SHIP AI Labs

Company Overview

Start a new assessment or load an existing XML.

Start New Assessment

Load Existing Lead (XML)

Resume a multi-department assessment from a previously exported XML.

Surgical Review Rubric

Gather deep-dive intel per department iteratively. Save locally to aggregate later.

Section 1: Operational Complexity & Friction

Section 2: The "Efficiency Leak" Calculator

Section 3: Technical & Cultural Readiness

Section 4: Strategic Impact

Review & Generate

Review the assessed departments before running the high-reasoning LLM to synthesize.

Calling the LLM will aggregate all department data above to calculate the overall ROI and Multi-Agent Architecture.

Synthesizing Proposal

Claude 3.5 / GPT-4o is modeling the specific cross-department architecture and calculating ROI...

Master Partnership Proposal

Aggregate Efficiency Leak Score

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Platform-Wide Impact Level

Projected Monthly Savings

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Company Hours Recovered

Proposed AI Agent Multi-Layer Architecture

12-Week Implementation Roadmap

  • Weeks 1-3 (Discovery & Mapping): Secure API keys, map exact data inputs, configure isolated testing sandbox for selected departments.
  • Weeks 4-6 (Agent Assembly): Develop cross-dept communication logic, connect internal databases, run unit tests on historical data.
  • Weeks 7-9 (Shadow Deployment): Run the agents in parallel to human workers (Human-in-the-Loop review). Target 95% accuracy.
  • Weeks 10-12 (Production & Scaling): Active routing to AI layer first. Change-management training for staff to handle escalation exceptions only.