Turn AI from a buzzword into a system that ships
Whether you’re still asking “what could AI even do for us?” or pushing a stalled pilot over the line, Smartisan embeds engineers directly inside your team to carry AI from idea to production — the architecture, the infrastructure, and the adoption work that pilots usually skip.
Led by Dr. Yan Huang — federated learning researcher, 30+ publications, NSF & NSA principal investigator.
Not sure where AI fits? Start here.
You don’t need an AI strategy to get value — you need one workflow that wastes hours every week. These are the places small and mid-sized teams usually see returns first, using the systems you already have.
Answer routine questions automatically
An assistant trained on your products and policies drafts replies and resolves the repetitive 80% — your team keeps the exceptions.
Turn paperwork into data
Invoices, orders, and contracts read and entered into your systems automatically — instead of hours of retyping and reconciliation.
Draft, personalize, follow up
Proposals, outreach, and product copy generated from your own materials, in your voice, in a fraction of the time.
Ask your own documents
A private assistant over manuals, policies, and past projects — so answers stop living in one veteran employee’s head.
Forecast demand and staffing
Your sales history turned into forward-looking signals for inventory, scheduling, and purchasing decisions.
Catch defects before they ship
Vision models watch the line and flag anomalies early — less rework, fewer returns.
Recognize one of these? Tell us what eats your team’s time → A short conversation is enough to scope whether it’s worth automating.
Building a model is the easy part.
Pilots are built to prove an idea works once. Production has to work every day, on real data, in front of people whose jobs depend on trusting it. That gap is where most AI initiatives quietly stop.
Data plumbing
Pilots run on cleaned, hand-picked samples. Production runs on your actual systems — inconsistent schemas, latency limits, and access controls no one accounted for in the demo.
Infrastructure
A notebook that runs on a laptop is not a service. Serving, monitoring, retraining, and rollback all have to exist before anyone can rely on it.
Adoption
The best model is worthless if the team that has to use it doesn’t trust or understand it. Deployment is as much a people problem as an engineering one.
Where we work
Engagements are scoped to the stage you’re actually at — from a first honest assessment to an engineer sitting inside your sprint cycle until the system ships.
AI Readiness & Architecture Review
An honest technical audit of what you have, what’s missing, and what it will actually take to reach production — before you commit budget to it.
Embedded Deployment Engineering
We sit inside your team, in your codebase and your sprint cycle, and do the unglamorous work of turning a prototype into a system people rely on.
Federated & Privacy-Preserving ML
For organizations that can’t centralize their data — multi-site, multi-tenant, or regulated environments — built on research-grade federated learning architecture.
MLOps & Production Hardening
Monitoring, retraining pipelines, rollback plans, and the operational guardrails that turn a demo into infrastructure.
Technical Advisory & Team Enablement
We leave your team more capable than we found it — documentation, training, and architecture decisions your engineers can maintain without us.
Employee AI Training
Hands-on workshops for your whole staff, not just engineers — how to use AI tools effectively, when to trust them, and how to build reliable AI workflows into everyday work.
Four stages, one accountable engineer.
No handoffs between a sales team and a delivery team. The person who scopes the engagement is the person who ships it.
Discovery
A 1–2 week technical assessment of your current AI initiative, data, and infrastructure — with a direct answer on whether it’s ready to scale.
Embed
Our engineer joins your team, on-site or remote, working inside your existing tools, repos, and process — not alongside them.
Build
We ship in increments. Progress is a working system in production, reviewed by your team — not a slide deck.
Handoff
Documentation, training, and a maintenance plan, so the system outlives the engagement.
Research-grade rigor, applied to production systems.
Smartisan is led by Dr. Yan Huang, Associate Professor of Software Engineering at Kennesaw State University, where his research on personalized federated learning — architectures and aggregation algorithms for training across distributed, non-uniform data — has been cited more than 1,100 times in the past two years alone.
He has published 30+ papers in venues including Neural Networks and IEEE/ACM transactions, served as Principal Investigator on NSF- and NSA-funded programs. That same discipline — building systems that hold up outside a controlled environment — is what Smartisan brings to every engagement.
Fewer clients, worked closely: every engagement is led personally, not routed through a bench of junior consultants.
peer-reviewed venues
learning, 2 yrs
Investigator
Tell us where your AI project is stuck.
Whether you’re weighing your first AI initiative or you have a pilot that’s stalled before production, a short message is enough to start.