Services / Engineering and advisory
Custom AI engineering, from first question to production
When the problem does not fit a product, we design and build the system with you. Twelve services across four pillars, delivered by one team that stays accountable for how the system behaves in production.
01 / Decide what to build
Advise
Strategy, readiness, use-case selection and governance, so investment goes to problems AI can actually solve.
- 1.1AI Strategy & ReadinessA clear, prioritised view of where AI can create value in your business and what has to be true to deliver it.Readiness assessment reportScored opportunity portfolioReference architecture options
- 1.2Use-case Discovery & ROITurn a long list of AI ideas into a few business cases you can defend, with baselines and success measures.Current-state process mapsBaseline metrics reportFuture-state design
- 1.3Responsible AI & GovernancePractical AI governance: policies, risk assessment and controls that teams can follow without stopping delivery.AI policy and usage standardsRisk tiering and intake workflowModel and system card templates
02 / Engineer the system
Build
Agentic systems, enterprise RAG and custom AI applications, engineered with evaluation from the first sprint.
- 2.1Agentic AI & Multi-Agent SystemsAgents that use tools to complete multi-step work, engineered with limits, approvals and evaluation.Agent architecture and threat modelProduction agent services and connectorsEvaluation suite and quality dashboard
- 2.2Enterprise RAG & Knowledge AIRetrieval-augmented systems that answer from your content with citations, permissions and measured quality.Ingestion pipelines and connectorsSearch index with permission filteringAnswer service with citations
- 2.3Custom AI SaaS & Multimodal AppsAI-native products and internal applications that work with text, documents, images and audio.Product and UX specificationProduction application and APIsEvaluation and safety test suites
03 / Connect it to the business
Integrate
Workflow, CRM and ERP integration, data pipelines and API modernisation, so AI works inside the systems people use.
- 3.1AI Workflow & CRM/ERP IntegrationBring AI into the CRM, ERP and service platforms where work already happens.Integration design documentConnectors and middleware servicesEmbedded user interface components
- 3.2Data Engineering & AI PipelinesThe data foundations AI needs: reliable pipelines, clean models, governed access and lineage.Data platform architectureProduction pipelines with testsCurated data models and metric definitions
- 3.3API & Legacy System ModernisationExpose legacy capabilities through clean, secure APIs so AI and modern apps can use them safely.Modernisation assessment and roadmapAPI specifications and gateway setupIntegration services
04 / Keep it reliable
Operate
Evaluation, guardrails, observability, secure deployment and managed optimisation for AI in production.
- 4.1Evaluation, Guardrails & ObservabilityKnow how your AI systems behave: test suites, runtime guardrails, tracing and quality monitoring.Evaluation framework and datasetsCI-integrated regression testsGuardrail configuration
- 4.2Security, Privacy & DeploymentDeploy AI where your data rules require, with threat modelling, privacy controls and hardened infrastructure.Threat model and risk registerData flow and privacy assessment inputsHardened deployment configuration
- 4.3Managed AI Optimisation & SupportOngoing operation of AI systems: monitoring, model updates, cost optimisation and continuous improvement.Service level agreementMonitoring and on-call coverageMonthly service reports
Start with a conversation, not a contract
Most engagements begin with a short, fixed-price discovery that produces evidence and a scoped plan. You decide what happens next.