Common Questions, Answered
What size of company do you typically work with?
We work with growth-stage startups through established enterprise teams. The common thread isn't company size — it's having a specific business problem where AI, ML, or data infrastructure investment can move a real metric.
Do you build custom models, or do you use existing AI providers?
Both, depending on the problem. Many generative AI applications are best built on top of existing foundation models via retrieval, fine-tuning, or prompt engineering. Some forecasting, scoring, and classification problems are better served by a custom-trained model on your own data. We recommend the approach that fits the problem, not a default.
How do you handle data security and confidentiality?
Client data is handled under signed confidentiality agreements, with access scoped to the engagement team. Where a project touches sensitive or regulated data, we design access controls and data handling into the architecture itself, not as an afterthought.
What does a typical engagement look like?
Most engagements start with a scoped discovery phase to validate the problem and data readiness, followed by an iterative build phase with regular checkpoints, and a handover phase focused on making sure your team can operate and extend what we've built.
Can you work alongside our existing engineering or data team?
Yes — most engagements are collaborative by design. We frequently pair with in-house engineering, data, and ML teams rather than working in isolation, and we prioritize documentation and knowledge transfer so the work is maintainable after we're gone.
How do you measure whether an AI project succeeded?
Before any build work starts, we agree on concrete success criteria tied to a business metric — not just model accuracy in isolation. Evaluation against those criteria continues after launch, not just at handover.