Tesla's Agent Python platform handled pricing logic for a complex set of dependent services — but the existing architecture had grown to the point where pricing computation took up to 12 minutes and data consistency across downstream systems was unreliable. With over five systems depending on accurate pricing data, errors and latency had compounding effects throughout the platform.
Sapot Systems designed and implemented a new API layer using Python, GraphQL, Django, and Django REST Framework to serve the Agent platform's pricing and lexicon generation needs. The work involved both new API architecture and a careful decomposition of existing pricing logic to ensure that downstream dependencies were mapped and handled correctly throughout the migration.
The results were significant: computation time dropped from 12 minutes to approximately 90 seconds, pricing accuracy improved by 22%, and data consistency across all five-plus dependent systems was substantially improved. The engagement ran 14 months with a team of three engineers embedded in Tesla's environment.
Why this matters for federal work: Complex API integrations between dependent systems — with strict accuracy and consistency requirements — are common across federal data programs, benefits systems, and financial platforms. This engagement demonstrates the kind of disciplined API engineering and dependency management that federal environments demand.
Pricing APIs and lexicon generation across dependent downstream systems — accuracy and consistency at scale.
Sapot Systems built APIs for Tesla’s Agent Python platform to support dependent pricing services and lexicon generation using Python, GraphQL, Django, and Django REST Framework — improving pricing accuracy, reducing computation time, and driving data consistency across more than five downstream systems.
Engagement details: Team: 3 engineers · Duration: 14 months · Environment: Enterprise pricing platform
What we delivered:
- Python/GraphQL/Django API architecture for pricing services
- Lexicon generation services supporting downstream dependencies
- Data consistency improvements across 5+ integrated systems
- Pricing computation performance optimization
Results:
22%improvement in pricing accuracy across dependent services- Pricing computation time reduced from
12 minutes → ~90 seconds - Improved data consistency across
5+downstream systems
