Alexey Yushkin — AI, Automation & Integration
The inefficiency is rarely where we think it is.
You can feel it even when you can’t name it — the manual steps, the broken handoffs, the systems that don’t talk. I find where the time and money actually leak, then build the automation or AI that stops it. Diagnosis first, tech last.
Fast · Qualified · No friction
The honest version
The tool is the last decision.
Most projects start with “we need AI” or “we need to automate this.” Both skip the real question: what’s actually slowing you down? It’s almost always the process — a handoff nobody owns, multiple systems that don’t agree, a step that was never defined. Fix that layer first and the right tool is obvious. Sometimes it’s AI. More often it’s plain automation. Occasionally it’s just deleting the step.
Receipts
The work, in numbers.
Specifics beat adjectives. A few of the outcomes the work has actually produced — then the track record underneath them.
The method
Four ideas the whole practice runs on.
Process legibility before automation
You can't automate a process no one can see. I make the workflow legible first — every handoff, owner, and exception — before a line of code. Half of what gets called an “AI problem” disappears at this step.
Integration & data contracts
Agents act on the ~20% that’s structured. The other 80% — contracts, email threads, policy docs — is where the work actually lives. An agent acting on 20% of the picture isn’t automation; it’s a faster way to be wrong. I build the seams and data contracts that close the gap.
AI governance & safe containment
Shadow AI isn’t a policy gap — it’s an inventory gap. Scoped access, kill switches, and an MCP layer that gives agents governed access to enterprise data. Deploy without it and you haven’t deployed an agent; you’ve deployed an incident with a delay.
ROI = removing a step
ROI shows up when AI removes a step — not when it removes a name from payroll. If the system doesn’t delete work, it’s theater. Every engagement is measured against one question: did it change a decision, or just a dashboard?
What I do
A method, not a menu.
Three moves, in order. A flat list of services reads like a freelancer; a sequence reads like an operator who knows where the failure is.
Find & fix the process
- Process discovery & optimization
- Advisory — "do you actually need AI?"
- Locating the broken layer before any code
Build the system
- Custom AI agents
- AI process automation
- Deterministic process automation
- Optimization engines (operations research)
- Mobile forms & field-ops development
Integrate & prove
- Software & platform integration
- Data contracts & governed AI access (MCP)
- Validation & measurement — a decision, not a dashboard
Featured work
A utility work scheduler no product could build.
A custom optimization engine resolving more than a dozen interacting constraints at once — geospatial separation, per-block permit status across every address, crew certifications, travel, equipment, and time windows. Built with lightweight Python libraries for serverless deployment. This is what “go beyond off-the-shelf” actually looks like — operations-research-grade scheduling, not an API wired to a calendar.
Read the breakdown →Where this lands
Built across asset-heavy, process-heavy industries.
- Energy & HVAC
- Logistics & freight
- Construction
- Manufacturing & warehouse
- Healthcare admin
- Legal
- Banking & finance
- Real estate / property mgmt
- Education
- Professional services
- SMB operations
Articles
The problems I write about are the ones I solve.
The AI opportunity in healthcare isn't in the chart. It's in prior authorization.
Prior authorization automation is a process problem, not an AI one. The bottleneck is the admin layer — fax queues, payer PDFs, manual handoffs — not the clinical model.
4 min read → Logistics & freightWhen a carrier exposes its automation stack as an API, the failure mode moves
Amazon-grade logistics automation is becoming reachable by API. The catch: an automation-first network won't tolerate messy data. Automating shipment status updates starts with the prep layer nobody wants to own.
4 min read → Enterprise AIMulti-agent orchestration won't fix a process that's already broken
Orchestration assumes the handoff underneath is clean. Usually it isn't. Why most multi-agent AI projects fail at the seam, and what to fix before you add a second agent.
4 min read →Interactive proof
Don’t take my word for it. Talk to the systems.
25 production-style AI assistants you can test right now — a sample of the 80+ I’ve built. Across legal, banking, manufacturing, education, staffing, and energy, each is a working retrieval + conversation system tuned to a real business’s content, with voice variants where the channel mattered.
Open the AI assistant lab →- Tech & services
- Banking & finance
- Manufacturing & industrial
- Energy, HVAC & home services
- Education
- Staffing & talent
- Legal
- Forms & field ops
- Non-profit
- + voice variants
About
I’m the person who tells you when you don’t need AI — then builds what you actually do.
Avionics engineer by training. Seventeen years running a $1M+ company I built from zero. Then an MBA, the analyst seat turning process into hard numbers, and the AI architecture work — RAG systems, integrations, a custom MCP server. I’ve seen the gap between AI promises and operational reality from every seat: founder, operator, analyst, engineer.
More about how I work →Tell me what’s broken. Or let me find it.
Know your bottleneck or just feel it — either way, I’ll find the fix. If you don’t need AI, I’ll tell you that too.
Open to contract AI-automation engagements across the US — on-site, hybrid, or remote.