Elevata is now a Services Partner of OpenAI. For customers on AWS, the partnership brings OpenAI implementation and cloud engineering into the same team: to build applications that serve customers, agents that complete business tasks, and engineering workflows that put Codex to work.
The opportunity is in what those systems can do. A customer can move from a conversation to a confirmed order. An account team can prepare for a decision using context from several business systems. An engineer can take a change from planning through implementation, tests, and review. Our programs connect those outcomes to the data, integrations, and controls needed to deliver them.
Programs built around business outcomes
AI Assessment: a clear path to production
We start with the decision that is blocking progress. Which use case should ship first? Can an agent access the information it needs without exposing data the user should not see? What integrations and operating costs will the application require?
AI Assessment turns those questions into a delivery plan with a defined business outcome, acceptance criteria, and the work needed to launch. You leave knowing which data and integrations are required, who needs access, and how the result will be measured.
OpenAI agents: connect business systems and complete the task
We build agents around the work a customer or team needs to finish. In conversational commerce, that means connecting product knowledge and transaction systems so an interaction can become a completed order. In business operations, it means bringing CRM, contracts, delivery, and financial records together to prepare a briefing, identify a gap, or put the next action in front of the right person.
For knowledge applications, we connect company documents and business data to answers with sources and permission-aware retrieval. Human review and approval belong at the points where a workflow calls for them. The result is an application designed around a complete task, with clear boundaries for what the agent can do.
AI Benchmark helps establish whether an OpenAI application meets the required standard. We evaluate OpenAI models, prompts, retrieval, and tool use against representative tasks, measuring quality, completion, response time, and cost. Those results guide improvements to the application and provide acceptance criteria before it reaches more users.
Codex adoption: support the whole software delivery workflow
We integrate Codex with repository context, development standards, tests, and review. The work covers how engineers understand a codebase, plan a change, implement it, and check the result. The aim is a shorter path from a task to a reviewable change, with the team’s quality standards built into the workflow.
AI Governance Gateway adds enterprise identity, model policy, and spend checks before requests run. Usage and audit records connect costs to teams, codebases, and work items, with history available in the customer’s S3 data lake.
Engineering teams can expand their use of Codex while finance and security get a common record of access, spending, and the work it supports. Adoption becomes a program the company can manage and measure.
Why this matters for customers on AWS
Elevata is an AWS Advanced Tier Services Partner with the Generative AI Competency. Our OpenAI partnership builds on that delivery experience. The model, data permissions, application architecture, and operating cost can be designed together around the AWS environment the customer already runs.
Delivery can use Amazon Bedrock for supported OpenAI models or the OpenAI API, according to the application’s requirements. We make that choice with model availability, data handling, security controls, and workload economics in view.
The OpenAI partner program adds technical resources, training, and support to this work. For customers, that strengthens the team delivering the whole program, from the first assessment through model evaluation, integration, rollout, and operation.
Start with the question that is blocking progress
Whether you are choosing a first production use case, scaling an OpenAI application, or bringing AI tools under company controls, the next step should be concrete. An AI Assessment identifies what needs to change and the outcome to measure.
