AI Workflow Implementation in Arlington, VA

Yes — Deltek Costpoint and Unanet both expose official APIs that we use to read and write the records that touch automated workflows. The integration runs through a scoped service account with permissions limited to the modules and projects the build actually needs, and every read or write is logged so the audit trail stays intact for cost-accounting and CO review. We do not modify timekeeping records that have already been submitted or approved — automation lives upstream of the formal timekeeping submission, not inside it. Common shapes we have built: pulling project IDs and labor categories into a proposal-staffing draft, surfacing burn-rate snapshots into a weekly PM dashboard, and validating that a new project setup matches the contract structure and CLIN structure before it goes live. Final review and submission stays with the human PM or accountant — the workflow saves them the data-gathering hours, not the judgment calls.

The standard build window is two to three weeks from kickoff to handover, which is engineered to fit inside a typical commercial capture or proposal cycle rather than collide with one. Week one is prototyping against your data — we connect to a sandbox or read-only copy of the systems involved, build the workflow logic, and put a working version in front of the program lead by Friday. Week two is live integration and edge cases, with the workflow deployed against production credentials but feature-flagged so it can be rolled back instantly. Week three, when needed, is documentation, training, and the formal handover to the team that will run it. If a capture deadline is in flight, we time the kickoff to land the live cutover after the proposal goes in, not before. Boring scheduling discipline is part of why the timeline holds.

The honest answer: it depends on the data classification and the deployment target. For restricted client information, we do not route content through public-cloud LLM endpoints — that pathway is reserved for unmarked or already-public data. For sensitive data workloads, the build deploys against models with security-reviewed authorization (enterprise AI processing endpoint in the compliance-ready cloud region, HIPAA-eligible AI infrastructure with the appropriate boundary, or an on-prem inference layer the firm already operates). For restricted-access but unrestricted-access content, we use enterprise endpoints with signed zero-retention DPAs in place. We do not handle restricted workloads — anything above sensitive client data sits outside our scope and requires a security-sensitive facility and accredited infrastructure we are not set up to provide. The compliance officer signs off on the data-flow diagram before any credential changes hands.

The integration map for security-sensitive Arlington shops is fairly consistent. On the ERP and finance side: Deltek Costpoint, Unanet, and occasionally Sage Intacct for non-commercial divisions. On BD and capture: market research platform, procurement data source, public opportunity source for entity and opportunity data, and Salesforce or HubSpot as the CRM of record. On document control: NetDocuments, document repository, and productivity suite with Information Protection labels intact. On collaboration: team chat or video meeting, depending on the firm's standardization. We integrate through official APIs with scoped service accounts — never browser scraping, never shared user credentials. If the firm runs an on-prem or self-hosted system that does not expose a clean API, we either deploy a connector inside the network perimeter or scope the workflow around what the system can export. The data-flow audit happens before the build kicks off, not after, so there are no surprises at go-live.

Both, depending on what saves the firm time. The default engagement is remote with scheduled video sessions for kickoff, weekly progress reviews, and the final handover walkthrough — most of the work is integration and code, which does not benefit from a conference room. That said, we are based in the DMV and do on-site sessions in Arlington, Tysons, Reston, McLean, and broader Northern Virginia when the project genuinely benefits from face time. The cases where on-site matters: kickoff workshops where five stakeholders need to map a workflow on a whiteboard before we can scope it, security reviews where the firm's IT team prefers to credential a service account in person, and post-launch training sessions where a partner or PM wants the team trained as a group rather than over Zoom. Compared to most ai consulting engagements that sell a strategy deck and leave implementation to whoever you hire next, our on-site days are scoped into the engagement upfront, not billed surprise.