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From AI experiments to production systems — agentic AI, retrieval, workflow automation, and the architecture required to run them reliably.
AI needs a place in the real workflow.
A promising experiment raises practical questions: which work should change, what information can the system use, who reviews its decisions, and how will people adopt it? Existing applications, permissions, evaluation, and operations shape the answer.
What CoRISE Provides
We begin with business and workflow goals, then test a bounded use case. If the evidence supports it, we design tool scopes, verifiable state, evaluation, observability, and human escalation around the AI capability. Integration, adoption, governance, and improvement remain part of the work.
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Five lifecycle stages: Discover, Design, Build, Integrate, Operate. AI Transformation supports discovery, design, build, integration and operation.
Capabilities
AI & Automation
Bound tool permissions, intermediate checks, and human escalation to the task.
Design retrieval around source provenance, freshness, access, and answer evaluation.
Fit AI-assisted decisions into existing work and human review points.
Connect applications, APIs, data, identity, and permissions around a defined use case.
Design guardrails, evaluation, observability, and failure containment.
Assess controlled inference where data location and operational control matter.
Typical Engagement
Select a decision or workflow, and test the value hypothesis.
Define evaluation, guardrails, responsibilities, and review points.
Implement bounded tools and verifiable intermediate state.
Connect existing systems and a human review path.
Observe quality, drift, cost, use, and governance needs.
Architecture
A representative architecture places permissions and human review around the model, while evidence from retrieval and operation stays traceable.
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Agents and tool permissions sit inside a guardrail boundary and connect to retrieval and a knowledge store. Observability connects trace and evaluation logs to alerting and human review.
Contact
Tell us the work, data, and systems your model would need in production. We can begin by framing the opportunity and the operating constraints.
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