Question 1

What does a typical project cost?

Accepted Answer

A two to three week diagnostic sprint starts around USD 6,000 to 12,000 and ends with a ranked opportunity map, an architecture outline and a costed plan. A first production build usually lands between USD 25,000 and 80,000 depending on how many systems it touches and how much data cleanup is involved. Ongoing support and iteration retainers start around USD 3,000 a month. We quote fixed scope milestones after diagnosis rather than guessing on a first call, and if the payback is not there we will tell you before you spend the build budget.

Question 2

How do you handle data privacy and security?

Accepted Answer

Your data stays in your environment wherever the use case allows: your cloud account, your database, your storage. Where a hosted model is the right choice, we use enterprise endpoints with training on your data switched off and zero retention terms in place, and we tell you exactly which fields leave your network. Access is least privilege and time bound, secrets live in a managed vault, prompts and outputs can be logged in your own tenancy for audit, and personal data is minimised or masked before it reaches a model. We sign NDAs and data processing agreements, and we can work under your own controls and review process. Digital Elite does not hold third party certifications such as SOC 2 or ISO 27001; we work inside the certified environments and controls our clients already operate.

Question 3

How quickly can an engagement start?

Accepted Answer

Most engagements begin within two to three weeks of the first call. If the scope is already clear, a diagnostic sprint can often start sooner. A first working system in production is typically six to twelve weeks from kickoff, with something demonstrable at the end of every sprint.

Question 4

Do you work alongside our internal team?

Accepted Answer

Often. We can lead delivery end to end, or embed a senior pod inside your team using your backlog, rituals and review process. Either way you get the repository, the infrastructure definitions, the runbooks and a documented handover, so nothing depends on us staying.

Question 5

Which AI models do you use?

Accepted Answer

Whichever fits the task, the budget and the privacy profile. Hosted frontier models where quality matters most, smaller or open weight models running in your own cloud where cost, latency or data residency matter more, and often a mix inside one workflow. We keep the model layer swappable so a better or cheaper option later is a configuration change, not a rebuild.

Question 6

How do you make sure an AI system is accurate enough to ship?

Accepted Answer

We agree what accurate means before we build: a test set from your real cases, a target threshold, and named failure modes that must never occur. Systems ship with evaluation runs, human review checkpoints on high risk actions, and monitoring that flags drift after launch.

Question 7

What happens after launch?

Accepted Answer

Every build ships with documentation, runbooks and monitoring so your team can take it internal. If you would rather we stay, support and iteration retainers cover monitoring, tuning, model updates and new workflow additions.

Question 8

Who owns the code and the data?

Accepted Answer

You do. Source code, infrastructure definitions, prompts, evaluation sets and any data involved are yours, transferred to your accounts and repositories as we go rather than at the end.

Before you get started.

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