AI opportunity mapping
We review your workflows to find where AI can save time or improve quality, then prioritise use cases by value and feasibility.
AI & Automation · AI Integration
AI integration means connecting large language models to the software, data and processes your business already runs on. Rather than adding another chat tool, we build AI into your CRM, documents, help desk and internal workflows, so it removes real work instead of adding another tab. Every project starts with a clear use case and ends with a measurable change in how work gets done.
AI Integration
We review your workflows to find where AI can save time or improve quality, then prioritise use cases by value and feasibility.
We choose between models from providers such as OpenAI, Anthropic and Google, or open-source alternatives, based on accuracy, speed, running costs and data requirements.
Secure connections to your documents, databases and business systems. Retrieval and permissions ensure the AI only sees what each user is allowed to see.
AI embedded into the tools your team already uses, from CRM and help desk to Slack, Microsoft Teams and Google Workspace.
Agents that research, draft, classify, summarise and take defined actions across systems. Human approval steps are built in wherever the stakes require it.
Testing frameworks, usage monitoring, access controls and clear policies. AI output stays accurate, compliant and auditable over time.
We audit your workflows, systems and data readiness, then agree a shortlist of use cases with clear success measures.
The highest-value use case is built and tested with a small group of users, measured against the agreed success criteria.
The proven solution is hardened, secured and rolled into production systems, with training and documentation for each team.
Lessons from the first deployment shape the next use cases, building a consistent AI capability across the business.
Start with one frequent, time-consuming task where the output is easy to check, such as summarising calls, drafting replies or sorting enquiries. We map your workflows, score each idea on value and feasibility, and pilot the strongest one with a small group. A focused pilot proves value quickly and shows what your data and team need before you scale.
There is no single best model; the right one depends on the task, accuracy needed, speed, data sensitivity and running costs. Many businesses use more than one, matching each model to a specific job. We test candidates from OpenAI, Anthropic, Google and open-source providers against your real examples, and keep the setup flexible so models can be swapped later.
It can be, when the integration is designed around security from the start. That means enterprise AI providers with clear data-processing terms, the same access permissions people already have, usage logs and keeping sensitive data out of scope where needed. Every data flow is documented, so your IT or compliance lead can review it before anything goes live.
A chatbot answers questions in a conversation, while an AI agent carries out multi-step tasks across your systems. An agent might read an incoming email, check the customer record, draft a reply and create a follow-up task. Because agents take actions, we build in approval steps, permission limits and logging wherever a mistake would be costly.
Not perfectly clean, but it does need to be accessible and reasonably organised. Language models cope well with messy documents and emails, but structured data used for decisions, such as prices or stock levels, must be accurate. We assess data readiness at the start of every AI integration and fix the gaps that would affect results.
Ready when you are
Tell us where you are and where you want to be. We will come back within one working day with next steps.