What We Do
Our services
Five areas of deep technical expertise, covering the full lifecycle from model training to production operations.
AI Engineering
Build AI systems that actually work in production
Most AI prototypes never reach production. We close that gap by designing systems that handle real data volumes, real users and real failure modes from day one.
- ✓LLM architecture and integration — OpenAI, Anthropic, Mistral, open-weights models
- ✓Retrieval-augmented generation (RAG) pipelines with hybrid search and re-ranking
- ✓AI agent frameworks (LangGraph, AutoGen) for multi-step automated workflows
- ✓Fine-tuning, RLHF, and LoRA/QLoRA for domain adaptation
- ✓Evaluation frameworks — automated evals, human-in-the-loop, regression suites
- ✓Latency and cost optimisation — caching, model routing, quantisation
Typical engagement
What good looks like
- ● Reproducible experiments with full lineage tracking
- ● Automated training pipelines that run on a schedule or trigger
- ● Model registry with staging, canary and production stages
- ● Real-time drift and performance monitoring
- ● A/B testing infrastructure for model comparisons
- ● Feature store for consistent train/serve parity
MLOps
From experiment to reliable, repeatable production
Notebooks and ad-hoc scripts do not scale. We build the engineering infrastructure that turns your models into dependable production systems — with proper CI/CD, monitoring and rollback capabilities.
- ✓Training pipelines with Kubeflow, Airflow or Prefect
- ✓MLflow, Weights & Biases experiment tracking
- ✓Seldon, BentoML, Triton model serving
- ✓Feast, Tecton or custom feature store implementations
Generative AI
GenAI that does what it is supposed to
We build GenAI applications that are accurate, safe and useful — not demo-ware that looks good in a pitch but breaks on real user queries.
- ✓Conversational systems — chatbots, copilots, customer-facing assistants
- ✓Document intelligence — extraction, classification, summarisation at scale
- ✓Multimodal workflows — vision, audio and text pipelines
- ✓Guardrails — output filtering, PII detection, hallucination controls
- ✓Structured output and tool-use patterns for reliable downstream processing
Common patterns we build
Internal knowledge assistant
Search and summarise internal documents, policies and runbooks using RAG.
Automated document processing
Extract structured data from contracts, invoices and reports with 95%+ accuracy.
AI-augmented workflow
Route, triage and draft responses for support, compliance or operations teams.
Tech we use
DevOps
Ship faster with confidence
Good DevOps is invisible. Deployments happen without drama, rollbacks are a non-event, and developers spend time building instead of fighting tooling. We design and build the platforms that make this happen.
- ✓CI/CD pipeline design, migration and hardening
- ✓Infrastructure as code — Terraform, Pulumi, CloudFormation
- ✓Kubernetes cluster setup, autoscaling and multi-tenancy
- ✓Internal developer platforms — golden paths, self-service tooling
- ✓Supply-chain security — SBOM, signing, SAST/DAST integration
Cloud & SRE
Cloud infrastructure engineered for reliability
We design, build and operate cloud infrastructure that is resilient, cost-efficient and genuinely observable. When things break — and they will — you know about it before your users do.
- ✓Multi-cloud architecture on AWS, GCP and Azure
- ✓FinOps — cost visibility, rightsizing, reserved/spot strategies
- ✓Observability stacks — Prometheus, Grafana, Loki, OpenTelemetry
- ✓SLO definition, error budgets and incident response runbooks
- ✓On-call and incident management — PagerDuty, OpsGenie integration
Cloud providers
AWS
Full stack
GCP
AI/ML focus
Azure
Enterprise
Not sure which service fits?
Tell us what you are working on. We will give you an honest view of what the right approach looks like.
Talk to us