How an FP&A Firm Cut Knowledge Searches to Seconds
A financial planning firm's analysts piped years of SharePoint memos into a RAG chatbot with citations — replacing hours of senior-colleague pings with seconds-long natural language queries.
Seconds, Not Hours
Saves hours per knowledge search
Evan Glaser
Founder & CEO
Alongside AI
Connect ↓
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. Analysts ask questions in plain language and get answers drawn from the firm's actual past work — with citations, so they can drill into source documents when needed. Role-based access controls keep sensitive materials appropriately gated. Training time collapsed. But the bigger surprise came post-launch: the firm became a white-label partner, deploying the AlongsideAI system to their own clients — turning an internal efficiency play into a new line of business.
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
The AI operates as a retrieval-augmented generation chatbot integrated with the firm's SharePoint environment, enabling analysts to submit plain-language queries and receive answers drawn directly from the firm's historical work — models, memos, and methodologies — with citations linking back to source documents. Role-based access controls are enforced at the retrieval layer to ensure sensitive materials remain appropriately gated.
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
Impact
Knowledge that previously required asking a senior colleague — or hours of manual searching — became retrievable in seconds via natural language query.
New hire ramp time collapsed as analysts could access institutional knowledge independently from day one, dramatically reducing the cost of onboarding.
The client firm became a white-label distribution partner, deploying the AlongsideAI system to their own clients and generating a new revenue stream from the engagement.
Implementation Complexity
The core RAG architecture is well-established, and integration with SharePoint is a standard use case. Moderate complexity comes from configuring role-based access controls, indexing the firm's historical document corpus, and tuning retrieval quality across varied document types such as financial models and memos.
Best Fit For
FP&A firms, financial advisory practices, and consulting organizations with years of institutional knowledge locked in document repositories and long new-hire ramp times.
