Build an AI system with a point of view, not just a prompt.

The capability map

• Architecture and workspace setup: map the workflow, connector needs, isolation boundaries, and approvals before a team wires tools together.
• Search and retrieval: turn approved connected sources into a traceable retrieval and synthesis path, rather than a vague promise of enterprise search.
• Product building: turn a high-value workflow into a scoped prototype, product brief, or implementation-ready artifact without claiming to replace a full product team.
• Evals and post-training: define task-specific checks, feedback signals, and post-deployment optimization. “Post-training” here means an improvement loop, not foundation-model training.
• Data and governance: organize source material, access boundaries, telemetry, and review points. Compliance approval remains client-specific and human-owned.
• Design and subjective domains: build rubrics and review loops for writing, communications, brand, and judgment-heavy work where a plausible answer is not enough.

The artifact

You receive a decision-ready capability map: the real task, the sources that matter, a bounded technical and operating design, a practical first artifact, and the feedback loop that should govern the next version.

task -> source -> quality bar -> prototype -> observed result -> human review -> next iteration

What stays explicit

Taz does not promise autonomous client communications, universal benchmarks, blanket compliance, or a set-and-forget AI deployment. A person still owns source approval, consequential decisions, and external actions.