Getting Started with AI
Many businesses know AI could help them but are not sure where to start.
These services are designed around companies that are new to AI, to help them identify which problems are worth solving, check whether a given approach is technically viable, and build something that works.
Services
Which service is right for you?
These four services cover the main stages of an AI journey, from initial exploration through to production. Each one is designed for a specific type of company and a specific starting point. Click or tap to see full details.
Not sure which fits where you are right now? That is completely fine, and it is exactly the kind of thing a free intro call is for.
Working with your team to understand business challenges and identify where AI can make a real difference. This includes assessing whether your data, infrastructure, and processes are ready for AI. Where off-the-shelf tools like Copilot are sufficient, we will say so. Where custom solutions are needed, we will identify those opportunities clearly.
Deliverables
- Structured meetings and working sessions with key stakeholders
- Assessment of AI readiness across data, infrastructure, and organisational capability
- Analysis of current operations and potential use cases
- Recommendations for both off-the-shelf tools and custom AI solutions
- Ranked list of opportunities with ROI estimates
- Honest recommendations on priorities, including whether AI is the right solution
Timeline
Varies by company size and scope. Can range from a focused 1-2 week assessment to a comprehensive multi-department review.
When to choose this
You want to use AI but do not have a specific use case yet. You want expert guidance to separate genuine opportunities from hype, and an honest view of whether you are ready to proceed.
A focused, rapid validation of the most challenging part of a potential solution. Rather than building a full prototype, this tackles the core technical question: is this viable? Unlike a proof of concept, it does not require real production data or a fully integrated build. The goal is to answer the key unknown as quickly and cheaply as possible. This is a standard part of a de-risking process: by addressing the hardest question first, you avoid committing significant time and budget to something that may not be technically feasible.
Deliverables
- Focused analysis of the critical technical challenge
- Small-scale technical validation using sample data or simplified scenarios
- Assessment of key risks and blockers
- Clear go/no-go recommendation
- If viable: outline of what a full proof of concept would involve
Timeline
Highly variable. Can be as quick as 1-2 weeks for a focused technical validation.
When to choose this
You have a specific idea but want to validate the most uncertain part before committing to a full build. This is the low-cost option to test viability before further investment.
Building a working prototype using your actual data and real-world conditions. Goes beyond a quick technical check to prove the approach works end-to-end in your specific environment. Useful when you need concrete evidence to justify investment or to demonstrate value to stakeholders.
Deliverables
- Working prototype built with your data
- Performance evaluation and results analysis
- Technical validation across realistic scenarios
- Production implementation roadmap
- Resource and cost estimates for full deployment
Timeline
Dependent on scope and complexity. Timelines are agreed based on your specific needs and constraints.
When to choose this
You have validated the core approach and now need concrete proof it works at scale before investing in full production. Or you need to demonstrate value with a working system.
Building production-ready AI systems that integrate with existing infrastructure. From data pipelines to model deployment to user interfaces, delivering complete solutions your team can operate and maintain. You might have already done a feasibility study or proof of concept, or be confident enough to go straight to production.
Deliverables
- Production-ready AI system
- Integration with existing systems and workflows
- Full documentation and deployment guides
- Team training and knowledge transfer
- Post-deployment support period
Timeline
Highly variable depending on scope and complexity. Determined based on your specific requirements.
When to choose this
You have a clear use case and are ready to build. You may have already validated the approach, or be confident enough to proceed directly.
Each one above sketches what that service typically involves, not a fixed menu. Many engagements combine more than one of these, or sit between them.
If your situation looks different, get in touch and we will talk through what fits.
Not sure where your business stands with AI?
Our free AI Readiness Assessment gives you an objective score across 6 key dimensions, with personalised results and a practical action plan. No call needed. Takes around 10 minutes, or 3 minutes with the Snapshot version.
Is this for you?
Who this is for
These services work for companies of all sizes, from SMEs to large enterprises, across industries including manufacturing, healthcare, professional services, financial services, and retail. The relevant question is not the industry: it is whether you have real business problems that AI could help solve.
You do not need an in-house data science team or prior AI experience. You do need data (even if it needs work), a specific problem worth solving, and a genuine intent to act on the outcome.
If you are unsure whether AI is right for your situation, a free intro call is the right starting point. In many cases, we can give you an honest read in that first conversation. If it is clearly not the right solution for what you are trying to solve, we will say so directly, without any obligation or need for a formal assessment.
When something else fits better
These services are for companies that are not yet sure where to start, or that need structured exploration and validation before committing to a build. If you already know which technical discipline you need, these pages go deeper:
- Custom AI and machine learning systems: AI & Machine Learning
- Language models, generative AI, and text processing: NLP & Generative AI
- Statistical analysis, insight, and analytics infrastructure: Data Science & Analytics
- AI strategy, governance, and organisational readiness: AI Strategy
FAQ
Common questions
Is this about training my team to use ChatGPT or Copilot?
No. These services focus on building custom AI systems for your business. If off-the-shelf tools like Copilot genuinely solve your needs, we will recommend those during an assessment. But these services are about building AI that does something specific and valuable for your operations, not training on consumer AI tools.
What is the difference between a Feasibility Study and a Proof of Concept?
A feasibility study is a targeted technical check. You identify the most uncertain part of an idea and test whether it is actually viable, without building a full prototype or needing production data. It is the quickest, cheapest way to find out if an approach is worth pursuing.
A proof of concept goes further. You build a working prototype using your actual data, in conditions close to real-world, and prove the approach works end-to-end in your specific environment.
The two are often run in sequence: feasibility study first to check the hard technical question; if that passes, a proof of concept to confirm the approach holds up with real data at scale. That way you are not committing significant budget to a full build before you know the approach is sound.
Does every engagement have to start with an assessment?
No. If you already know what you want to build, you can start with a feasibility study, proof of concept, or go straight to implementation. The assessment is for businesses that do not yet have a clear use case and want structured guidance on where to start.
How much does this typically cost?
It varies significantly depending on scope, complexity, and which service you need. An assessment is typically far less expensive than a full build. The best starting point is a free introductory call to understand your situation and provide a realistic indication of cost and scope.
Can this work with our existing systems and data?
Yes. The focus throughout is on integration with your existing infrastructure, databases, and workflows. We will assess data readiness as part of any engagement and be straightforward about what needs to be in place before useful AI work can begin.
What happens after implementation?
Every implementation includes documentation, team training, and a post-deployment support period so your team can operate the system independently. Ongoing support can be arranged after that if needed.
Ready to get started?
Let's talk about where AI can make a difference for your business.