Most AI projects that fail do so for predictable reasons: the problem was vague, the data was not ready, the vendor was chosen on the strength of a demo, or the pilot never had clear criteria for success. Our approach is designed to avoid those traps. It follows a solution from first definition to company-wide rollout, with a clear decision point at each stage, so clients can stop early, change course, or scale with confidence.

Use-Case Definition: Good AI projects start with a business problem, not a technology. “Use computer vision” is not a use case; “halve the paint defects that reach the customer” is. We work with operations, engineering, and commercial teams to define the problem, the decision or task AI would improve, the data available to support it, and the value at stake. This short step often changes the answer: some problems need better data or a simple rules-based tool, not a new AI system.

Build, Buy, or Partner: Most companies should buy far more AI than they build. Off-the-shelf tools now cover many common needs, from document processing to predictive maintenance, and they improve faster than an internal team could keep up with. Custom development makes sense where a use case depends on proprietary data or processes that set the company apart. In between sit configurable platforms and co-development with specialist partners. We help clients weigh these options on total cost, speed, control of data and IP, and the risk of depending on a single vendor.

Vendor Search and Evaluation: We build a long list of global, regional, and Chinese providers, then narrow it based on what matters for the specific use case. Wherever possible, vendors are tested on the client’s own data rather than on their best demo. We also check what demos do not show: implementation track record, reference customers in similar industries, data security, local support in the countries where the solution will run, and the financial health of younger vendors. The result is a short list the client can defend internally, with the trade-offs made explicit.

AI Solutions for China: Many global AI services are unavailable or restricted in mainland China, and data transfer rules limit what can be sent to models hosted abroad. As a result, the tools headquarters standardizes on often cannot be used by the China organization. At the same time, Chinese providers offer strong, lower-cost options, from domestic language models such as DeepSeek and Qwen to industrial AI for manufacturing and quality. We help clients find compliant solutions for their China operations, and decide which Chinese technologies could also be used in other markets.

Proofs of Concept and Pilots: A pilot should answer a specific question within weeks: does the solution work on real data, will people use it, and is the value large enough to justify rollout? We agree on success criteria and a go/no-go decision before the pilot starts, so it cannot drift on indefinitely. Many companies have dozens of AI pilots and few solutions in production. Clear criteria, a fixed timeline, and a named business owner are the simplest ways to avoid joining them.

Contracts and Costs: AI pricing is still unsettled. Vendors charge per user, per transaction, by usage, or by outcome, and costs that look small in a pilot can grow quickly at scale. We benchmark pricing, model the cost at full rollout, and negotiate the terms that matter later: who owns the data and any models trained on it, how performance is guaranteed, what happens if the vendor changes its pricing or is acquired, and how the client can exit. Getting these terms right up front is far cheaper than renegotiating once the solution is embedded.

Deployment and Scaling: Most of the work in an AI project happens after the vendor is chosen. Solutions have to be connected to ERP, MES, CRM, or PLM systems, secured, and built into the way people actually work. Our engineering partners handle development and integration, while we manage delivery, keep business and IT teams aligned, and make sure the result meets the goals set at the start. Once a solution works at one site or in one market, we plan the rollout to others, set up monitoring so performance does not quietly degrade, and hand ownership over to the client’s teams.