Maslow AIMASLOW|AI
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CASE STUDIES

Production work and current status

Two production engagements, followed by clearly labeled scenarios showing where the same architecture can be applied next.

PRODUCTION EVIDENCEMANUFACTURING · PRODUCTION ENGAGEMENT
90 days
idea to foundation

Infinite AI OS: from tribal knowledge to an AI operating system in 90 days

CHALLENGE
Estimating, quoting, reporting, and file search all ran on manual effort and knowledge locked in people's heads: high cost to serve, and high key-person risk.
SOLUTION
Four named AI employees (a manager and three specialists) working in Microsoft Teams on a six-layer system: company memory, knowledge map, file intake, profiles, tool connectors, and observability.
4 AI employees live in Teams3 core systems in production90 days idea to foundation
AI_EMPLOYEESKNOWLEDGE_GRAPHTEAMS_CONNECTIONWORKFLOW_SYSTEM
PRODUCTION EVIDENCEHEALTHCARE ENTERPRISE · DEPLOYED DEMO
93%
expected first-tool routing
26 of 28 test cases routed to the expected first tool call through the live streaming pipeline (92.9%).

AgentHub: a prompt library with a contract-review system

CHALLENGE
Knowledge workers had no shared home for prompts that work, and no fast, trustworthy way to interrogate dense Statement-of-Work documents.
SOLUTION
A department-organized prompt library paired with an SOW review system. Parallel vector and knowledge-graph retrieval supports field-level citations, while the chat can return charts, tables, and drafted documents.
Field-level citations in drafted SOW sections50-SOW grounded corpus7 generative-UI widgets
HYBRID_RAGKNOWLEDGE_GRAPHGENERATIVE_UIINTENT_ROUTING

What a typical engagement looks like.

The scenarios below are labeled composites. They show representative architectures. Performance figures are reserved for verified evidence. The two engagements above are production work with their current evidence status shown.

ILLUSTRATIVE SCENARIOFINANCIAL SERVICES
SCENARIO
representative workflow pattern

120,000 documents, one knowledge graph

CHALLENGE
Decades of contracts, policies and memos scattered across shares. Compliance answers took days of manual digging and senior staff time.
SOLUTION
Full ingestion pipeline: files chunked and embedded into a vector DB, entities and obligations extracted into a knowledge graph. Hybrid RAG with citations, deployed behind their firewall.
Illustrative time-to-answer modelCitation-backed retrievalOn-prem deployment pattern
HYBRID_RAGKNOWLEDGE_GRAPHON_PREM
ILLUSTRATIVE SCENARIOLEGAL
SCENARIO
representative workflow pattern

An AI intake employee in the shared inbox

CHALLENGE
Intake requests arrived by email at all hours. Partners triaged manually; slow responses were losing engagements to faster firms.
SOLUTION
An AI employee connected to the shared inbox through an approved email connection. It triages intake, drafts engagement letters from precedent, and escalates edge cases with a full audit trail.
Human-reviewed intakeCited draft responsesDocumented escalation path
AI_EMPLOYEEEMAIL_CONNECTIONWORKFLOW_SYSTEM
ILLUSTRATIVE SCENARIOMANUFACTURING
SCENARIO
representative workflow pattern

Local AI on the factory floor

CHALLENGE
Cloud AI costs scaled with every query, and sensitive process data was leaving the building to third-party APIs.
SOLUTION
Right-sized GPU hardware on site, open models fine-tuned on their procedures, and a controlled workflow system connected to Teams for floor supervisors.
Modeled cost comparisonNo external data transferWorkload-based capacity plan
LOCAL_AIOPEN_MODELSTEAMS_CONNECTION

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