Capabilities

Engineers embedded with the customer to design, build and ship AI systems inside real business environments, owning the outcome from scoping to production.

End-to-end delivery of AI systems into production, from requirements and architecture through integration, rollout and handover.

Engineers who join the customer's team, tools and rituals for the duration of an engagement rather than delivering from the outside.

Building AI-native products and features: UX, backend, model orchestration, evaluation and release engineering.

Pipelines, warehouses, lakehouses and data quality work that AI systems depend on.

Training, fine-tuning, serving and monitoring machine learning models in production.

Automating business processes with LLMs, agents and workflow tooling across existing systems.

AI deployments with enterprise constraints: security review, procurement, compliance, change management and scale.

Connecting AI systems to CRMs, ERPs, ticketing, messaging and internal APIs.

Multi-step, tool-using agents that take actions across systems with guardrails, evaluation and human oversight.

Applications built on large language models: prompting, orchestration, structured outputs and evaluation.

Grounding LLM outputs in company knowledge with retrieval pipelines, chunking, ranking and citation.

Conversational assistants for customers or employees across chat, voice and messaging channels.

Image and video understanding: detection, classification, OCR and visual inspection.

Classification, extraction, summarisation and language understanding beyond generic chat.

Extracting structured data from contracts, invoices, forms and unstructured documents.

Forecasting, scoring, recommendation and anomaly detection models.

LLM-powered steps inside business workflows, approvals and back-office processes.

Eval suites, regression testing, tracing and monitoring for LLM systems in production.

Real-time speech agents for phone, receptionist and call-centre use cases.

Services, queues, auth and business logic that AI features run on.

Web and mobile interfaces for AI products and internal tools.

Architecture and operations on AWS, Azure, GCP and hybrid environments.

CI/CD, infrastructure as code, model deployment pipelines and reliability.

Designing and building APIs that expose AI capabilities to other systems.

Connecting SaaS, legacy and on-prem systems reliably.

Networking, security, compute and GPU infrastructure for AI workloads.

Warehouses, lakehouses, streaming and vector stores.