AI Consulting
AI agents powered 20% of retail sales during the 2025 Christmas sales window. This is production infrastructure, not a pilot.
LLM outputs are probabilistic. The same prompt returns different results each run, and most teams still treat AI as a novelty rather than infrastructure. Agentic commerce is already production-grade. AI agents drove 20% of retail sales during the 2025 holiday season, and multi-agent orchestration is moving from experimental builds into deployed systems. We help teams score automation opportunities, build custom agents grounded in proprietary data through RAG systems, and ship production workflows with monitoring and guardrails. For Jersey businesses, the cost of waiting compounds. Entity authority in AI training data favours early movers, and that advantage is structurally difficult to reverse later.
20%
of retail sales during the 2025 holiday season were powered by AI agents
20%
consistency rate. The same prompt returns consistent brand mentions only 1 in 5 times.
67%
of AI citations are controlled by just 30 domains per topic. Entity authority compounds.
The Shift
Agentic Commerce
AI is compressing the purchase funnel. What used to take 14 clicks across search, comparison, and checkout now collapses into 1 or 2 interactions with an AI agent. During the 2025 holiday season, AI agents powered 20% of retail sales. Multi-agent orchestration, tool-calling workflows, and AI-mediated purchase decisions already run in production. Businesses that build for agentic interfaces now are positioned where the transaction layer is heading next.
Compounding Entity Authority
AI training data has a memory effect. Brands that establish structured, authoritative digital presence early get embedded in model weights and retrieval indices. Late entrants face a structural disadvantage because competing against entrenched training data takes disproportionate effort. The top 30 domains per topic control 67% of AI citations, and that concentration is self-reinforcing. Early movers compound an advantage that grows steadily more expensive for competitors to displace.
For Jersey firms across financial services, legal, and professional services, the cost of waiting compounds every quarter. No local competitor in the Channel Islands is running production AI infrastructure yet. Firms that deploy agents, establish entity authority, and build agentic interfaces now will set the benchmark in a market where reputation travels fast.
What We Do
Expert AI Consulting strategy to execution.
AI Strategy and Roadmap
We assess your workflows and operations to score the highest-value automation opportunities. Not every problem needs AI. Discovery separates real ROI from hype and produces a prioritised roadmap with expected return per initiative.
Custom AI Agents
Purpose-built agents that handle repetitive tasks, content generation, data analysis, and customer interactions. Each one is grounded in your proprietary data through RAG systems for accuracy. We deploy to production with monitoring and guardrails, rather than stopping at a demo prototype.
Workflow Automation
End-to-end automated workflows that connect AI to your existing tools. Email triage, report generation, lead scoring, and content pipelines run autonomously with human review on exceptions. Multi-agent orchestration handles complex processes that require coordination across systems.
Agency and Partner Advisory
We advise agencies and marketing partners on AI adoption, tool selection, and workflow integration. From Claude deployments to martech stack architecture, we help partners deliver more output with fewer people. That matters in a market as concentrated as the Channel Islands.
Prompt Engineering
LLM outputs are probabilistic by nature. Systematic prompt design, testing, and evaluation frameworks keep output quality consistent across your use cases. We build prompt libraries with version control and regression testing, treating prompts as code with the same review rigour.
Claude Consulting
Specialised practice for marketing teams and agencies building on Claude. Workflows, training, custom agents, and team enablement, delivered by an agency that runs on Claude itself. Built for production, not pilots.
Explore our Claude practiceOur Approach
How we deliver results.
Discovery and Assessment
We map your current workflows, identify automation candidates, and score each opportunity by expected ROI, complexity, and risk. The output is a prioritised roadmap with specifics, including named systems, owners, and expected returns.
Proof of Concept
Rapid prototyping of the top-priority use case. A working demo in 1 to 2 weeks validates the approach on your real data before committing to a full production build. Most failed AI projects skip this step.
Production Build
Full engineering build with error handling, monitoring, guardrails, and integration testing. RAG systems ground responses in your proprietary data for accuracy. We deploy to your infrastructure or our managed environment, whichever your security posture requires.
Testing and Evaluation
Systematic evaluation against accuracy benchmarks, edge cases, and failure modes. LLM outputs are non-deterministic, so we build evaluation frameworks that catch regressions and quality drift as models update.
Deployment and Iteration
Production deployment with monitoring, alerting, and a continuous improvement loop. Regular model evaluation and prompt refinement run against real usage data. AI systems improve with use, but only if you instrument them to measure it.
Your First 90 Days
What the first quarter looks like.
A phased rollout that builds momentum month by month.
Discovery and Proof of Concept
- Workflow mapping and automation opportunity scoring
- Use case prioritisation by ROI and complexity
- Proof of concept build: top-priority use case
- Data architecture and RAG strategy assessment
- Tool and platform selection
- Working demo with real data for stakeholder review
Production Build
- Full engineering build with error handling and guardrails
- RAG system implementation: grounded in proprietary data
- Integration with existing tools and workflows
- Prompt engineering with version control and testing
- Evaluation framework: accuracy benchmarks and edge cases
- Staging deployment and user acceptance testing
Deployment and Evaluation
- Production deployment with monitoring and alerting
- Team training and enablement sessions
- Performance review: time savings, accuracy, ROI
- Quality drift detection and prompt refinement
- Second use case scoping based on learnings
- Quarterly strategy review and roadmap update
Pricing
Transparent pricing. No surprises.
Strategy
- AI opportunity assessment
- Workflow mapping and analysis
- Prioritised automation roadmap
- ROI projections per initiative
- Tool and platform recommendations
- Executive presentation
Retainer
Most Popular- Dedicated AI lead
- Custom AI agent development
- Workflow automation builds
- LLM API integration
- Prompt engineering and evaluation
- Fortnightly sprint delivery
- Production monitoring
Custom
- Multi-agent orchestration
- Custom model fine-tuning
- On-premise deployment
- Team training programme
- Priority support
Frequently asked questions
with specific answers
That is the right question. AI agents powered 20% of retail sales during the 2025 Christmas sales window, so for the right use cases it is already past the experimental stage. Not every problem needs AI, though. Our discovery process identifies where AI genuinely adds value and where simpler automation or process changes would be more appropriate. We only recommend AI when ROI is clear and measurable.
We are model-agnostic and work with Claude (Anthropic), GPT-4o (OpenAI), Gemini (Google), and open-source models. We select the right model for each use case based on cost, accuracy, latency, and data privacy requirements. Model selection should follow the use case rather than the other way round.
We enforce strict data governance: no training on your data without consent, enterprise API agreements with model providers, and on-premise deployment for sensitive data. Every implementation includes a data flow audit. For Jersey financial services firms, we account for regulatory requirements specific to the Channel Islands jurisdiction.
LLM outputs are probabilistic, so mistakes are a feature of the technology rather than a bug. We build guardrails, validation layers, and human-in-the-loop review into every deployment. Monitoring and alerting catch quality drift in real time. AI handles the volume and humans handle the exceptions.
Yes. We build retrieval-augmented generation (RAG) systems that ground AI responses in your documentation, knowledge base, or proprietary data. Accuracy and relevance improve significantly for your specific context without the cost of fine-tuning a model. Your data stays under your control.
A proof of concept is typically ready in 1 to 2 weeks. Production deployment follows in 4 to 6 weeks. Measurable time savings and quality improvements show up from the first deployment onward.
Entity authority compounds in AI training data. Businesses that establish a structured, authoritative digital presence now are building advantages that grow harder for competitors to displace each quarter. This applies to your own AI adoption and to how AI systems represent your brand to potential customers. In Jersey's concentrated business community, early movers set the benchmark.
Get in Touch
Let's talk about AI Consulting
Tell us about your business and we'll outline how we can help you grow. All engagements start with a free 30 minute discovery call.
