Scored on value, feasibility, data readiness and risk.
Business, people, data, technology, operations, governance.
Integration, model strategy, security and deployment.
Roles, controls, evaluation and oversight.
Baselined, costed and ready to fund.
Four situations where consulting is the right first step.
If none of these describe you, say so. We will point you at AI development or managed services instead.
Forty use cases on a spreadsheet and no agreed way to decide which three get funded this year.
Two or three proofs of concept that worked in a demo and stalled before production.
The board has asked for an AI strategy and the answer needs to be defensible, not enthusiastic.
Risk, legal or audit are blocking deployment because controls were never designed in.
Our Enterprise AI Consulting Services
They are sold separately and they work better together. Most engagements combine three or more.
AI Strategy Consulting & Roadmap
Define how AI supports your business priorities, then build a practical roadmap connecting opportunities, investment, enterprise capability, delivery phases and measurable outcomes.
You Receive- AI strategy on a page, agreed with the executive team
- Investment and funding model by phase
- Sequenced 12 to 24 month roadmap
- Named P&L owners per initiative
- Value measurement framework
AI Readiness Consulting & Assessment
Score whether your business strategy, data, technology, governance, workforce and operating capability can actually support enterprise AI. Maturity is set by the weakest pillar, not the strongest.
You Receive- Six-pillar maturity score with evidence
- Gap analysis against your target state
- Data and platform readiness findings
- Remediation actions, sequenced by dependency
- An honest view of what is not ready yet
AI Use Case Discovery & Prioritization
Identify AI opportunities across business functions and workflows, assess viability, and prioritise the initiatives with the strongest combination of value, feasibility and readiness.
You Receive- Use case inventory across functions
- Scoring on value, feasibility, data, risk and effort
- Prioritised portfolio with a recommended first three
- Baseline estimates for each shortlisted case
- A documented list of what we recommend not doing
AI Architecture Consulting & Model Strategy
Define the solution architecture, model approach, enterprise integrations, infrastructure, security and deployment requirements needed to move AI into production rather than into another pilot.
You Receive- Enterprise AI reference architecture
- Model strategy across commercial, open-weight and private
- Integration map to ERP, CRM and systems of record
- Security, identity and deployment design
- Build versus buy versus extend recommendation
Responsible AI Consulting & Governance
Establish the policies, roles, controls, evaluation methods and oversight required to develop and operate AI responsibly. Designed with risk and legal in the room, not presented to them afterwards.
You Receive- AI governance framework and operating model
- Risk tiering methodology by use case
- Agent identity, access and human oversight design
- Evaluation and audit approach
- Policy set ready for internal approval
Decades Of Data & AI Delivery
The scale and experience behind every engagement, from strategy through production.
Three ways to start.
Scoped and time-boxed. You know what you are buying before the first workshop.
A focused look at one business area. Where AI could create value, what is feasible with the data you have, and what the first initiative should be.
The full engagement. Assess, align, discover and prioritise, architect and govern, then roadmap. Ends with a business case your executive team can fund.
Continuing architecture, governance and portfolio guidance as your programme scales. Useful when you are building in-house and want independent review.
What lands on your desk at the end.
Consulting that stops at a recommendation is a report. These are the artefacts a delivery team can actually build from.
Scored, ranked and sequenced, with the rejected cases documented and the reason recorded.
Evidence-based maturity across business, people, data, technology, operations and governance.
Solution and enterprise architecture, integration map, model strategy and deployment design.
Policies, risk tiering, roles, evaluation approach, human oversight and audit design.
Current cost and cycle time per workflow, expected value, run cost and payback position.
Phases, dependencies, resourcing, milestones and the measurement plan for each release.
Every artefact is built to be handed to a delivery team, whether that team is ours, yours or a third party.
From business priorities to an implementation-ready plan.
Phases one and two run first for a reason. Architecting before aligning is the most expensive mistake in enterprise AI.
Assess
Understand the current stateEvaluate maturity across business strategy, existing initiatives, data, technology, architecture, security, governance, skills and operating capability.
Week 1–2Align
Connect AI with business prioritiesDefine the business outcomes, enterprise priorities, investment criteria, operating principles and success measures that guide adoption.
Week 2–3Discover
Focus on the right opportunitiesIdentify and score use cases on business value, technical feasibility, data readiness, risk, complexity, effort and time to value.
Week 3–5Architect
Design for enterprise requirementsDefine solution architecture, model strategy, integrations, infrastructure, security controls, governance framework and deployment approach.
Week 5–7Roadmap
Create the path to implementationSequence use cases, dependencies, resources, validation activity, production milestones, governance requirements and value measurement.
Week 7–8Every AI failure maps to one of six pillars.
We score all six. Maturity is set by your weakest pillar, not your strongest, so the recommendation is always to fund the weakest rather than the loudest.
Value case, P&L ownership, funding, benefit tracking
Sponsorship, operating model, roles, adoption, change
AI-ready data, lineage, quality, access, retrieval
Reference architecture, integration, agent platform
LLMOps, AgentOps, evaluation, observability, run model
Security, risk, compliance, responsible AI, audit
Consulting, development, or managed services?
Most enterprises need two of the three. Here is how to tell which conversation to start.
| AI Consulting | AI Development | AI Managed Services | |
|---|---|---|---|
| The question it answers | What should we build, and is it worth it? | How do we build it so it survives production? | Who keeps it working and improving? |
| Start here if | You have ideas but no agreed priorities or business case | You know the workflow and need it delivered | Something is already live and degrading or unmonitored |
| Typical duration | 4 to 8 weeks | 8 to 16 weeks to first production release | Ongoing |
| Main output | Roadmap, architecture, governance, business case | A working solution integrated with your systems | Uptime, quality, cost control and continuous improvement |
| Learn more | You are here | AI Development → | AI Managed Services → |
Most consultancies cannot build what they recommend.
As an AI consulting firm with production delivery experience, we create recommendations that account for the systems, controls, and people needed to implement them.
Start Your AI ConsultationRecommendations account for the data, technology, governance, people and integration capability actually required to deliver them.
Every engagement starts with business priorities, workflow pain and measurable outcomes, before any model or platform is discussed.
We do not resell a platform. Architecture and model recommendations follow your use cases, data sensitivity, cost and deployment needs.
Sixteen years building the data platforms, integrations and enterprise applications that AI has to reach into.
Security, oversight, evaluation and accountability are part of the architecture from the first workshop, not a later approval gate.
Real Outcomes, Delivered at Scale
See how we help enterprises apply AI to improve operations, decision-making and customer experiences.
Transforming Dealer & Customer Support with AI
An AI-powered knowledge platform streamlined dealer and customer support by delivering faster, context-aware responses and reducing manual effort.
Response
AI Virtual CFO Platform for Smarter Financial Decisions
A multi-tenant AI platform consolidated financial data, automated reporting, and delivered advisor-level insights for thousands of concurrent users.
Reporting
From Customer Requirements to Engineering-Ready Designs
An AI-powered platform converted customer requirements into engineering-ready design inputs, reducing design iterations and accelerating production readiness.
Iterations
Decision Intelligence Agents for Financial Institutions
A unified AI finance engine integrated ERP and credit models to deliver real-time liquidity insights and automated working capital decisions.
Decisioning
AI Development Solutions Across Key Industries.
Our AI software development services are designed around the workflows, data, systems, risks, and operational requirements of each industry.
Build AI solutions for commercial operations, dealer networks, supply chains, engineering, production, quality, and service.
Explore Manufacturing Financial ServicesDevelop governed AI applications for banking, lending, insurance, wealth management, risk, compliance, and customer operations.
Explore Financial Services Healthcare and Life SciencesCreate secure AI solutions for clinical, research, administrative, enterprise knowledge, and patient support workflows.
Explore Healthcare Retail and E-CommerceBuild AI applications for merchandising, personalization, inventory, pricing, fulfillment, and customer experience.
Explore Retail Consumer GoodsDevelop AI solutions for demand planning, product operations, supply chains, sales execution, and customer engagement.
Explore Consumer Goods Travel, Transportation and LogisticsCreate AI applications for planning, logistics, asset utilization, service delivery, and customer support.
Explore Transportation Energy and UtilitiesBuild governed AI solutions for forecasting, asset management, field operations, compliance, and customer service.
Explore Energy Media and EntertainmentDevelop AI applications for content operations, enterprise knowledge, audience engagement, and workflow automation.
Explore MediaRecognitions and Awards
Our enterprise AI, data, and technology work has been recognized by independent industry organizations and technology communities.
AI Consulting Questions
It helps an organisation determine where AI can create value and what is required to implement it successfully. That covers AI strategy, readiness assessment, use case prioritisation, architecture planning, model selection, governance and implementation roadmapping.
Consulting defines what to build, why it matters, how it should work and what is required for it to succeed. Development designs, engineers, integrates, tests and deploys the chosen solution. Many enterprises need both, sequentially.
An AI Opportunity Assessment runs two to three weeks for a single function. A full AI Strategy Sprint runs four to eight weeks. Advisory retainers are ongoing. Timelines extend where data access is slow or where many stakeholder groups are involved.
A prioritised use case portfolio, a six-pillar readiness score, a reference architecture, a governance framework, a baselined business case and a sequenced implementation roadmap. All built to be handed to a delivery team.
Across six pillars: business, people, data, technology, operations and governance. Maturity is set by the weakest pillar, so the recommendation is always to invest in the weakest rather than the most visible.
Each case is scored on business impact, technical feasibility, data readiness, integration requirements, risk, implementation complexity, expected cost and time to value. We also document what we recommend not doing and why.
Model selection considers the use case, data sensitivity, accuracy, latency, explainability, security, scalability, deployment preference, operating cost and governance requirements. We are model independent and do not resell a platform.
Yes. Architectures are designed around the data platforms, cloud environments, enterprise applications, APIs, identity systems and security controls you already run. No rip-and-replace.
Yes, and that is deliberate. Because the same organisation delivers production AI, recommendations are constrained by what can actually be built. You are equally free to take the roadmap to another delivery partner.
Then that is the finding and we will say so, with the specific gaps and the sequence to close them. A four-week engagement that prevents a poorly targeted build is a better outcome than a programme that loses funding in month nine.