What ArchIQLabs brings
ArchIQLabs pairs strategy with delivery. I bring 20+ years across enterprise software and federal IT, so the strategy comes from someone who has built the systems, not only advised on them.
Solutions I've built
A few of the solutions behind the capability. Most were built for clients, so the client stays unnamed and the details are kept general. What's shown is the capability and the pattern, not anyone's confidential system.
ArchIQ: my AI strategy platform
Instead of asking each system "what can AI do here?" and getting siloed features, start from the business and surface the connected opportunities that span it. ArchIQ does this with specialist agents that read your discovery material and rank each one by impact and feasibility.
Multi-agent judging, in the open
Evaluation that stays consistent and defensible at scale, so scoring dozens of submissions never rides on one tired reviewer. A panel of agents scores each hackathon submission across architecture, AI integration, and production-readiness, shows its reasoning, and runs in public on AWS Bedrock AgentCore.
A regulated agency, on its own terms
EA teams usually arrive at stakeholder meetings with questions and collect answers by hand. This flips it: a multi-agent system pre-aggregates from email, chat, SharePoint, and the EA tool, so stakeholders just validate, running entirely inside the agency with no data leaving.
Grounded answers, wherever the knowledge lives
When an AI answers too generically, the fix is usually a curated library it can cite, not a bigger model. I've built this retrieval pattern across three very different bodies of knowledge: an internal proposal library, an organization's scattered procedures and SOPs, and a public government website. Each returns a specific, cited answer instead of a confident guess.
Find and stop the waste in AI spend
A concept I've designed for agency CIOs facing AI sprawl across clouds. Specialist agents find where the dollars leak and map every finding to a lever, Align, Reduce, or Maximize, grounded in each cloud's own well-architected guidance, like a credit-card statement for AI spend.
Speeding up EA review of acquisitions
For a federal health agency's EA team, a first pass on an acquisition strategy document: a short executive summary plus a table mapping it to the standard EA requirements. The reviewer keeps the call, and the same pattern repoints at any recurring document the agency reviews.
The common toolkit
Every solution above is built from the same set of techniques, applied to a different problem each time: multi-agent systems, retrieval, evaluation, and fine-tuning, delivered on whichever cloud fits and tied to no vendor.
What I help leaders do
Across all of that, the work usually falls into four kinds of help. Whichever one a project needs, it's the same person doing the strategy and the building.
Set the AI strategy
Where AI fits across the enterprise, what's worth doing first, and how to sequence it. The architecture-level view that turns "we should use AI" into a roadmap you can act on.
Turn strategy into working systems
The plan comes with delivery, not just a deck. Because I build, I can take a decision from the whiteboard to a running, governed system.
Govern and de-risk it
An honest read on feasibility, cost, and risk before anyone commits, and the governance that keeps AI trustworthy once it's in production.
Grow adoption
Getting teams to actually use AI, not just buy it. From enablement on managed tools to the patterns that make it stick across the organization.