Past the demo, into production — and into governance
The question is no longer whether AI works. It is whether an AI system stays in production, keeps earning its cost, and survives an audit. That is why evaluation, guardrails and observability are part of how we build, not a later phase.
Figures from published industry research current to 2026 (Gartner forecasts; AI-search and zero-click studies). Cited because they shaped our approach — not as our own measurements.
Grouped by the job it does
Claude
Anthropic’s frontier LLM, used across our delivery and inside the products we build.
- Strong instruction-following and long-context reasoning for real business documents
- Enterprise agreements mean your prompts and data are not used to train public models
MCP
Model Context Protocol — the open standard that connects AI models to your tools and data.
- Open standard, so integrations are portable rather than vendor-locked
- Access is explicit and auditable — you can see what the assistant touched
RAG / Vector DB
Retrieval-augmented generation over your own content, powered by vector search.
- Answers stay current because you update content, not the model
- Citations make responses checkable by staff and customers
LangChain
A framework for orchestrating LLM applications and multi-step agents.
- Composable chains and agents keep complex logic maintainable
- Instrumentation makes failures diagnosable in production
Claude Code
Agentic coding — AI that reads a codebase, plans a change and implements it under review.
- Reported human intervention rates for coding agents sit near 21%, so review remains mandatory and we plan for it
- Faster delivery on mechanical work without adding contractors to your project
AI Evals & Observability
Automated evaluation, tracing and monitoring for AI features in production.
- Rollback rates reported at roughly 9% with full eval coverage versus about 47% without
- Traces make incidents explainable to auditors, not just engineers
Guardrails & AI Policy
Enforceable limits on what an AI system may say, do and access.
- Policy becomes code, so compliance is demonstrable rather than asserted
- Human-in-the-loop thresholds set per decision type, not per system
AI Visibility Tracking
Measurement of brand mentions and citation share inside AI answer engines.
- Brands cited in AI Overviews have been measured earning around 35% more organic clicks than uncited brands on the same query
- AI-referred visitors have been reported converting at multiples of traditional organic traffic, so quality matters more than volume
Power BI
Executive dashboards and business-intelligence reporting.
- Combines GA4, platform and ERP data in one view
- Scheduled refresh and row-level security for wider distribution
GA4
Google Analytics 4 for event-based measurement and attribution.
- Event-based model captures real user journeys across devices
- Feeds media platforms for better optimisation signals
React
The industry-standard library for fast, interactive user interfaces.
- Enormous ecosystem and talent pool — you can hire for it
- Component reuse enforces design-system consistency at scale
Next.js
A React framework for server-rendered, SEO-friendly, high-performance sites.
- Server rendering and static generation deliver strong Core Web Vitals
- Rendered HTML is fully readable by search and AI crawlers