AI Strategy & Roadmap in Tysons, VA

Federal IT integrators face a specific version of the build-vs-buy problem that commercial AI strategy consulting rarely addresses well. The question isn't just "should we build or buy?" — it's "can this vendor operate inside a FedRAMP Moderate boundary, who holds the ATO, and what happens to model outputs when they're incorporated into a deliverable for an agency customer?" Our strategy engagements for federal-adjacent organizations include a deployment-context analysis as part of the build-vs-buy work. That means mapping each candidate capability against its likely deployment environment — GovCloud, on-prem, air-gapped, or commercial — and flagging the compliance dependencies before they become program blockers. The Phase 1 scope brief we produce is written to be legible to a contracting officer, not just an engineering lead. That's not a small distinction when the next step involves a task order modification or a new CLIN.

SR 11-7 — the Federal Reserve and OCC guidance on model risk management — applies to any model used in a bank's decision-making processes, which now includes most AI applications touching credit, fraud, operations, or customer service. Our AI strategy engagements for banking clients in the Tysons and McLean corridor build model risk documentation requirements into the roadmap from the start, not as a retrofit. That means each candidate capability in the ranked roadmap includes an initial classification: is this a model under SR 11-7's definition, who is the model owner, what validation process does it require before deployment, and how does it get documented in the model inventory? We're not a model validation firm — that work stays with your internal model risk team or your designated validator. But the roadmap we deliver gives them a clear picture of what's coming before engineering starts, which is the conversation that usually gets skipped when AI initiatives are driven by business lines without compliance at the table.

Yes, and we've written Phase 1 scope briefs specifically for that handoff. If you're already contracted with a systems integrator for implementation work — which is common in the Tysons federal and enterprise market — the strategy engagement produces a brief that integrator can execute against. That means a capability specification, a data dependency map, a build-vs-buy recommendation with rationale, and an initial technical approach that a competent engineering team can scope and price without starting from scratch. We don't require that implementation work flow to us. The deliverable is designed to be portable. If you do want us to build Phase 1, that's scoped separately after the strategy work closes. Some clients do both. Many use the roadmap output with their existing implementation resources. Either outcome is a valid result of the engagement.

The two-day workshop works when the problem is reasonably bounded — one business unit, a clear set of candidates already on the table, and a core team of three to five people who can make or influence the decision. We run a structured intake before the workshop to pre-load context, then use the two days for capability mapping, scoring, and build-vs-buy analysis. You leave with a ranked list, a Phase 1 scope, and a written summary. Total elapsed time from kickoff to deliverable is typically ten to fourteen days including scheduling. The two-week deep dive is appropriate when the decision space is wider: multiple program lines or business units, a vendor selection with long-term contract implications, a board-level AI strategy that needs defensible analysis behind it, or a situation where the compliance and technical stakeholders haven't been in the same room yet. The deep dive includes structured interviews across teams, scenario modeling for the top candidates, and a more detailed Phase 1 brief that accounts for a broader range of implementation paths.

For most Tysons enterprise teams, the $99 AI readiness audit is the right first step before committing to a strategy workshop or deep dive. The audit produces a written report — not a slide deck, not a capabilities briefing — that maps your current stack, your data flows, your compliance environment, and the AI candidates already in circulation internally. For teams that have been through multiple vendor conversations without a clear path forward, the audit gives you an independent read on what's actually buildable given your constraints, what the effort-to-impact spread looks like across your candidate list, and where the blockers are before you put budget against a build. Most enterprise teams find that the audit either confirms their instincts about where to focus — in which case the strategy workshop follows quickly — or surfaces a constraint or opportunity they hadn't accounted for, which changes the workshop scope. Either way, ninety dollars is a low-cost way to walk into the strategy conversation with a written baseline instead of a vendor's pitch deck.