Knowledge Systems & AI Assistants in Alexandria, VA

Both, depending on the source and the cost profile the client wants to run. For Drive, SharePoint, NetDocuments, and Notion, we wire change-event hooks where the platform exposes them — Drive push notifications, SharePoint webhooks, NetDocuments activity feeds — so a touched document re-indexes within minutes. Sources without reliable webhooks (legacy file shares, certain on-prem DMS deployments, custom document repositories) run on a scheduled delta scan, usually nightly off-hours, with a weekly full rebuild as a safety net to catch deletes and permission changes the delta might miss. The retainer covers monitoring this — when re-index latency drifts because a folder grew tenfold or a webhook stopped firing, we catch it before the partner gets a stale answer in front of a client.

Yes, and matter-level scoping is the first thing we wire — not the last. The assistant's retrieval layer respects the existing ethical-wall and matter-level permissions in the DMS, so a build for the litigation group cannot pull documents from corporate, family-law, or any matter the requesting attorney could not open manually in NetDocuments or iManage. Every answer ships with citations back to the source document and page or paragraph, so the attorney can verify the retrieval before any client-facing use. If the firm runs on-prem iManage or has a self-hosted document repository, the integration layer deploys inside the firm's network — privileged content never leaves the perimeter for indexing or processing. Final responsibility for the output stays with the licensed attorney; the assistant is a research accelerator, not an autopilot.

Clinical and behavioral-health clients get a different stack from day one — not a bolt-on after launch. The deployment runs on AWS Bedrock in a region with a signed BAA, private vector storage inside the client's own AWS account or a dedicated tenant we manage under BAA, and prompt and output logging configured to meet PHI handling requirements with no third-party model retention. We do not route PHI through general-purpose model endpoints, even with zero-retention terms — the architecture is segregated. Access is scoped to clinicians authenticating against the client's existing identity provider, audit logs route to the client's SIEM or to CloudWatch with retention configured per the client's compliance policy, and the build documentation includes the data-flow diagram and risk assessment the client's compliance officer needs for their HIPAA security risk analysis. Non-clinical lines of business at the same organization can run on a separate, lower-cost deployment if the PHI boundary is clean.

Three separate scoped deployments, one shared engineering relationship. The reason is not technical, it is operational: each line of business has different access rules, different retention requirements, and different question patterns the assistant has to be tuned against. The legal group needs matter-scoped retrieval and privilege controls. The federal-contractor arm needs cleared-versus-uncleared document boundaries and contract-mod-aware indexing. The clinical practice needs HIPAA-compliant infrastructure with no shared model routing. Trying to merge those into one assistant produces a tool that is mediocre at all three and failure-prone at the worst possible moment. The economical path is to build the highest-ROI one first as a 3-4 week fixed-price engagement, then bring the second online six to eight weeks later reusing the same indexing pipeline and engineering team, then the third. Same retainer covers all three after launch — one relationship, three scoped systems.

Useful answers in week three, production rollout in week four for a single line of business. Week one is the document audit — we sit with the team and watch them try to answer real questions from the existing system. That uncovers retrieval scope: which folders, document types, excluded categories. The client signs off on what is in and out of scope, and indexing starts. Week two wires the retrieval pipeline against the live source and stands up the answer interface. Week three is the answer-quality pass: the client team writes 30 to 50 real questions, the assistant answers, the team grades each response on retrieval accuracy and citation quality, and we tune until the pass rate clears the threshold set on day one. Week four is rollout, training, and runbook handover. Team time investment is roughly four to six hours across the four weeks, mostly in the audit and the answer-quality pass. Worth flagging: this is not a generic AI chatbot pulling from stale training data — it is a retrieval-grounded assistant that only answers from your indexed documents and cites the source paragraph, the difference between a research tool attorneys can rely on and a malpractice risk.