Knowledge Systems & AI Assistants in Gaithersburg, MD

For a federal contractor, the most common build is a proposal-history assistant: an internal tool that lets a proposal manager query past performance narratives, technical approaches, and capability statements in plain English and get cited answers back from the firm's own document library. The engineering work has three phases. First, document audit — we inventory the existing library, identify which formats are in play (PDFs, Word docs, SharePoint pages, email attachments), and define the retrieval scope. Second, index build — documents are chunked, embedded, and stored in a private vector database with metadata tagging for contract vehicle, agency, and recency. Third, retrieval tuning — we run evaluation sets of real proposal manager questions against the index and adjust chunking strategy and ranking weights until precision is high enough that staff trust the output without second-guessing every citation. The finished tool lives inside your network or on a private cloud deployment, and proposal managers query it through a Slack integration or a simple web interface. Build window is 3–4 weeks.

Clinical and regulated clients in the I-270 corridor get a deployment path on AWS Bedrock with private vector storage — no data leaves the compliance boundary for embedding or inference. The architecture uses Bedrock's managed embeddings and a private OpenSearch Serverless vector store inside a VPC, so PHI and regulated CMC documentation never touch a third-party embedding API. We map every data flow in writing during the audit phase, and the engagement includes a data handling annex the client's legal and compliance team can review before any credential changes hands. For biotech CMC use cases specifically, the index is built to handle the cross-document citation pattern that regulatory submissions require: a query about a deviation event should pull the deviation report, the relevant SOP, the batch record, and any prior FDA correspondence that references the same process parameter — cited back to document, section, and page. That retrieval precision is what makes the system useful under submission pressure rather than a liability.

Engagements run 3–4 weeks from signed scope to handover. What you need to provide: access to the document sources you want indexed (SharePoint, Google Drive, Notion, a local network share, or a curated file export), a named internal owner who can answer questions during the audit, and two or three examples of the questions your team would realistically ask the system. We handle the rest — document parsing, chunking, embedding, index build, retrieval tuning, interface development, and runbook writing. The internal owner's time commitment is roughly two to three hours in week one for the audit and scoping conversation, and a one-hour walkthrough in week four for the handover. If you have regulated data handling requirements, add a half-day with your legal or compliance contact in week one to review the data handling annex. You don't need a technical team in place to receive the build — the runbook is written for an operations or admin owner, not an engineer.

Yes — most Gaithersburg builds index two to four document sources simultaneously: a SharePoint library, a Google Drive folder structure, a Notion wiki, and sometimes a structured database of historical records. Source diversity is handled at the metadata layer: every chunk in the index carries a source tag, a document date, and an authority weight that the retrieval pipeline uses when the same question could be answered by multiple documents. For policy and procedure documents where the most recent version should always win, we apply recency weighting that surfaces the latest SOP over an older one with higher keyword overlap. For cases where two sources genuinely conflict — say, an internal procedure that contradicts a regulatory guidance document — the system surfaces both with their source citations and flags the conflict rather than picking one silently. That's a deliberate design choice: the assistant's job is to give staff cited, traceable answers, not to make judgment calls that belong to a licensed professional or a compliance officer.

Three things drive maintenance needs: document changes, upstream API changes, and retrieval drift. Document changes are the most common — every time a major SOP is revised, a new contract is awarded, or a regulatory guidance document updates, the relevant chunks need to be re-indexed. The runbook we hand over covers this as a manual process a non-technical owner can run, or it can be automated with a scheduled re-index job if the document sources have reliable change detection. Upstream API changes affect the integration layer — if SharePoint or Notion updates their API, the ingestion connector may need a patch. This is rare but happens once or twice a year with major platform updates. Retrieval drift is subtler: as your document library grows and your staff's query patterns evolve, the retrieval precision you had at launch may drift if the index isn't periodically re-tuned against current usage. Our optional retainer covers all three: scheduled re-indexing, integration maintenance, and quarterly retrieval quality reviews. Clients who don't take the retainer handle this themselves with the runbook — most manage fine for the first year without needing us back.