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1---
2title: "Enterprise GenAI for People Operations | Case Study"
3description: "How we consolidated people ops knowledge across a PE roll-up into one SharePoint repository and deployed a generative AI assistant for the entire enterprise."
4url: https://appstream.studio/case-studies/enterprise-genai-people-operations
5markdown_url: https://appstream.studio/case-studies/enterprise-genai-people-operations.md
6---
7 
8[Home](https://appstream.studio/index.md) · [Work](https://appstream.studio/case-studies.md) Private Equity
9 
10Private Equity/ Engineer · Operate
11 
12# One AI assistant. Every acquired company's HR knowledge. One place to ask.
13 
14A PE-backed study abroad provider had acquired multiple companies, each with its own HR systems, policies, and tribal knowledge. We consolidated everything into SharePoint and built a generative AI assistant that answers people ops questions across the entire enterprise.
15 
16Client
17 
18PE-Backed Enterprise
19 
20Engagement
21 
22Ongoing
23 
24Focus
25 
26RAG + AI Assistant
27 
28Compliance
29 
30Enterprise data governance
31 
32![Enterprise generative AI for people operations across a PE roll-up](https://appstream.studio/enterprise-genai-people-operations.png)
33 
3401
35 
36## The work
37 
38One of the nation's largest private equity-backed study abroad program providers, formed through serial acquisition. Multiple companies operating under one umbrella, each with its own systems, policies, and institutional knowledge.
39 
40What was getting in the way
41 
42- Every acquisition brought its own HR systems, policy documents, and SOPs. Nothing was consolidated.
43- Employees had no single place to find answers. They had to know which legacy system from which acquired company held the information.
44- Every new deal compounded the problem. More systems, more doc formats, more policies that may or may not align.
45- Finding basic HR information meant knowing who to ask, not where to look. Institutional memory was the search engine.
46- The people ops function couldn't scale with the acquisition pace. Each integration was manual discovery and reconciliation.
47 
4802
49 
50## What changed
51 
52Consolidate everything. Then put AI on top.
53 
54We started by mapping the people operations landscape across every acquired entity. Where documentation lived, what formats it was in, what questions employees were actually asking, and which sources they were using or failing to find.
55 
5603
57 
58## What we built
59 
60We deployed an enterprise SharePoint system as the single repository, organized by function and policy area. All people ops documentation from every acquired company — SOPs, benefits, policies, procedures — migrated, structured, and indexed for AI retrieval.
61 
62Then we built the generative AI assistant on top of it. Employees ask natural language questions about HR, benefits, policies, or procedures and get answers grounded in the company's actual documentation. Not generic responses. Sourced, verifiable answers from indexed content. And when the next acquisition closes, their docs get added to the same knowledge layer.
63 
64AI Agents RAG Pipelines SharePoint Architecture Knowledge Management
65 
6604
67 
68## How it works
69 
70- All people ops documentation consolidated from multiple acquired companies into one enterprise SharePoint repository
71- Generative AI assistant deployed enterprise-wide for natural language HR queries
72- Answers grounded in actual company documentation, not generic AI responses
73- Repeatable onboarding process for new acquisitions — add their docs, they're searchable
74- HR teams fielding fewer basic information requests. Employees finding answers on their own.
75- First enterprise AI deployment for the company, with architecture designed to expand beyond people ops
76 
7705
78 
79## Business impact
80 
81of employees
82 
83100%
84 
85AI assistant deployed across every acquired company
86 
87knowledge layer
88 
891
90 
91Consolidated from scattered systems across multiple acquisitions
92 
93rebuild per deal
94 
950
96 
97New acquisitions onboarded into the same knowledge layer
98 
99> “We had HR information scattered across every company we'd acquired. Now any employee can ask a question and get the right answer, sourced from the actual policy. It scales every time we do another deal.”
100 
101Ryan Sylvester , CIO, CEA CAPA
102 
10306
104 
105## What’s next
106 
107Turn acquisition sprawl into AI-powered access
108 
109If your portfolio companies have fragmented knowledge across systems and acquisitions, we've built the playbook to consolidate and deploy AI on top.
110 
111[Talk to an AI engineer](https://appstream.studio/schedule-a-discovery-call.md) · [More work](https://appstream.studio/case-studies.md)
112 
113Team
114 
115- Justin Tannenbaum Solutions Architect
116- Lukasz Chmielewski Lead Engineer
117- Cesar Bustamante Engineering
118- Taylor Larson Product Manager
119- Ernesto Quispe Product Design
120 
121Stack
122 
123.NET · Azure · SharePoint · M365 · Semantic Kernel · MAF
124 
125Related
126 
127- [Engineer](https://appstream.studio/solutions/engineer.md)
128- [Operate](https://appstream.studio/solutions/operate.md)
129- [Agent Engineering](https://appstream.studio/solutions/agent-engineering.md)
130- [Agent Knowledge Layer](https://appstream.studio/solutions/agent-knowledge-layer.md)
131- [Private Equity](https://appstream.studio/industries/private-equity.md)
132 
133More work
134 
135## Keep reading.
136 
137[All work](https://appstream.studio/case-studies.md)
138 
139- [Healthcare/ Engineer · Operate A scheduling engine on a legacy EMR books 250,000 appointments a year. Read the case study](https://appstream.studio/case-studies/healthcare-scheduling-transformation.md)
140- [Healthcare/ Engineer AI scores reimbursement likelihood before a medical device ships. Read the case study](https://appstream.studio/case-studies/ai-reimbursement-eligibility-engine.md)
141- [Proptech/ Engineer A 50-state buy-side marketplace, with search that understands intent. Read the case study](https://appstream.studio/case-studies/buy-side-marketplace-agentic-search.md)
142 
143---
144 
145## AppStream Studio
146 
147AppStream turns frontier AI capabilities into production systems that perform real work. Talk to an AI engineer: https://appstream.studio/schedule-a-discovery-call · Email: contact@appstream.studio
148 
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153- [Operate](https://appstream.studio/solutions/operate.md)
154- [Govern](https://appstream.studio/solutions/govern.md)
155- [How we work](https://appstream.studio/how-we-work.md)
156- [Work (case studies)](https://appstream.studio/case-studies.md)
157- [Industries](https://appstream.studio/industries.md)
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162