Knowledge Systems & AI Assistants in Reston, VA

Inside your tenant by default. For Reston operators on a Microsoft commercial or GCC subscription, the standard deployment uses Azure OpenAI, Azure AI Search, and a private vector store provisioned in your subscription — documents are indexed and retrieved without ever leaving the tenant boundary. We use your existing Entra ID for auth, your existing SharePoint and Confluence connectors for ingestion, and your existing storage account for the vector index. No third-party SaaS hop, no cross-tenant data movement, no separate vendor DPA to negotiate. For firms with stricter requirements (CUI handling, customer NDAs that prohibit any external processing), we can deploy entirely on Azure Government or air-gap the build inside a customer-isolated subscription. The architecture decision happens during the audit, before any data moves.

Yes — scoped retrieval is the default, not an add-on. During the document audit phase, we map every source repository to the access-control taxonomy your firm already enforces: clearance level (public, internal, customer-restricted, cleared-personnel-only), customer or matter ID, project code, or NAICS-and-vehicle pairing for federal capture work. Those scopes propagate into the vector index as metadata filters, and the assistant enforces them at query time based on the authenticated user's Entra ID group membership. A SOC analyst querying for one customer's runbooks will not surface another customer's playbooks even if the underlying documents are technically in the same index. For cybersecurity firms with strict customer-segregation requirements, we can also deploy separate per-customer indexes and route queries based on the user's project assignment.

Yes, and this is the part that catches most off-the-shelf RAG SaaS tools off-guard. For cybersec firms rotating advisories weekly and amending customer runbooks after every engagement, we configure incremental re-indexing on a daily or hourly cadence depending on the source. New documents and edits are picked up automatically through the SharePoint or Confluence change feed, embeddings are recomputed only for the affected sections (not the full corpus), and stale versions are retired so the assistant doesn't surface superseded guidance. For federal capture libraries where the velocity is lower but accuracy matters, we run a weekly re-index with a manual review gate before new past-performance write-ups go live. Drift in retrieval quality is the silent failure mode of knowledge systems — the retainer is what keeps it from happening.

It starts with the $99 audit — a structured review of where your institutional knowledge lives, how it's accessed today, and where retrieval failures cost real time. From that audit, we produce a written assessment that maps candidate use cases (runbook search, capture library, internal helpdesk) against effort, retrieval risk, and expected time-to-value. That assessment is the deliverable whether you build with us or not. If you move forward, the ai chatbot development work runs in a 3–4 week sprint: document ingestion, access-control mapping, retrieval pipeline build, and UAT with your target users. The ai knowledge assistant that ships is scoped to the highest-value use case the audit identified — not a general-purpose tool that tries to answer everything and fails at the things that matter. Reston operators who've been through a round of enterprise AI consulting with larger firms tend to find the audit conversation useful because it's specific to their corpus and tenant architecture, not a vendor pitch for a SaaS platform.

Three to four weeks from signed scope to production deployment, on the standard pattern. Week one is document audit and architecture: we inventory the corpus, map access controls, agree on the retrieval scope, and stand up the Azure AI Search index plus connectors. Week two builds the retrieval and synthesis pipeline against a curated subset of documents and validates accuracy with the partner reviewing the engagement. Week three wires the assistant into the real corpus, runs scoped UAT with two or three target users, and tunes prompts against real queries. Week four is documentation, security review, training for the team, and go-live. After launch, the retainer covers re-indexing pipeline monitoring, prompt and retrieval tuning when the corpus shifts, scope-filter additions when new customers or projects come online, and quarterly retrieval-quality reviews so accuracy doesn't drift.