Charleston, SC · Automation Consulting · Scientific ML and Analysis
Most of my work is workflow automation, internal tools, integrations, and reporting systems for operations-heavy teams. I also take on machine learning, scientific computing, and analysis-heavy software where my research background is a real fit.
Most engagements start with a workflow audit, a scoped build sprint, or a focused discussion around one technical bottleneck.
I run Blaine Heffron Consulting. I work with businesses and technical teams that know something is clunky, manual, or slower than it should be, but do not want a bloated consulting process just to hear obvious things read back to them.
I earned a PhD in physics from the University of Tennessee, where I worked on the PROSPECT neutrino experiment at Oak Ridge. A lot of that work was machine learning, detector analysis, simulation, and extracting useful answers from noisy systems and imperfect instrumentation.
That background now shows up in two kinds of engagements. Sometimes I help a business clean up a workflow, connect systems, or build an internal tool. Sometimes I work on ML, scientific computing, or technical analysis problems where the software has to match a real research or engineering context.
Right now the clearest example is StockForge, an inventory and operations platform being developed around a chemical-industry use case. Other work includes Songcraft, client and open-source tooling, and custom technical software across product and research contexts.
I am usually most useful to founder-led companies, operations-heavy teams, and research or engineering groups that need someone comfortable with both implementation and technical depth.
I take on two kinds of work: automation and internal systems for operations-heavy teams, and ML or analysis-heavy software for technical environments.
I usually start small. One call, one audit, or one build sprint. That keeps things concrete and makes it obvious pretty quickly whether I can help.
Both lanes are structured so a team can start small, solve one real bottleneck, and expand only if the first step proves useful.
For founder-led companies and lean teams that need one operational problem taken off their plate without turning it into a giant project.
I look at how work actually moves through your business, where people are getting stuck, and what is worth fixing now. You leave with a concrete plan and the clearest place to start.
$750 to $1,500
A good starting point if the work feels clunky or too manual, but you do not yet know what should change.
A short build engagement for one painful workflow. Intake, reporting, onboarding, internal tools, CRM cleanup, document handling, or whatever keeps eating time every week.
$2,000 to $5,000
For teams that already know the bottleneck and want a bounded fix with a clean handoff.
For research and engineering groups that need machine learning, scientific computing, analysis tooling, or custom software in environments where correctness and technical context matter.
Focused consulting for analysis strategy, system design, technical review, or debugging a messy pipeline before a larger build starts.
Custom quote or premium hourly
Best when the work still needs diagnosis, design review, or a senior technical read before anyone should start building.
For harder problems: ML pipelines, detector or signal analysis tooling, research software, technical dashboards, or bespoke systems where generic tools run out of road.
Quoted per scope
A fit when the work needs real software, careful analysis, and somebody who can move between code, data, and domain constraints.
If the problem is operational drag, reporting, approvals, inventory, or internal admin work, start with the automation lane. If it is closer to ML, analysis tooling, simulation, or research software, start with the technical lane.
A few examples of what I have actually built across both lanes. The point is not breadth for its own sake. It is to show that I can understand a messy system, find the leverage point, and ship the right thing.
Inventory and operations software developed around a chemical-industry use case. The platform brings BOM management, barcode scanning, reordering logic, and supply chain workflow into one system. It is currently in testing ahead of a broader market push.
A music reading game where kids play along with sheet music in real time. Teachers can run classroom sessions, parents can track progress. Built on MusicXML with live note matching via WebSockets.
Rust proc macros and test utilities for Soroban contracts: typed storage handles, flattened contract errors, stronger contract imports, and auth helpers for tests. The project was funded through an SCF #39 Developer Tooling grant.
MCP server for searching and indexing scientific papers across multiple sources with local hybrid search. A good example of building operator-facing tooling around search, indexing, and automation-heavy workflows.
MCP server for symbolic mathematics and tensor algebra. More niche than the operations work, but it shows the same pattern: build purpose-fit tools where generic software does not go deep enough.
CNN and GCN-based event reconstruction for the PROSPECT neutrino detector. Achieved a 3.3% improvement in usable signal statistics for the short-baseline oscillation search.
Contracting work on the Stellar stack: contributions to stellar-cli and stellar-js-sdk, developer experience work on Scaffold Stellar, and smart contract development for EquitX.
If you have a workflow that is wasting time, a software problem that needs a senior hand, or a technical project that keeps getting deferred, send me a note. A short description is enough to start.
The easiest way to begin is to send three things: what is broken, what tools are involved, and whether this is a small-business workflow problem or a more technical R&D-style project. If it looks like a fit, I will suggest the smallest useful starting point.
If you want a faster response, include your rough timeline and budget range. That saves both of us a round trip.
Academic work in neutrino physics, detector analysis, and machine learning. This is the background behind the technical analysis and scientific ML side of my work.