Our Portfolio
Practical automation, shipped. Here's a look at how we put it to work for real businesses.
Case Study · From our founder's work
FeaturedMigrating 5,689 Employees from Dayforce to UKG Pro for $3.58
A serverless HRIS migration pipeline — Dayforce HCM API to BigQuery to UKG Pro — built solo in 38 days against a cutoff someone else set.
5,689
Employees migrated
4,158 US · 1,531 Canada
38
Days, code to cutoff
fixed date, set externally
$3.58
Total cloud spend
entire project
191
Columns mapped
two tax regimes
The Challenge
Two HR systems describing the same 5,689 people in incompatible vocabularies — 191 columns across two national tax regimes — against a hard, non-negotiable cutoff. The conventional approach hand-builds the load files in spreadsheets, then hand-maintains them as the business keeps hiring. That dual maintenance window is where migrations go wrong.
What He Built
A serverless pipeline that treats the migration as a compiler rather than a transfer: extract everything once, interpret it in SQL, regenerate load-ready files on command. Post-cutoff changes routed to an append-only log, so the exports were current at the moment of ingestion instead of reconciled by hand.
Built by founder Hunter Strange in his HRIS role at a 5,689-employee aviation services company — not a StrAinge Business Solutions client engagement. We publish it because it's the clearest demonstration we have of how we approach a problem.
Read the full case studyCase Study · From our founder's work
An Hour of Daily Copy-Paste, Rebuilt as Six Minutes of Oversight
A six-day-a-week, three-system employee-lifecycle process rebuilt as a human-in-the-loop automation — and the same pattern that powers AP, AR, and HR automations at any size.
~6 min
Per run, reviewed
was ~60 min expert
96%
Of the time cut
vs. the peer baseline
$20K
Build quote replaced
built in-house instead
$120K
Role later absorbed
the capacity it freed
The Challenge
A daily onboarding, transfer, and offboarding process spanning three systems that didn't talk to each other, on a same-day compliance clock — fast only because one expert with formula sheets and mouse macros ran it. Fragile, unshareable, and living in a single operator's head.
What He Built
A Google Apps Script automation with a two-phase stage → review → execute design: it proposes every account change on a sheet a human reads, then executes only after approval — with a per-row record of what happened. The labor is automated; the judgment stays human.
Built by founder Hunter Strange in an operations role at a national aviation-services company — not a StrAinge Business Solutions client engagement. His first end-to-end automated rebuild of a live process, and the clearest demonstration of how we approach automation.
Read the full case studyCase Study · Our own platform
What We Won't Let Our Own Software Do
The design record for Palanae, the business platform we build and run ourselves — where the interesting decisions aren't the features, but the things it's structurally incapable of doing.
0
Writes the AI can make
propose-only, lint-enforced
6
Business nouns, fixed
everything else is a Fact
29/29
Tables with row-level security
isolation tested, not assumed
2,067
Automated tests, passing
across 213 files
The Challenge
Business systems rarely fail on features. They fail on trust — either people stop feeding them, or an AI fills the gaps and is confidently wrong often enough that everyone goes back to the spreadsheet. Same empty system, two different causes.
What We Built
A platform where the AI is structurally incapable of writing to the database — its only output is a proposal a human accepts or rejects, and a lint rule makes the alternative impossible to write. Every inferred value carries its source, and sits beside the human value rather than overwriting it.
Palanae is StrAinge AI's own product, not a client engagement — every figure here measures our codebase, not a result delivered to a customer. We publish it because how a firm builds when nobody is watching is a better signal than a testimonial.
Read the full case studyCase Study · Our own operation
New · September 2026What One Owner and AI Tools Accomplished in About 45 Hours
Software delivery, client work and marketing during September 5–12, 2026, with AI tools building, reviewing and testing and the owner setting direction — documented from our own records, with a transparent estimate of comparable conventional effort.
≈45 h
Owner's time
retrospective estimate
117
Code changes merged
two products · not all in production
127
Tracked issues completed
includes planning and tracking work
≈1,000–2,800 h
Modeled conventional effort
smaller-task scenario · not measured savings
The Situation
One owner, working on existing foundations: our own business platform, a client's community-marketplace platform built under contract, four client and prospect engagements, and the firm's own marketing — about 45 hours of his time across the period.
How It Ran
Claude Code and Codex (GPT-6 Astra) built and reviewed the software, with the reviewer always from a different vendor than the builder; an AI agent tested screens in a real browser; the same tools researched, drafted and edited. The owner defined the work, resolved questions, reviewed results and made the business decisions.
Our own operation, not a client engagement. Counts are from our own records for September 5–12 (through 2 p.m. Central); no client is named. The 45 hours is a retrospective estimate, and the conventional-effort figure is an internal model, not measured time saved.
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