Models that know
your domain.
Off-the-shelf AI gets you 60%. The last 40% is domain, data and deployment. That's the part we build.
Ten architectures. One team that ships them.
We don't pick a model and force-fit. We pick the architecture your problem deserves — and the smallest one that works.
Large Language Models
Enterprise-scale text understanding and generation — trained on your corpus, fine-tuned for your domain.
Small Language Models
Efficient, cost-effective models for focused tasks. On-device or edge-deployable.
Vision Language Models
Images + text, understood together. Inspect, describe, decide.
Multimodal LLMs
Unified processing across text, image and audio — for documents, support and media.
Generative Transformers
Custom conversational and generative AI, tuned to your brand voice and knowledge.
Diffusion Models
High-quality image, video and audio generation. Product, marketing, synthetic data.
Graph Neural Networks
Relationship and network data — fraud, recommendation, supply-chain reasoning.
Mixture of Experts
Scalable, specialised AI. Route each input to the expert that handles it best.
State Space Models
Efficient long-sequence processing. Patterns for documents, logs and time series.
Latent-Based Models
Compressed representation learning. Faster inference, smaller footprint.
Which model fits?
Five steps from data to production.
Typical timeline: 8–14 weeks. Sometimes less if the dataset is clean; sometimes more if we're also building the pipeline.
Scope
Define the task, the data, the target metric, the deployment constraint.
Architect
Pick the architecture. Defend the choice. Size it to your budget and your latency.
Train
Curate data, run fine-tunes, evaluate honestly against held-out sets.
Deploy
Quantise, serve, monitor. Integrate with your existing stack and auth model.
Improve
Feedback loop, retraining cadence, drift detection. We stay as long as you want.
Need a model that's actually yours?
Bring us your data. We'll come back with an architecture, a timeline and a number.
[email protected] →