AI + ML
Design, evaluation, and integration of AI/ML systems into real organizational workflows.
Capabilities
Technical capabilities for applied generative AI
The capabilities we use to turn generative AI into useful, measurable, auditable, and governable internal systems.
We do not start from the tool. We start from the process, the risk, and the evidence. Each capability can be combined into diagnostics, pilots, red teams, internal copilots, document workflows, or decision-support systems.
Capability map
Applied GenAI Workflows
Turning generative models into measurable processes with owners, logs, metrics, and fallback.
RAG + Document Intelligence
Assistants over documents, policies, contracts, procedures, and internal knowledge with sources and permissions.
LLM Evaluation
Testing outputs against expected behavior, hallucination risk, critical errors, and failure cases.
AI Governance
Policies, risk matrices, lifecycle controls, human approval rules, and operational evidence.
LLM Security
Red teaming against prompt injection, data leakage, tool abuse, RAG poisoning, and unsafe outputs.
Human-in-the-Loop
Approval gates, escalation paths, and human control points for sensitive AI-assisted decisions.
Observability + Audit Trails
Tracking prompts, outputs, sources, versions, decisions, approvals, and failures across AI workflows.
Data Integration for AI
Connecting documents, APIs, tickets, databases, policies, and internal systems into AI-ready contexts.
Process Mining
Identifying where AI can reduce manual work, decision latency, rework, and operational bottlenecks.
Real-Time Alerting + Decision Support
AI-assisted summaries, triage, recommendations, and alerts for operational teams.
Research-to-Prototype
Turning research hypotheses and technical experiments into controlled prototypes and applied workflows.
How they connect
Offerings
Offerings are what a client can buy: readiness, governance sprint, red team, or prototype.
Capabilities
Capabilities are the reusable technical muscles that make execution credible.
Work
Work shows evidence: technology, demos, artifacts, and anonymizable applied cases.
Research
Research supports technical judgment, evaluation, falsification, and traceability.