The Challenge
A financial planning and analysis firm had years of institutional knowledge scattered across SharePoint folders — past models, memos, and methodologies that new analysts couldn't find without asking someone who'd been there long enough to know where to look. New hire ramp stretched to six months. Experienced staff spent disproportionate time fielding knowledge requests. The firm's intellectual capital was effectively locked away from the people who needed it most.
What They Built
AlongsideAI built a custom RAG chatbot integrated directly into the firm's SharePoint environment, allowing analysts to query years of past models, memos, and methodologies in plain language — with cited source documents and role-based access controls preserving data security.
AlongsideAI built a retrieval-augmented generation chatbot integrated directly into the firm's SharePoint environment. The key architectural decision was to work within the existing document infrastructure rather than migrating content to a new system — preserving permissions, reducing change management, and connecting the RAG layer to the actual location of the firm's institutional knowledge. Analysts submit queries in plain language and receive answers sourced from past financial models, memos, and methodologies. Responses include citations, so analysts can drill into the underlying source document when they need to verify or expand on an answer. Role-based access controls were carefully configured to mirror existing SharePoint permissions, ensuring that sensitive materials remained appropriately gated in the new system. The firm's unstructured folder organization was the primary technical challenge: years of documents with no consistent taxonomy required significant work in indexing and retrieval tuning before query quality reached an acceptable level. Post-launch, the unexpected outcome was commercial: the firm became an AlongsideAI white-label partner, deploying the system to their own clients and turning an internal efficiency tool into a new line of business.
AI Role
New hire ramp time collapsed as analysts could access institutional knowledge independently from day one, dramatically reducing the cost of onboarding.
AI Model
Custom / proprietary
Infrastructure
• RAG architecture (custom build) • SharePoint (document repository and permissions layer) • Historical financial models, memos, and methodologies (knowledge corpus)
Integration Points
• SharePoint → RAG indexing layer (document ingestion) • SharePoint permissions → role-based access control in RAG system • RAG chatbot → analyst-facing query interface with citations
FP&A firms, financial advisory practices, and consulting organizations with years of institutional knowledge locked in document repositories and long new-hire ramp times.
