The technical capabilities of our team.

Machine learning, optimisation and decision models

Turning data into predictions, signals and decisions that can be measured and improved.

We design models for classification, ranking, forecasting, anomaly detection, vision, time series, optimisation and decision-making over structured and unstructured data. We select models and methods according to the real problem, the cost of error, uncertainty, latency and the ability to measure the result afterwards.

AI architecture and advanced systems

Turning new AI capabilities into architectures that can be integrated, operated and evolved.

We design architectures for models, agents, inference, distributed systems, cloud and edge, runtimes and components of our own. We evaluate emerging technologies, build prototypes when necessary and take the ones that add value into architectures that are maintainable, observable and ready for production.

Software and enterprise integration

Turning models and knowledge into software that works inside real systems.

We build applications, APIs, services, integrations, event-driven architectures and backend components to connect models, agents and knowledge systems with ERPs, databases, applications and existing processes. We also design the identity, permissions, contracts, state and operating mechanisms needed to take an AI system into production.

Information, semantics and knowledge

Turning scattered sources into information that is structured, connected and usable by people and systems.

We work with ingestion, structured extraction, entities, relationships, taxonomies, graphs, semantics, hybrid search, RAG, embeddings, reranking and memory. We design representations that make it possible to find, relate and reuse knowledge while preserving context, provenance and meaning.

Linguistic engineering and knowledge-based generation

We design systems capable of analysing, representing, transforming and generating language under explicit constraints of meaning, terminology, evidence, audience and context.

We combine NLP, computational terminology, semantic representation, contrastive research, localisation, NLG planning, structured generation and generative models. We design systems in which language works as a computational layer connected to data and knowledge, with controls over what may be transformed and what must be preserved.

Evaluation, observability and control

Measuring how an AI system performs, detecting where it fails and controlling what it is allowed to do.

We design evals, adversarial testing, benchmarks, simulations, validators, traces, metrics and observability mechanisms for models, agents, knowledge systems, workflows, software and physical systems. We combine automatic evaluation, deterministic checks and specialist review according to what actually needs to be measured.

Physical Intelligence

LABORATORY · CAPABILITIES UNDER EVALUATION

Extending perception, decision and action from digital systems out into the physical world.

We work with computer vision, sensing, signal fusion, state estimation, simulation, navigation, motion planning, control, manipulation and edge computing. We explore how to integrate these capabilities with models, software and operational systems in defined physical tasks and environments.