Federal Agencies · Intelligent ETL
Every agency speaks a different data dialect.
NNIPA translates.
Intelligent ETL
Reads schema, resolves meaning, produces one governed dataset — driven by AI/ML/NLP, not hard-coded scripts.
Fifty states, a dozen backends, no shared vocabulary. NNIPA's Intelligent ETL engine reads schema, resolves field meaning, and produces one governed, metadata-rich dataset — driven by AI/ML/NLP, not hard-coded scripts.
ssn_numSocialSecurityNumberemp_wage_amtWageAmountclm_dtClaimDatest_cdSourceState
Where this runs today
Two very different messes, one engine
Unemployment insurance data from state agencies
50+ states, 50+ backends — Oracle, SQL Server, CSV, APIs — each defining fields on its own terms. NNIPA detects schema per state, maps 'ssn_num' and its variants to SocialSecurityNumber, normalizes dates and income ranges, tags every record with source_state and source_system, masks PII, and loads it all into one schema in PostgreSQL or MongoDB.
Title 5 workforce survey data from federal agencies
Dozens of agencies, three formats — Excel, XML, and OCR'd PDFs — each with its own headers, codes, and narrative justifications. NNIPA extracts structured data from all three, uses NLP on free-text justifications, reconciles agency-specific job classifications, and generates metadata automatically.
Any format, any state
Ingestion
Manual upload or automated pipeline — NNIPA takes Oracle exports, SQL Server, CSV, REST APIs, Excel, XML, and scanned PDFs without a pre-agreed contract.
- Oracle
- SQL Server
- CSV / Excel
- REST APIs
- XML payloads
- Scanned PDFs
No two states alike
Schema detection & inference
Structure is auto-detected on arrival. Every column is typed — text, date, integer, SSN, PII — before a single mapping decision is made.
- Structure inferred per source, per file
- Column-level type detection, including PII
- Works across CSV, Excel, JSON, XML, SQL dumps
The core translation layer
AI-powered field mapping
Semantic matching and similarity scoring align messy source fields to one canonical schema, with fallback rules for anything ambiguous.
ssn_numSocialSecurityNumberemp_wage_amtWageAmountclm_dtClaimDatest_cdSourceStateEvery record, traceable
Normalization & metadata
Dates, codes, casing, and currency are standardized; duplicates and nulls are handled before load. Every output carries its own paper trail.
Attached metadata
source_name: DOL_OIG_UI_2026origin_agency: State of Ohiocontains_pii: trueconfidence_score: 0.94OCR + NLP
Unstructured data handling
Scanned files run through OCR; NLP pulls entities and intent from narrative justifications, converting free text into rows and columns.
- OCR on scanned files and PDFs
- NLP entity and sentiment extraction
- Free text converted to structured fields
Nothing untracked
Load & governance
Transformed data lands in PostgreSQL, MongoDB, or ElasticSearch. Mapping logs, overrides, and job history stay queryable for audit.
- PostgreSQL · MongoDB · ElasticSearch
- Full mapping history per job
- User overrides logged and fed back into the model
Prompt-driven ETL
Tell NNIPA what you need. Skip the pipeline config.
No DAGs to hand-wire. Describe the transformation in plain language and NNIPA interprets the prompt, then runs the correct detection, mapping, and load steps behind it.
Book a Demo> Extract employment and wage data, standardize the date fields, and flag duplicates.
Detecting schema across 12 sources...
Mapping 214 fields to canonical schema...
6 fields flagged for manual review
Ready to translate every agency's data dialect?
See how NNIPA's Intelligent ETL engine can turn fifty states of mismatched schema into one governed, metadata-rich dataset.