Model-agnostic by design, because this field moves every quarter.
Anything built tightly around one provider's API will need rebuilding when the pricing, the capability or the terms change, and one of those changes every few months. We build behind an abstraction so switching is a configuration decision.
- First question
- What can your team operate?
- Second
- What can you hire for locally?
- Third
- What does the existing estate already run?
- Rarely decisive
- Benchmarks and framework fashion
The right technology is the one you can still run in five years.
The interesting engineering in an AI system is almost never the model call. It is retrieval quality, evaluation, cost control, permission handling and the fallback path when the provider has an outage.
We hold no allegiance to a vendor. Where confidentiality or data residency requires it we run open-weight models inside your own environment, and where a commercial API is acceptable we use zero-retention terms.
Every engagement is delivered with your engineers in the repository from the first week, so the knowledge accrues internally, not leaving when we do. You own the code, the pipelines and the documentation regardless of how the engagement ends.
If you are weighing a decision and want a second opinion instead of a proposal, we are happy to give one.
OpenAI, Claude & Gemini
Commercial frontier models where capability matters most and the data can leave your boundary under appropriate terms. Routed per task, because the best model for extraction is rarely the best for reasoning.
- Provider abstraction so models can be swapped without a rewrite
- Task-matched routing between cheaper and stronger models
- Zero-retention terms and prompt-level data minimisation
- Fallback across providers for availability
TensorFlow, PyTorch & Keras
Classical and deep learning where a trained model beats a prompted one — forecasting, classification, computer vision and anomaly detection on your own data.
- Forecasting and demand prediction
- Classification, scoring and anomaly detection
- Computer vision for inspection and document processing
- Model training, versioning and serving infrastructure
LangChain & LlamaIndex
Useful building blocks for retrieval and orchestration, used selectively. We are careful about depending too heavily on fast-moving frameworks in production systems.
- Retrieval-augmented generation pipelines
- Document ingestion, chunking and vector indexing
- Tool calling and multi-step agent orchestration
- Evaluation harnesses for retrieval and answer quality
Azure AI
The pragmatic route for Microsoft estates — models inside your own tenancy, under your existing agreements, with identity and compliance already solved.
- Azure OpenAI within your tenancy and network boundary
- Azure AI Search for enterprise retrieval
- Document Intelligence for forms and contracts
- Integration with Entra ID and Microsoft Purview
Need engineers on your own team instead?
Named individuals you interview, on monthly rolling terms. Each of these has a page explaining what separates a good hire from an average one in that stack.