Offerings

Solution Planning

Every autonomous inspection program starts with the same problem: moving from ad-hoc drone flights to something repeatable. I work with organizations to assess current operations, asset inventory, inspection cadence, regulatory posture (BVLOS/FAA waivers), and existing hardware or software investments, and build a roadmap that fits the organization's actual constraints, not a generic best-practice template.

This includes:

• Evaluating whether a drone-in-a-box (DIB) program makes sense for your asset base and geography, and if so, what deployment model fits (fixed dock vs. truck-mounted, coverage radius, site selection)

• Mapping regulatory requirements and waiver strategy so operations don't stall on compliance

• Defining data requirements upfront (what the inspection needs to produce, and for whom) so the hardware/software stack is chosen to serve the outcome, not the other way around

• Building the business case and stakeholder alignment needed to move a pilot into a funded, scalable program

Implementation

This is where the plan becomes a working system. I lead the hands-on deployment of autonomous inspection infrastructure docks, remote piloting workflows, and the software integrations that tie it together.

Concretely, this covers:

• Deploying and configuring dock-based systems (e.g., DJI Dock 3) for utility and energy infrastructure inspection, including field-expedient solutions for power, connectivity, and site logistics

• Standing up remote/DRC (Direct Remote Control) operations so a small team can safely and reliably operate multiple docks without being on-site

• Integrating platforms like FlightHub 2 / Cloud API into existing operational tooling, so flight data, alerts, and command workflows fit into how your team actually works.

• Building the data pipeline from capture to usable output, standardizing formats, automating ingestion, and reducing the manual, one-off handling that keeps programs from scaling

Solution Delivery

Once a program is running, the work shifts to scaling it without scaling headcount and ensuring the data being collected drives decisions instead of sitting unused.

This includes:

• Designing the operational model for scaling remote dock operations so that one pilot/operator can responsibly manage many docks across a service territory

• Advising on the shift from reactive to proactive asset maintenance, using inspection data and ML-driven analysis to prioritize where attention (and capital) actually needs to go

• Identifying where machine learning and automation can remove human dependencies in repeatable workflows: anomaly detection, defect flagging, reporting without losing accountability

• Ongoing program strategy: benchmarking against industry practice, adjusting as regulations or platforms evolve, and positioning the program for the next phase of scale