Amicus Data builds the machinery that changes that: ingestion, warehousing, orchestration, and AI classification on the way in — dashboards and data products on the way out. Custom software at every layer, from raw API to final pixel, built for professional services.
Raw data — messy, scattered, high-volume, arriving from dozens of sources — gets turned into something a business can actually use. Not a spreadsheet. Not a static report. A living system that updates itself, catches what matters, and presents it in a way that makes the next decision obvious.
Every layer built in-house. No outsourcing, no drag-and-drop tools, no templates.
The cloud platforms — Google Cloud, BigQuery — provide effectively unlimited compute and storage. Everything on top of them is custom software: the extraction logic, the transformation rules, the orchestration, the APIs, the dashboards. Written from scratch, because the edge cases matter and generic tools don’t know about anyone’s edge cases.
APIs have rate limits, websites have anti-scraping measures, databases have schemas designed in 2009. Getting data out cleanly and reliably is its own discipline.
Where the real work happens. The rules that turn a messy API response into a structured, queryable record that means something specific — written by hand.
Incremental, deduplicated, properly partitioned. At 660 million records a month, the loading strategy matters as much as the transformation logic.
Pipelines aware of each other — shared state, dependency management, downstream triggers. The orchestration layer that makes dozens of pipelines behave as one system.
At this volume, “it ran” and “it’s correct” are different claims. Every stage emits counts, checksums, and quality scores that are checked before downstream work proceeds. A pipeline that can fail silently will, eventually — so none of these can.
“The data problems, the engineering puzzles, the moment a dashboard makes something visible that wasn’t visible before — that never gets old.”
The flagship platform: competitive intelligence for a professional-services vertical, covering roughly 2,200 firms in one regional market.
Every firm’s search rankings — tracked from a geographic grid of points, because rankings differ block by block. Every firm’s advertising, reviews, business listings, backlink profile, and site performance. Collected on a rolling 72-hour cycle, classified on ingestion, and layered into a warehouse where raw history is preserved permanently and reporting tables serve dashboards directly.
The result is a market where nothing moves unexplained. When a firm gains position, the data shows what changed — the reviews, the pages, the links, the spend — and when a client asks “why them and not us,” the answer is a query, not a theory.
Engagements are defined pieces of working software — a pipeline, a warehouse, a dashboard, a platform — with written specifications and verifiable completion criteria. Payment is for delivered systems, not attended meetings.
Everything is built inside the client’s own cloud accounts, in the client’s name: the code, the data, the infrastructure. Combined with documentation written to be read by the next engineer, this answers the reasonable question about hiring a one-person firm — continuity is designed in, not promised.
Professional-services data is sensitive by definition. Client data stays in client infrastructure; nothing is pooled, resold, or used to train anything.
A short paid diagnostic: what data exists, what condition it’s in, what it would take to make it decision-grade, and whether the project is worth doing at all. Sometimes the honest answer is no, and that answer is cheap.
Data engineer, analyst, and developer. Amicus Data came out of a problem that kept coming up: interesting data trapped in systems that couldn’t do anything useful with it. Raw APIs returning millions of records with no structure. Organizations sitting on gold mines of information with no way to see them.
Amicus Data is a one-person operation, and that’s intentional. Every pipeline, every dashboard, every line of code comes from the same person who understood the problem. No handoff. No interpretation layer. No game of telephone.
The focus is professional services — legal, accounting, medical — because those are industries where the data is rich, the competition is real, and the existing tools consistently fall short of what’s actually needed.
Three things make a first conversation useful: where the data lives, roughly how much of it there is, and the decision it should be informing. From that, I can usually tell you within a day whether there’s a real project here — and if there isn’t, I’ll say so before you’ve spent anything.
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