AI transformation, in production.

AI transformation is the work of moving an organization from isolated AI experiments to AI that runs reliably inside its products and operations.

In practice that means selecting use cases with a sound business case, building the data foundation they depend on, engineering and deploying the systems, putting governance around them, and leaving your team able to extend the work.

No. Some clients want the full sequence; others need one piece, such as a readiness assessment, a data foundation, governance, or one AI workflow in one business area.

We shape the engagement around what you need in order to move forward.

That is the usual starting point. We help you evaluate which opportunities have a strong business case, which your data and systems can support, and what needs to be true before you build.

From there we define the first use case and the technical path to production.

The strategy, the engineering and the capability transfer happen in one engagement with one team.

The objective is working systems in production and a team that can extend them, not a roadmap you are left to implement alone.

Against a metric agreed at the start and measured at team level.

Typical measures are cycle time, throughput on the workstream, and defect rates holding steady as speed increases. Agreeing the metric first keeps the work tied to a business result rather than to activity.

Any of the three. You can add AI engineers to your team through staff augmentation, have a dedicated AI team run a workstream, or hand AIGard a defined AI project end to end.

The choice depends on how much of the delivery you want to manage.

NDAs are signed before any access is given, and engineers work inside your environment under least-privilege access. Privacy and security controls for each AI use case are defined up front.

We agree which AI tools and models may access your code and data, including retention and training settings. Agents operate within defined permissions, with human approval for sensitive actions.

Yes. The team’s experience includes conversational AI with retrieval over a medical reference corpus, diagnostic machine learning, and agentic commerce with managed model lifecycles.

The case studies describe the scope of each.

You do. IP assignment is part of every contract.

That includes models, prompts, evaluation sets, pipelines and source code produced for you.

Engineers typically start within two to four weeks of the scoping call. The exact date depends on the roles, seniority and number of people required.

We confirm a realistic start date in writing before you commit, and you meet the proposed engineers before work begins.

AIGard engineers typically cost 30–50% less than the fully loaded cost of a comparable US-based hire, with no recruiting fees, benefits overhead or insurance costs on your side.

The figure for a specific engagement depends on the model, the roles, the seniority mix and the size of the team. We issue a written proposal after the scoping call, so the commercial terms are agreed before any work starts.