturity of the open source model ecosystem. You no longer have to choose between powerful AI and keeping your data secure.

Built with: NVIDIA NIM, vLLM, AWS

Partners

Technology Stack