Qylogix provides fractional CTO leadership and AI systems architecture for pre-seed through Series B startups. We bridge the gap between experimental R&D and production-grade reality: resolving architectural bottlenecks, controlling inference spend, and building infrastructure your next round can stand on.
You do not need a full-time CTO yet. You need senior technical judgment, applied at the moments where it compounds. You are in the right place if any of these read true:
Executive technical leadership without the full-time hire. Blueprinting the path from early prototype to enterprise-grade system: build-vs-buy decisions, technical hiring, vendor selection, and an investor-grade technical narrative.
Small language models, custom compute, and hybrid architectures that keep latency, cost, and sensitive data close to the metal. The cloud gets called only when the workload earns it.
Persistent AI workflows on hardened, POSIX-compliant infrastructure. We transform fragile generative demos into observable, recoverable systems that operate continuously at scale.
A focused assessment of your architecture, AI roadmap, and infrastructure spend. You get a written blueprint, a prioritized backlog, and a technical story you can defend to your board or your next investor.
Embedded technical leadership. Architecture ownership, hiring and vendor decisions, engineering mentorship, and a senior operator at the table for board meetings and diligence.
An independent read on a technical stack or an AI claim. For founders preparing to raise, and for investors who need the demo separated from the deliverable.
You bring where you are and where the architecture hurts. We map it to the right engagement, or tell you honestly if you don't need us yet.
Architecture, roadmap, and infrastructure spend under review: access, artifacts, and the questions your team hasn't had time to ask.
Decisions made and documented. A written architecture blueprint, a prioritized backlog, and a cost model your CFO can read.
A board-ready narrative and an execution plan your team owns. Continue into a retainer, or run with it yourselves.
Designed a local-first inference architecture with selective cloud escalation for a clinical-environment product. On-device models carry the always-on workload; the cloud is reserved for calls that justify their cost. Result: a hard ceiling on per-device spend and sensitive audio that never has to leave the room.
Built an LLM routing layer across a heterogeneous GPU fleet, from datacenter-class nodes to consumer cards. Each request is matched to the cheapest endpoint capable of serving it, with embeddings, utility, and frontier tiers separated by design.
Stood up a managed tablet fleet for a clinical pilot: MDM enrollment, cellular connectivity, and a repeatable deployment runbook a non-technical team can execute without an engineer on site.
Jason helped us define the core AI strategy for Mandala, specifically focusing on GPU optimization and scalable cloud architecture. His guidance was instrumental in setting up our initial roadmap and ensuring we were focused on the highest impact areas.
Jason provided valuable technology strategy to my team at OneStream. He ensured we always knew the latest (and near future) AI offerings and worked with us to understand both the tech and the business drivers.
Jason served as our technical advisor during The Batchery and has remained a trusted resource for GuidePad ever since. He quickly understood our space, gave us thoughtful feedback on the product, and connected us with the right people and resources to help move the company forward.
Qylogix is led by a technologist whose career runs from systems administration and enterprise networking through global-scale data infrastructure and AI strategy. Prior roles include Director of AI Strategy at Microsoft, global AI strategy at Equinix, Big Data Solutions Architect at AWS, and enterprise engagements at Databricks.
He knows the founder side of the table too. A former Managing Partner and angel investor at The Batchery, a Berkeley-based startup accelerator that has supported more than 2,500 startups, he helped early-stage teams move from idea to funded company. He continues to serve as an advisor to multiple startups.
Jason is the author of Hands-On Data Science with the Command Line (Packt) and teaches Big Data and AI for Business at the University of Maryland's Robert H. Smith School of Business. He is a United States Air Force veteran.
The through-line: he has built and operated the systems he now advises on, at startup speed and at hyperscale.
A hired Series A CTO runs roughly $390,000 in first-year cash once you count base salary, benefits, and the executive search fee, plus 1–3% of your cap table, and the search itself takes 4 to 6 months. A Qylogix retainer runs $120,000 to $216,000 per year, flat, with 30 days' notice and no equity required.
Pre-seed through Series B. That's the window where startups need senior technical judgment a few days a week rather than a fifth executive salary. When you're genuinely ready for a full-time CTO, we say so, then help you run the search and hire well.
Both, deliberately. The core of the work is architecture, decisions, and technical leadership, but it stays hands-on: reference implementations, code and infrastructure review, and direct work on the systems that matter. You get an operator, not a slide deck.
Usually, and often dramatically. The levers are local-first architectures with selective cloud escalation, model right-sizing, and routing workloads to the cheapest compute capable of serving them. If your AI bill is growing faster than revenue, that conversation is worth thirty minutes.
With a 30-minute discovery call. From there you get a written proposal with scope, price, and start date. Fixed-scope work books with a 50% deposit; retainers run month to month after an initial 90-day term.
Boston, Massachusetts, working remotely with teams across US time zones. On-site time is available by arrangement for board meetings, diligence, and working sessions.
Thirty minutes, no charge. Bring where you are, what you're building, and where the architecture hurts. Prefer async? The form works, and serious inquiries with specs get a same-week response.