AI Readiness Assessment
We map processes, data, constraints, risks, and real opportunities before recommending implementation.
- Process inventory
- Data constraints
- Risk map
- Measurable value criteria
Applied R&D
Applied generative AI R&D for organizations
We turn generative AI capabilities into measurable prototypes, workflows, and internal systems.
We work with organizations that need to decide where to apply AI, how to measure impact, what controls to require, and how to move from experiment to operation without improvising.
What we do
Use-case diagnostics
We prioritize cases where generative AI can improve speed, quality, or traceability without creating operational risk.
- Impact-effort-risk matrix
- Testable hypotheses
- Success metrics
- No-scale conditions
Internal copilots and RAG
We build assistants over documents, internal knowledge, and processes with sources, permissions, and response evaluation.
- Document intelligence
- Traceable RAG
- Output evaluation
- Human fallback
AI automation
We integrate models into real workflows with logs, metrics, controls, and explicit autonomy limits.
- Tool integration
- Auditable states
- Operational metrics
- Approval controls
Working method
Evaluate
Define the problem, risk, available data, and utility metric.
Govern
Design controls, approval criteria, traceability, and usage limits.
Prototype
Build a measurable test with users, sources, and expected failure modes.
Implement
Move into an internal workflow with observability, documentation, and maintenance.