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Agentic AI Solutions

AI that does the work.
Not just the talking.

Most organisations run AI that produces insights. Agentic AI goes further. It takes action, resolving customer queries end to end, handling supply chain exceptions autonomously, generating compliant documentation, and escalating only what genuinely requires human judgement.

Talk to our Agentic AI team See use cases by sector

From Workflows to AI Workforces

How an Agentic AI programme works

We follow a structured deployment approach. Each phase produces something real before the next begins. No big-bang releases. No runaway scope.

Discover
Map current workflows and identify agent opportunities
Design
Define agent scope, escalation rules, and human gates
Build
Develop, test, and validate with your actual data
Deploy
Production release with monitoring, alerting, and audit trail
Operate
Continuous improvement, cost governance, and compliance

Every agent we build includes a defined human gate, a documented audit trail, and an EU AI Act risk classification. Governance is built in from the first design session, not retrofitted after deployment.

Discuss your use case

Use cases by sector

Where we have deployed agents in production.

Select your sector to see the specific agent deployments, the problems they solve, and the outcomes achieved in comparable programmes.

Customer Service
Customer Resolution Agent
Returns, order queries, and complaint handling consume 60% of customer service headcount time. Average handle time is 12 minutes. Most cases follow the same resolution path.
70%
of cases resolved without human intervention in production deployments
Commerce and Revenue
Abandoned Journey Recovery Agent
Checkout abandonment runs at 70%+ for most retailers. Standard email sequences recover 5%. An agent with live access to inventory, pricing, and customer history recovers significantly more.
18%
recovery rate from agent-led interventions with Shopify and Salesforce integration
Inventory and Operations
Inventory Exception Agent
Stock-out events cost retailers 4–8% of annual sales. Manual monitoring is reactive. By the time a buyer sees the exception, the sale is already lost.
40%
reduction in stock-out events with proactive agent monitoring and automated reorder triggering
Supply Chain
Supply Chain Exception Agent
Supply chain exceptions, late deliveries, quality holds, capacity shortfalls, require manual review from buyers who spend 40% of their time on exception management rather than strategic purchasing.
60%
reduction in manual exception handling time with automated monitoring and substitution execution
Sales and Quoting
B2B Quote Generation Agent
Complex B2B quotes require three to five days of manual work pulling pricing from ERP, availability from WMS, and configuration from product databases. Competitors respond faster.
4 hrs
average quote turnaround versus 3-5 days manual, with the same accuracy and approval routing
Data and Analytics
Retailer Data Normalisation Agent
Analyst teams spend 40% of their time normalising retailer feeds (Tesco, Sainsbury's, Amazon) before any analysis can begin. The format changes. The mapping is manual. The delay is constant.
40%
analyst capacity recovered from data preparation and redirected to commercial insight generation
Compliance and Legal
Regulatory Change Agent
New regulatory guidance arrives frequently. Legal and compliance teams read, interpret, and assess impact manually, a process that takes weeks and introduces human interpretation risk at each step.
Days not weeks
compliance cycle time reduction with agent-led reading, gap identification, and draft documentation
Customer Retention
Involuntary Churn Prevention Agent
Payment failures, wrong card on file, expired credentials, insufficient funds, cause 20–40% of subscription cancellations. Standard retry logic recovers 30% of failures. Agents recover significantly more.
20-40%
involuntary churn reduction with agent-led payment recovery and customer communication
Risk and Credit
Credit Assessment Agent
Manual credit assessment for SME lending takes 3–5 days and relies on static rule sets. Agents that draw on live data sources assess in hours with documented rationale for each decision.
Fully auditable
every decision documented with rationale, compliant with EU AI Act high-risk classification requirements
Citizen Services
Citizen Enquiry Agent
Council contact centres handle thousands of routine enquiries weekly. Council tax, bin collection, planning applications, benefits. Most follow predictable resolution paths that consume skilled staff time.
60%
of tier-1 citizen enquiries handled autonomously once the underlying knowledge base is properly governed
NHS and Healthcare
Patient Pathway Agent
Administrative tasks, referral triage, appointment management, discharge documentation, consume clinical administration time. Delays in these processes directly extend patient waiting times.
35%
reduction in administrative processing time with agent-assisted triage and documentation
Policy and Procurement
Procurement Compliance Agent
Public sector procurement documentation is complex, time-consuming, and error-prone. Each error risks compliance failure or tender disqualification. Manual review capacity is limited.
GDS aligned
all agents deployed in public sector contexts are GDS-aligned and EU AI Act risk-classified from day one

Why Agentic AI

Standard AI assists. Agentic AI acts.

The difference is not just technical. It is operational. Standard AI surfaces information. Agentic AI takes action on it, within the guardrails you define and the governance your organisation requires.

CapabilityStandard AI / ChatbotAgentic AI (Aravian)
Takes autonomous action✗ No✓ Yes, within defined guardrails
Connects to live systems▲ Limited✓ Full integration via MCP
Escalates to humans intelligently▲ Rule-based only✓ Context-aware escalation
Produces audit trail✗ Rarely✓ Full decision log, every action
EU AI Act compliant✗ Typically not✓ Classified and documented from day one
Learns from outcomes✗ Static✓ Monitored, measured, and improved
Handles multi-step processes✗ Single turn✓ Full workflow orchestration

How we deliver

Four phases. Production at the end of phase three.

We do not run open-ended discovery programmes. Every phase has a defined scope, a fixed cost, and a tangible deliverable. You decide whether to continue after each one.

1

Agent Discovery

Map current workflows, identify high-value agent opportunities, define scope and human gates. Output: an agent design document and business case.

Free · 3 hours · Workshop format
2

Agent Design and Validation

Design the agent architecture, define integration requirements, validate with a working prototype on your actual data. Output: a validated design ready for production build.

Fixed scope · 2–3 weeks
3

Production Build and Deployment

Build, test, and deploy the agent to production with full monitoring, alerting, and audit trail in place. Output: a working agent in production with documented governance.

Fixed scope · 4–6 weeks
4

Operate and Improve

Ongoing monitoring, performance improvement, cost governance, and compliance maintenance. Output: continuous improvement with quarterly performance reviews.

Monthly retainer · Optional

Technology stack

SF

Salesforce Agentforce

Native CRM agent platform

MS

Microsoft Copilot

M365 and D365 native agents

GM

Google Gemini

Vertex AI agent framework

MC

MCP Protocol

Multi-system integration layer

LG

LangGraph

Custom agent orchestration

AW

AWS Bedrock

Enterprise AI infrastructure

Start the conversation

Tell us the workflow.
We will show you the agent.

Bring us a process that is consuming too much of your team's time. In a 90-minute conversation, we can map it, identify the agent opportunity, and give you a realistic view of what implementation would look like.

Book the free workshop Talk to the team