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Federal contractor competitive intelligence. Posture scoring, posture classes, and scored datasets for every federal contractor.

Project description

FedComp Index

FedComp Index

Federal contractor competitive intelligence. Scores every federal contractor in a state from 0 to 100 based on public award data, assigns posture classes, and provides pre-scored datasets.

This is the meta-package. It installs:

Install

pip install fedcomp-index

Usage

from fedcomp_index import score_contractor, load_state, lookup, PostureClass

# Score a contractor from raw award data
result = score_contractor(
    total_awards_usd=45_000_000,
    last_award_date="2025-09-15"
)
print(result.fedcomp_index)   # 65
print(result.posture_class)   # PostureClass.CLASS_1

# Load pre-scored data
contractors = load_state("NV")
print(f"{len(contractors)} contractors scored")

# Look up by UEI
c = lookup("CGAKREGGN9J3")
print(c["legal_name"])        # FLEET VEHICLE SOURCE INC
print(c["fedcomp_index"])     # 81
print(c["posture_class"])     # Class 1

Posture scoring methodology

Two index drivers, no normalization:

Index driver Weight How it works
Award volume 90% log10 of total dollars won, mapped to 0-100
Award recency 10% Last award date, bucketed by age

Posture classes are fixed thresholds:

Posture class FedComp Index score Typical award volume
Class 1 60-100 $100M+
Class 2 40-59 $5M-$100M
Class 3 0-39 Under $5M

Full methodology: fedcompindex.org/methodology

Packages

Package What it does
fedcomp-index Meta-package (this one)
fedcomp-index-scoring Posture score computation
fedcomp-index-data Pre-scored contractor datasets

Data sources

All public federal records:

  • USASpending.gov - award history, dollar amounts, agencies, NAICS, PSC
  • SAM.gov - entity registration, certifications, CAGE/UEI
  • SBA.gov - certification verification

Datasets

Also available on npm

npm install fedcomp-index

Links

Citation

@software{fedcomp_index,
  title = {FedComp Index},
  author = {FedComp Index},
  url = {https://fedcompindex.org/},
  year = {2026}
}

License

MIT

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