Mid-market ops · DFW Metro
I wire AI into your document, coordination, and billing workflows so you can free your back-office team from full-time data entry and put them to work that grows the business.
Document intake labor down from two full-time roles
Days-to-pay on automated invoice follow-up
Accuracy on transport documents
Value domains
Most businesses start with one of these. The process scan shows which one to invest in first.
Rate confirmations, bills of lading, orders, portal uploads – common in trucking and wholesale. A clerk or coordinator pulls data from email, images, and PDFs and re-enters it into the operations system. Re-entry consumes most of the week.
LTL carrier proof →Dispatch, customer updates, delivery confirmations – everyday work in field service and logistics. A dispatcher or CS lead types the same update into the inbox, the schedule, and ERP. That relay is why the team stays behind and why a single system of record is impossible.
Rental operator proof →Invoice prep, AR chase, payment status – typical at mechanical, HVAC, and trade contractors. An office manager or AR person builds the invoice, follows up on payment, and logs every touch by hand. Manual work stretches days-to-pay and loads a coordinator seat year-round.
Contractor proof →Method
Most teams today improve individual productivity with chatbots and helpers embedded in email and office tools. The underlying process still assumes people move data between systems at every step; the work itself barely changes.
Spend on AI often outpaces process readiness: tools are scattered with no clear map of which system owns each step of an order, invoice, or status update.
Your AI operating system starts with the path of work, control points, and decision rights, then redesigns the process so machines handle rule-bound steps and people make judgments. Only after that design is clear do we wire automation into your systems of record.
Follow one item end to end. Score payroll hours, cycle time, rework, and cash impact. Remove steps that exist only for re-entry, status chasing, or waiting, so the remaining work stops depending on someone retyping, chasing, or waiting.
For every remaining step, name one system of record and one clean output the next step can read. Remove people as the glue between tools.
Separate rule-based steps from judgment steps. Automate the first; put people on the second. Route failures and exceptions into owned queues so work does not disappear or stall in a private inbox.
Outcomes
Chatbots help with tasks. Integration removes them.
Score each step on labor and error cost before building. Automate what makes sense after people stop being the link between systems.
See proof →No parallel inbox or shadow spreadsheet. Process runs inside existing tools – if those tools remain after AI integration.
See the offer →30 consecutive business days of live data before you decide to expand.
Process scan →Engage
Step 1 · Process review
A working session with you and your operations lead – on site or remote.
Step 2 · Process build
Fixed-scope build of one job* inside your tools – path redesign, systems integration, standard work, and ownership transfer after it's proven.
* When that job ties up a loaded coordinator seat in payroll ($55K–$80K per year), the install usually beats another hire on year-one math alone.
Case studies
Document intake
I redesigned and built document intake into the carrier's existing transport systems – rate confirmations, bills of lading, and related paperwork. Manual entry dropped from two full-time roles to one-third of one person, at 98% accuracy on documents in production use.
Billing & follow-up
I redesigned and built invoice follow-up into the contractor's accounting tools and email – open invoices that had averaged 52 days. One coordinator spent about 12 hours a week chasing payments between spreadsheets, accounting, and inbox threads. Follow-ups now draft, route for approval, and log each touch; days-to-pay dropped to 38.
Coordination & status
I redesigned and built status updates and delivery confirmations into the operator's phone, inbox, and scheduling tools – drop-offs, returns, and customer updates that had run through manual calls and shared threads. Incoming status calls dropped about 40% with the same coordinator headcount.
Outbound & bids
I redesigned and built AI-assisted outbound to local property managers for a painting contractor – bids had run almost entirely on word of mouth. Bids per week tripled and win rate rose with the same estimator headcount.
Channel launch
I designed and built a direct-to-consumer channel for a decades-old processor – social content and online sales on top of existing wholesale accounts. Same headcount; new revenue line without a marketing hire.
Research platforms
I designed and built AI data platforms the firm relied on for client work at scale – analysis time cut 70%. The platforms supported more than four billion dollars in client decisions across 20+ markets.
years building AI systems for business
typical operator scale
Founder
I started building with AI before it was a product category. In 2017 I designed a proprietary machine-learning research platform for a global strategy consultancy. Then, I co-founded a startup around market intelligence (later acquired). When I joined the private beta of GPT-3 in 2021, I had already spent years building algorithmic and machine learning-based automation into business workflows.
I am happy to work with lean, capable teams that want meaningful gains – starting with one workflow.
References
"We were about to add another office person just to keep up with paperwork and follow-ups. Vlad built with our coordinator until the process ran every business day without us chasing it. Days-to-pay moved – that was the proof we needed before talking about what to fix next."— Owner, DFW mechanical services business
"Vlad built the research platforms our firm relied on for client work at scale – systems that supported billions in decisions across dozens of markets. Over many years, he delivered what he promised, under pressure, every time."— Dominic Pride, CEO, Upstart Breakthrough Strategy
FAQ
Questions owners ask before a process scan.
A paid diagnostic immersion – see Step 1 under Engage for activities. You get a stage-gate package (benefits case, risks, proceed / re-scope / hold) before any install commitment.
Copilot helps individuals. A production AI install wires one recurring process into your live systems so the same volume takes less back-office time and you can see the change on the same jobs your people already run.
You – owner or operations leader – plus the person who will run the workflow daily.
Template packages turn on a feature. Here I build one job inside your existing tools with your operations lead and stay until your team runs it every business day. Fixed scope at each step.
The goal is to remove the repetitive work so your people handle bids, customers, and exceptions that grow revenue.
Pick a time below for a 20-minute fit call, or call (469) 283-8448.