AI Engineering
Custom AI, engineered into production.
AI Engineering builds the systems a roadmap specifies: customer-facing AI products and internal AI systems, built to a written specification and deployed in the client's environment.
AI Engineering builds the systems a roadmap specifies: customer-facing AI products and internal AI systems, built to a written specification and deployed in the client's environment.
Customer-facing AI products designed, built, and shipped with the client's team.
ML engineers, software engineers, product managers, and designers take a product from specification to release. Models are selected against an evaluation set built from the client's data, and each product ships with the interfaces, APIs, and analytics required to operate it.
AI embedded in internal operations and connected to the systems already in place.
Nexa builds agents, retrieval, and automation that operate inside the client's existing infrastructure. Requests captured by customer-facing products open work in these systems, and status returns to the customer through the same product. Each system inherits the client's identity and access controls, writes to the systems of record, and keeps a human approval step on actions with financial or regulatory consequence.
Production deployment in the client's environment and a team equipped to operate it.
Systems deploy to the client's private cloud or on-premises infrastructure. Each deployment includes an evaluation suite that runs against every release, monitoring for quality and cost, and documentation and training for the team that operates the system after handover.
Release depends on criteria agreed before engineering starts.
Each build is tested against the acceptance criteria in the specification and the evaluation set built from the client's data. The client's team runs acceptance testing, and the system moves to production after written sign-off.
Four practices apply to every build.
Every model is evaluated against a test set built from the client's data before release.
Model access sits behind a single interface, so a model can be replaced without rebuilding the system.
Actions with financial or regulatory consequence require a person to approve them.
Code, configuration, and documentation belong to the client.
Send a short description of the operation and the problem. A 30-minute scoping call follows.