AI Case Studies &
Enterprise AI
Success Stories

The library is built to show AI running in production. A demo and a production system are judged differently: production has to work on real data, connect to the systems you already use and be monitored after launch. If you want to know how any one of these projects was scoped, integrated and taken live, tell us which one in the contact form and we will walk you through it.

Yes. Use the category tabs in the AI case study library above to filter by Enterprise AI, Generative AI, Voice AI, Document AI, Machine Learning, AI + ERP, Automation or Decision Intelligence. New case studies appear automatically as they are published. If your industry or exact scenario is not listed, describe your workflow and we will say plainly whether we have delivered something close to it.

It depends on the use case, how ready your data is, how many systems the AI must connect to and how much it will be used once live. After a short discovery we give you a breakdown that separates build effort, infrastructure and model usage, and ongoing support, so the full cost is visible before you commit. For most teams we recommend a focused first release that proves value before the scope grows.

A narrow, well-defined use case can reach a working first version in weeks. Systems that touch messy data, several enterprise platforms or regulated workflows take longer and are delivered in stages, each with its own milestone. We prefer to give you a phased plan after we understand the problem, not a number on the first call.

No. Reviewing your data is part of the early work: where it lives (ERP, CRM, documents, call recordings, images), how complete and consistent it is, and who can access it. If the data needs cleanup first, or a different approach would fit better, we tell you at the start, before any build budget is spent.

Yes. AI + ERP is one of our core capability areas, covering ERP, Odoo and enterprise transaction workflows. The AI is connected to the systems your team already works in, so people keep using familiar tools instead of switching to a separate one. See our AI integration and implementation services for how this works.

We agree the rules at the start: where your data is stored, who can access it, what (if anything) is sent to third-party models, and which regulations apply to your industry. For sensitive workloads such as healthcare or financial services, the system can be designed to work within your own cloud, region and access policies. Share your requirements early and we will build to them.

We define what success means with you before building, using a measure your team already cares about, such as processing time, cost per transaction, accuracy or forecast error, set against today as a baseline. The AI is tested on real examples from your data before launch, people stay in the loop where errors are costly, and performance is monitored after go-live. Case study results are the documented outcome for that project, so treat them as evidence of how we work and not a guarantee for yours.

Your data remains yours. Ownership of the delivered code, custom models and documentation, and the licensing of any third-party components, is agreed in writing before work starts, so there are no surprises later. Ask us for these terms up front.

AI systems need attention after go-live because data, users and business rules change. We scope post-launch support with you as part of the plan, covering monitoring, tuning, model or prompt updates and new features. The arrangement can range from a clean handover to your team to ongoing support from ours.

Tell us the workflow, the data and the outcome you need using the contact form. We will map the right AI approach, architecture and delivery plan, then build it to run in production. It helps to bring a short description of the process, a few sample documents or records, the systems involved and the result you want to measure.