AI Consulting
AI agents powered 20% of retail sales last holiday season. This is not experimental.
LLM outputs are probabilistic. The same prompt gives different results each time, and most businesses deploying AI treat it as a novelty rather than infrastructure. Agentic commerce is already production-grade: AI agents drove 20% of retail sales during the 2025 holiday season. Multi-agent orchestration is moving from experimental to deployed. We help teams identify high-value automation opportunities, build custom AI agents grounded in proprietary data via RAG systems, and deploy production workflows that genuinely reduce costs. For Jersey businesses, the cost of inaction is compounding. Entity authority in AI training data favours early movers, and that advantage becomes structurally difficult to reverse.
20%
of retail sales during the 2025 holiday season were powered by AI agents
20%
consistency rate. The same prompt gives consistent brand mentions only 1 in 5 times.
67%
of AI citations 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 is collapsing to 1-2 interactions with an AI agent. During the 2025 holiday season, AI agents powered 20% of retail sales. This is not experimental. Multi-agent orchestration, tool-calling workflows, and AI-mediated purchase decisions are production-grade infrastructure. Businesses that build for agentic interfaces now are positioned where the transaction layer is moving.
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 requires 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 becomes increasingly expensive for competitors to displace.
For Jersey businesses across financial services, legal, and professional services, the cost of inaction is compounding. No local competitor in the Channel Islands is building production AI infrastructure yet. The firms that deploy AI agents, establish entity authority, and build agentic interfaces now will set the benchmark in a market where relationships and reputation concentrate quickly.
Platforms & Technologies
Can't see a platform on your tech stack? Get in touch to see how we can support you.
What We Do
Expert AI Consulting strategy to execution.
AI Strategy and Roadmap
Assessment of your workflows and operations to identify the highest-value automation opportunities. Not every problem needs AI, and our discovery process separates genuine ROI from hype, producing a prioritised roadmap with expected return per initiative.
Custom AI Agents
Purpose-built AI agents that handle repetitive tasks, content generation, data analysis, and customer interactions. Grounded in your proprietary data via RAG systems for accuracy. Deployed to production with monitoring and guardrails, not just prototyped in a demo.
Workflow Automation
End-to-end automated workflows connecting AI to your existing tools. Email triage, report generation, lead scoring, and content pipelines that run autonomously. Multi-agent orchestration for 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 with fewer resources. This 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 ensure consistent output quality across your use cases. We build prompt libraries with version control and regression testing, treating prompts as code rather than afterthoughts.
AI Training and Enablement
Hands-on workshops for your team covering AI tools, prompt engineering best practices, and how to evaluate and deploy AI solutions responsibly. The goal is independence. Your team should be able to identify and implement AI opportunities without ongoing consultancy.
Our 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, not a generic capabilities deck.
Proof of Concept
Rapid prototyping of the top-priority use case. Working demo in 1 to 2 weeks that validates the approach with 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 grounded in your proprietary data for accuracy. Deployed to your infrastructure or our managed environment.
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 over time.
Deployment and Iteration
Production deployment with monitoring, alerting, and continuous improvement. Regular model evaluation and prompt refinement based on real-world usage data. AI systems improve with use, but only if you measure.
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 holiday season, so for the right use cases it is well past experimental. But not every problem needs AI. Our discovery process is designed to identify where AI genuinely adds value and where simpler automation or process changes are more appropriate. We only recommend AI where the 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, not the other way round.
We implement strict data governance: no training on your data without consent, enterprise API agreements with model providers, and on-premise deployment options for sensitive data. Every implementation includes a data flow audit. For Jersey financial services firms, we account for the regulatory requirements specific to the Channel Islands jurisdiction.
LLM outputs are probabilistic. Mistakes are not bugs, they are a feature of the technology. We build guardrails, validation layers, and human-in-the-loop review processes into every deployment. Monitoring and alerting catch quality drift in real time. AI handles the volume, humans handle the exceptions.
Yes. We build retrieval-augmented generation (RAG) systems that ground AI responses in your documentation, knowledge base, or proprietary data. This dramatically improves accuracy and relevance for your specific context without 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. You will see measurable time savings and quality improvements from the first deployment.
Entity authority compounds in AI training data. Businesses that establish structured, authoritative digital presence now are building advantages that become increasingly difficult for competitors to displace. This applies to both your own AI adoption and how AI systems represent your brand to potential customers. In Jersey's concentrated business community, early movers set the benchmark.
Get in Touch
Book a 30-minute strategy session
Tell us about your business and we'll outline how we can help you grow.
