AI Strategy & Roadmap in Reston, VA

The core deliverable is a ranked capability roadmap with a build-vs-buy decision on each shortlisted item, plus a Phase 1 scope brief written for any builder or contracting vehicle to execute against. For federal IT contractors specifically, that roadmap has to account for a few things that commercial strategy work often skips: data classification and what it rules out for commercial model APIs, CMMC posture and how it affects which automation tools are even on the table, and the difference between internal productivity automation (which has a faster path) versus client-facing AI capabilities that require a separate compliance review. We map all of that during intake. The output isn't a generic AI roadmap — it's a prioritized list with effort, revenue impact, and compliance risk scored for each candidate, so the program director can walk into a budget conversation with a defensible recommendation rather than a technology wish list.

Build-vs-buy for a SaaS product team has a different shape than the same question for an internal ops team. On the product side, the key variables are time-to-ship, differentiation value, and model dependency risk. Buying a model layer from a major provider is fast, but it creates a margin compression problem as that provider moves upmarket and a lock-in problem if the provider's terms change. Building in-house is slower but preserves differentiation and controls the cost curve. We score each candidate capability against those dimensions during the workshop, and we pull in the vendor contract landscape — what the major AI platform providers are actually offering on enterprise terms right now — so the decision is based on real options, not theoretical ones. The output is a recommendation with the reasoning documented, not just a conclusion. Your CTO should be able to read the reasoning and disagree with a specific assumption if they want to, not just accept or reject the conclusion.

It's a real deliverable. The audit produces a written report covering your current operational stack, the workflows with the highest automation potential, the data and compliance constraints that affect which AI approaches are viable, and a ranked list of three to five candidate capabilities with preliminary effort estimates. That report takes a few days to produce and is written to stand alone — it's the document you'd share internally to align stakeholders before any build conversation starts. The only thing the audit doesn't include is the full build-vs-buy analysis and Phase 1 scope brief, which require the deeper intake of a workshop or Founder Review Call. Some clients run the audit and use it to make decisions internally without ever engaging further. That's fine. The $99 isn't structured to create obligation — it's structured to produce something useful at a price that doesn't require a procurement cycle to approve.

Yes, and this comes up constantly with Reston clients because of the concentration of cybersecurity firms, federal contractors, and healthcare-adjacent organizations in the area. The short answer: the strategy engagement itself handles no production data. We work from documentation — process maps, workflow descriptions, system architecture diagrams, and organizational context — not live data extracts. When the strategy output includes an implementation recommendation, we spec the data handling architecture as part of the Phase 1 scope brief, including whether the capability requires a private deployment, a FedRAMP-authorized provider, or a fully on-prem model. If the build would touch classified or highly sensitive data, we say so in the roadmap and note what that means for vendor options and timeline. The strategy work is designed to surface those constraints before any implementation budget is committed, not after a vendor has already been selected.

The sweet spot is companies with ten to two hundred employees — small enough that the founder or CTO is still in the room for strategy decisions, large enough that there's a real operational problem worth solving. In Reston and the surrounding NOVA corridor, that typically means federal IT contractors with a handful of active programs and a proposal shop that's working at capacity, enterprise SaaS companies with a product roadmap that includes AI features they haven't scoped yet, and cybersecurity firms evaluating whether to add AI-assisted detection or response capabilities to their product stack. We also work with larger organizations for specific, bounded strategy questions — a division of a larger firm that needs a roadmap for one product line, for example. What we don't do well is open-ended AI transformation programs for large enterprises where the strategy engagement is the beginning of a multi-year consulting relationship. That's a different business model and a different set of consultants.