AI-Powered Business Operations

Most repetitive, rules-based or data-heavy work. Typical examples are document processing (invoices, contracts, meetings notes), customer support with AI agents, reporting and data entry, demand forecasting, and internal knowledge assistants that answer staff questions from your own documents. During discovery, we map your workflows and rank processes by savings and ease of automation, so you start where the return is highest.

It depends on the process. Well-chosen automations can cut maintenance and operating costs by up to 60%. We don’t promise a number upfront: in the discovery phase we set clear KPIs and ROI projections for each process. In the Proof of Value stage we measure the real impact on your data before you invest in full rollout.

A discovery phase usually takes a few weeks, and a working prototype on real data typically follows within a month or two. Production rollout depends on the number of processes and integrations. Because we start with a focused Proof of Value, you see measurable results early instead of waiting for a long transformation programme.

No. Assessing data quality is part of our feasibility study. We check what data you have, where it lives and what needs fixing, then build the cleaning and integration steps into the plan. Many automations, such as document processing or support agents, work with unstructured data like emails, PDFs and chat logs.

Yes. We connect AI to existing platforms through APIs and integration layers, including Model Context Protocol (MCP) servers that let AI agents securely read from and act on older systems. You can add AI capabilities without a costly rebuild, and modernise step by step if needed.

Data protection is built into every project. We define which data can be shared with AI models and which must stay inside your systems. We use business-grade AI services that don’t train on your data, and we can mask sensitive information before it reaches a model. For strict requirements, models can run in your own cloud account. All work follows GDPR, and every engagement is covered by an NDA. Read our Risks of using AI article.

We pick the right tool for each process rather than forcing one platform. Our stack includes leading LLMs (Claude, ChatGPT, Amazon Bedrock), agent and orchestration frameworks (LangChain, Flowise), and workflow automation platforms (n8n, Make, Zapier), integrated with your existing systems in Python, Java, Node.js, .NET and other technologies.

You do. All code, workflows, prompts and documentation are delivered in your repositories and accounts, with full IP ownership and no vendor lock-in. You can run, change or extend them in-house at any time.