Backend services, infrastructure automation, production ML platforms, and public software products — Go ingestion systems, FastAPI APIs, fault-isolated pipelines, agentic orchestration, and deployment-ready tooling.
Multi-step SPED data pipeline required manual sequential execution every week — steps were skipped, order was wrong, and failures were silent.
Districts only learned about poor student outcomes after the fact — no forward-looking indicators existed for proactive intervention.
Suspension rates varied wildly by school and had different seasonal patterns — a single global model was unreliable for planning intervention resources.
No systematic way existed to find nearby California schools that consistently outperform a given school across multiple indicators, while handling real-world data gaps correctly.
Weekly manual combining, normalizing, and routing of SEIS export files from 25 districts into a single warehouse-ready format.
25+ districts' CALPADS extract files arrived with delimiter errors, wrong LEA codes, and encoding issues — manual upload was silently corrupting warehouse data.
CALPADS ODS reports needed regular download for 25+ LEAs across both REST API-accessible and portal-only formats, using two different credential sets.
A rural LEA's SIRAS export schema was entirely different from the county's required 80+ column format — requiring weekly manual field mapping, date normalization, and disability code translation.
Charter school's Aeries SIS CALPADS extracts required navigating a complex UI, unzipping downloads, renaming files to standard convention, and uploading via SFTP each cycle.
Charter school's SIS generated attendance in a proprietary CSV format entirely incompatible with the CALPADS STAS 23-field caret-delimited fixed format required for state submission.
SEIS exports contained incorrect district name assignments caused by student transfer timing, propagating errors into downstream compliance reports.
Education data files arrived in varying formats and encodings (CSV/TXT, UTF-8/CP1252) incompatible with legacy CALPADS upload systems.
Mis-routed or misconfigured CALPADS extract files would silently overwrite correct warehouse data with no error raised during upload.
Students exit-coded as dropouts may have re-enrolled elsewhere, inflating dropout rates in federal compliance reports — verifying each SSID manually against live CALPADS was taking 3–4 hours per LEA.
21 LEAs were manually tracking IEP annual review and triennial re-evaluation deadlines in spreadsheets — missing a federal IDEA deadline is a compliance violation.
15 LEAs needed IDEA Least Restrictive Environment placement percentages calculated from SEIS data and compared against California state benchmarks — a calculation that required deep understanding of federal LRE category definitions.
14 LEAs had no consistent method to identify students needing SST referrals, 504 plans, or SEL support — each coordinator used a different ad-hoc process.
15+ LEAs had no automated way to calculate current special education service hours by type (SLP, OT, PT) or project future service demand for resource planning.
Client prospects needed to experience the full iTAAP platform without accessing production student data — a demo environment required matching compliance logic without real records.
Each SEIS data export required manually clicking ~103 checkboxes in the SEIS portal per LEA, taking 30–60 minutes per cycle — and any missed column required starting over for that LEA.
CALPADS 8.1a Student Profile Exits report is embedded inside a nested SSRS iframe and cannot be accessed via API — download required manual portal navigation every week.
A large school district needed 4 different SEIS report types downloaded weekly from different portal sections, with optional MFA and a cross-month datepicker that reset on navigation.
18 school sites had no regular visibility into their performance metrics — administrators only saw data when they logged into Power BI, which many never did.
14 stored procedures per district database needed sequential execution with a validation checkpoint in the middle — manual runs caused missed steps, wrong order, and no audit record.
150+ .pbix files across 6 directories needed regular refresh after each pipeline run — manually opening and refreshing each file in Power BI Desktop was taking hours.
Districts needed to identify California SLP and audiologist contractors from the ASHA directory — no bulk export existed, requiring individual profile page visits for each of thousands of providers.
25+ districts needed consistent, self-service reporting across 9 analytics domains — building and maintaining dashboards manually for each district was not scalable.
Complex compliance metrics needed consistent calculation logic across 94 district databases — ad-hoc queries per district produced inconsistent results that broke reporting.
Power BI dashboard consumers had no way to know if they were looking at fresh or stale data — two silent pipeline failures went unnoticed until decisions were already made.
Power BI .pbix files are opaque ZIP containers — auditing embedded data models or dashboard structure required opening each file in Power BI Desktop manually, one at a time.
12 Aeries SIS dataset types needed to be ingested into MongoDB for analytics — high-volume iteration over all schools required reliable per-entity error recovery that Python threading made complex.
Administrators had no interactive way to find and compare nearby schools on multiple performance indicators geographically — static reports required a GIS analyst to produce and were instantly outdated.
A small creative team needed consistent, structured review of incoming submissions without coupling its workflow to one model, one latency/cost tier, or an unreliable external provider.
Families, researchers, and education teams need trustworthy ways to compare California public schools, but official education data is fragmented across datasets, years, denominators, suppressed rows, and changing reporting rules.
Most online image tools require uploading private files to a server for basic compression, conversion, resizing, and PDF workflows — unnecessary friction for everyday creator and developer tasks.
AI agents need a public, crawlable network where humans and other AI systems can discover their identities, tools, capabilities, and activity instead of relying on isolated chat interfaces.
/llms.txt, robots rules, and generated sitemap alongside Google OAuth and Supabase client/data-model scaffolding