OrionScale is building a curated network of professionals and domain experts who can help AI teams and enterprises train, evaluate, test, govern, and improve AI systems. The model is simple: combine strong technology delivery with the judgment, context, and professional standards that only experienced people can bring.
OrionScale is developing a managed model that can combine expert sourcing, structured evaluation, delivery leadership, quality review, and responsible AI practices around a client’s use case.
Domain experts review outputs, create examples, compare responses, and apply professional judgment to improve AI behavior and quality.
Create realistic tasks, scoring criteria, review standards, and repeatable evaluation workflows grounded in how work is actually done.
Probe failure modes, edge cases, workflow risks, hallucinations, policy issues, and domain-specific quality gaps before wider deployment.
Match vetted professionals and subject-matter experts to remote, project-based AI work based on domain, capability, and client requirements.
For organizations adopting AI internally, OrionScale can connect its existing strengths in delivery, architecture, governance, and change with a growing expert-led AI capability.
Identify where AI can create measurable value, map the workflow, define human checkpoints, and turn ideas into testable delivery increments.
Define ownership, risk controls, review gates, policy alignment, human oversight, and practical guardrails for responsible adoption.
Help teams integrate AI into delivery practices, evaluate tools, strengthen operating models, and build the skills needed to work effectively with AI.
The operating model is designed to scale from a small specialist engagement to a managed expert workflow as OrionScale’s network and client demand grow.
Clarify the AI use case, target workflow, quality criteria, security constraints, deliverables, and the expertise required.
Select professionals based on domain knowledge, experience, capability evidence, availability, and project fit.
Experts perform structured work such as output review, task creation, rubric development, workflow testing, or specialist validation.
Apply defined QA checks, review standards, delivery governance, and feedback loops before work is accepted or scaled.
You do not have to be an AI engineer to contribute to many expert-led AI projects. Deep knowledge of a profession, workflow, industry, or technical specialty can be the value.
Review AI outputs using the standards you would apply to real professional work and explain where quality succeeds or fails.
Build realistic tasks, examples, reference outputs, scenarios, or work products that reflect how experts actually solve problems.
Challenge models and AI workflows with edge cases, difficult scenarios, domain constraints, and realistic expectations.
Tell OrionScale what you are training, evaluating, deploying, or trying to improve. We can explore whether our delivery, architecture, governance, and developing expert network can support the work.
