non-linear dynamics llc · ALEX SPAULDING

You're running your business
on data you can't see.

Clarity across complexity.

The data is there. The systems are running. But when you actually need to know something — really know it — you end up in a spreadsheet at 10pm making your best guess.

I co-ran a manufacturing business as a second-generation owner. I know what that feels like. And I know what changed when someone finally showed me what my own data was actually saying — not just cleaner reports, but a completely different understanding of what the real problem was.

That's what I do for operators now.

LET'S TALK SEE THE WORK
131,100+ rows processed
5 phases delivered
3 automated tools built
Manufacturing · Healthcare · Professional services
THE PROBLEM

Does any of this sound familiar?

Different words, same situation. I've heard some version of each of these in almost every first conversation.

01
"We have all this data but can't do anything with it."
It's locked somewhere. A system no one can query, a format no one understands, a report that stopped being trusted months ago.
02
"Our systems don't talk to each other."
Revenue here, operations there, finance somewhere else. Someone is manually copying numbers between screens every week. Probably you.
03
"I don't trust the numbers I'm looking at."
The dashboard exists. But somewhere along the way the numbers stopped matching reality, and now nobody looks at it. That's not a data problem — that's a confidence problem.
04
"I don't have visibility into what's actually happening."
You find out about problems after they've already cost you something. You're always looking at last month. You're running on instinct when you should be running on information.
HOW I WORK

Three phases. No surprises.

You see everything before I move forward. Fixed fees — if it takes longer than I estimated, that's on me, not you. I work with a small number of clients at a time so the work actually gets my attention.

01

Get the data out

Whatever system is trapping it — legacy ERP, practice management software, a patchwork of spreadsheets — we get the data out, clean it, and make sure the foundation is actually solid before we build anything on top of it.

02

Make it visible

Dashboards, tools, pipelines — whatever the business actually needs to see what's happening in real time. I start with prototypes so you can react before I build the full thing. You tell me what's right, we lock it in.

03

Make it run itself

The manual work goes away. The data flows without anyone copying numbers between screens. You get a written SOP so you can keep the system running without calling me every time something changes.

WHO I WORK WITH

Owner-operated businesses that have outgrown what they can see.

Too much going on for a freelancer to handle. Not the right size for a Big 4 engagement. The kind of business where one person who can go from the technical layer all the way to the strategic conversation is exactly what's needed.

Operators sitting on legacy systems Software that runs the business fine — but was never built to tell you anything useful about it.
Businesses in the middle of a migration Moving platforms and realizing the data has to move too — cleaned, restructured, and validated before it lands somewhere new.
Owners who've outgrown their spreadsheets What worked at $2M becomes a liability at $8M. The data's there. It just needs infrastructure that keeps up with the business.
Leaders who know something is off but can't see what You know your business well enough to know the numbers aren't telling the full story. That instinct is usually right. Let's find out what it's pointing at.
WORK

Three engagements. Same problem. Different stakes.

Every engagement is different in its technical specifics. The through-line is always the same: operational data that exists but can't be read clearly — and someone who needs to understand it before they can act.

ENGAGEMENT · MANUFACTURING
Industrial Manufacturer
ERP data migration — legacy system to modern platform
131K+
ROWS PROCESSED
48,487
ERRORS RESOLVED
596
PART TRANSLATIONS
5
PHASES DELIVERED
A mid-market industrial manufacturer mid-migration from a legacy ERP to a modern platform. The data existed — years of bills of materials, operations files, part numbers, engineering revisions — but it was structured in a format the new system couldn't read, riddled with errors no one had catalogued, and too large to fix by hand. The migration had stalled.
199 files analyzed across 1,058 data tabs. 48,487 errors identified, categorized into 24 specific types, each with a documented root cause and an automated fix. A complete BOM conversion system built from scratch — restructuring the legacy dot-notation format into the flat architecture the new platform required. A crosswalk system applying 596 part number translations automatically across every file type, with ambiguity logic flagging the four cases that needed a human decision. A dynamic revision lookup against 4,172 engineering drawings with three different fallback rules depending on file type. Five phases of work. Ten delivered output files. Three reusable tools that future batches can run with a single configuration change.
131,100+ rows processed and validated. A migration that had stalled was unblocked. The engineering team had clean, correctly formatted files they could hand directly to the implementation team — and a pipeline that handles the next batch in minutes, not weeks. The tools aren't disposable. They're built to keep running.
ENGAGEMENT · HEALTHCARE / AESTHETICS
Medical Aesthetics Practice
Data infrastructure & dashboard build
5 yrs
DATA ACTIVATED
8 wks
TO FULL BUILD
Ongoing
ON RETAINER POST-PROJECT
Five years of revenue, provider, and marketing data sitting in a practice management system that doesn't connect to anything. The owner had stopped entering data because she couldn't visualize it and it felt pointless.
A complete data infrastructure: margin calculator repair, budget tool consolidation, automated export pipeline, and a full owner dashboard — provider KPIs, ROAS tracker, trend alerts, and a Monday morning summary view. Plus a written SOP for ongoing maintenance.
Weekly data entry from an hour to 15 minutes. Real margin data on every service for the first time. A dashboard that makes the state of the business visible in under 60 seconds — every Monday morning.
ENGAGEMENT · SPECIALTY RETAIL · M&A DUE DILIGENCE
Specialty Retail Acquisition
Operational data analysis — buyer-ready financial translation
9
DATA FILES ANALYZED
3 yrs
FINANCIALS RECONSTRUCTED
$40K
LIABILITY SURFACED
4
BUYER-READY DELIVERABLES
A specialty retail business under acquisition consideration. A decade of operations, real inventory, loyal customers, and a cloud-based POS system that had recorded every transaction. The data was all there. The problem was that nobody could read it — not in a way that told the real story of the business to someone about to buy it. An M&A advisory firm brought me in to get into the system, figure out what it actually said, and translate it into something a buyer could understand and act on before signing anything.
The advisory team went into buyer conversations with a complete, accurate picture of the business — not just what the POS system showed on the surface, but what it actually meant. A $40,000 liability was surfaced and adjusted before closing. A data export error that would have inflated the entire analysis was caught before any conclusions were drawn. And a restocking opportunity worth roughly $27,000 in gross profit was framed as the clearest value creation move available on day one after closing — turning a problem into an opening.
Nine data files analyzed. Three full years of revenue, inventory, and transaction data reconstructed. The first thing I caught: a platform behavior where certain reports export all-time historical data regardless of the date range selected — without knowing that, the analysis would have shown revenue nearly three times the actual period. I identified which reports were reliable, rebuilt everything around the correct sources before a single conclusion was drawn.

From clean data: 1,400+ active products, $31,800 in inventory at cost, 413 products at zero stock representing nearly $50,000 in retail revenue the business couldn't capture. Pricing errors. Items sold below cost. A revenue decline of 19% over three years — but the reason mattered. In year two, the same number of customers came in and spent slightly less. In year three, transaction count dropped by over 1,000 while spend-per-visit held steady. Those are two entirely different problems with different causes and different implications for a buyer. Getting that distinction right changed the frame of the deal.

The gift cards: several hundred unredeemed, recorded in the system as a neutral line item. They're not neutral — every one represents money already collected that a buyer will be obligated to honor in future goods. Total face value just under $40,000. A liability, not an asset, requiring a direct adjustment to the deal structure. Four buyer-ready deliverables produced: an 11-slide presentation deck, a six-page plain-language executive summary, an interactive data dashboard, and a 37-term glossary with real examples from this business's own data attached to every definition.
ABOUT

I've been on your side of the table.

The reason I do this work isn't that I'm good at data. It's that I know what it changed for me — and I've spent the last several years trying to create that same moment for other operators.

THE ORIGIN
Litelab is a family business. I co-ran it alongside my father, my brother, and a minority partner — which meant that for all the years I was there, clear leadership was always the thing we were working around rather than the thing we had. We weren't a poorly run business. We had real customers, real product, real history. But we also had a factory floor with waste nobody was measuring, margins that felt fine until you looked closely, and a ton of operational data that nobody had ever actually sat down with.

When I brought in a business analyst, I wasn't expecting what happened. He didn't hand me a report. He started walking me through the numbers — one leak at a time. Margins on products we thought were profitable. Waste patterns on the floor that had been invisible because nobody had connected the data points. Revenue that looked solid until you understood where it was actually coming from and where it was quietly going.

I tell people he radicalized me. That's not an exaggeration. What shifted wasn't just what I could see — it was how I thought about running a business entirely. I stopped guessing. I stopped assuming. The answers were all there in the data. What I needed to do was learn to read what it was telling me, follow each thread all the way down, and ask why — five times if necessary — until I got to something real.

That's the methodology I bring to every engagement. And that moment at Litelab is the reason I built non-linear dynamics.
OPERATOR BACKGROUND
Second-generation owner — high-end lighting manufacturing
Full P&L, operations, strategy. Real money, real people, real consequences. That perspective doesn't come from a dataset.
RESEARCH BACKGROUND
PhD — ineffable experience & practitioner knowing
How people sense something is true before they can prove it — and what it takes to surface that into something others can act on.
TECHNICAL DEPTH
Data engineering, automation & systems integration
Custom tooling, automated pipelines, ERP migration, dashboard architecture, API integration. From raw legacy data to something you can use Monday morning.
HOW I THINK
Pattern recognition across complex systems
I read the relationships between data points, not just the numbers. I follow threads. I don't stop at the first answer.
HOW I WORK
Small number of engagements. Full attention.
I don't run a team. Fixed fees — if I underestimated the scope, that's on me.

Most operators I work with are running on instinct — not because they're not smart, but because nobody has ever sat down with their data and shown them what it's actually saying. The answers are almost always already there. The work is learning to read them. My PhD research focused on how practitioners create and understand ineffable experiences — the kind of knowing that lives below language. How people sense something is true before they can prove it. That research is directly applicable to every engagement where a client says "something is off" but can't say what. They're usually right. The data almost always confirms it. I think in systems and I see in patterns. When I get into a business's data I'm not just looking at numbers — I'm reading the relationships between them. Where a pattern breaks unexpectedly. Where the data points at something nobody has named yet. The 5 Whys isn't a technique I apply — it's closer to how I naturally move through a problem. Operator background, technical depth, pattern recognition, a research foundation in how people understand complex systems. I'm not bringing a framework. I'm bringing a way of seeing.

GET IN TOUCH

If any of this sounds like your business, let's talk.

The first conversation is just that — a conversation. I want to understand your situation before either of us decides if it's a fit. No pitch, no pressure. If it's not the right match, I'll tell you that too.

EMAIL alex@nonlinear-dynamics.com
BASED IN Buffalo, New York
RESPONSE TIME Within one business day

I read every message and respond within one business day.