PromptLogic provides the industrial-grade middleware to transform stochastic LLM outputs into structured, validated, and executable business logic. Built natively for the AWS ecosystem.
Robust tools for managing the prompt-to-logic lifecycle.
Automated schema enforcement (Pydantic/Zod) ensuring LLM outputs strictly follow your business rules and database constraints.
Seamlessly deploy prompt chains as AWS Lambda functions or SageMaker endpoints with one-click CI/CD.
Integrated memory layer using Amazon OpenSearch for long-term prompt context and semantic consistency.
Our stack is designed to leverage high-compute AWS resources to ensure low-latency reasoning and high-throughput data processing.
Utilizing Claude 3.5 and Llama 3 for high-reasoning tasks via AWS Bedrock APIs.
Complex prompt chains managed by AWS Step Functions for stateful reliability.
Real-time token usage tracking and performance logging for auditability.
Integrate deterministic AI into your CI/CD pipeline.
from promptlogic import LogicEngine, BedrockProvider
# Initialize AWS Bedrock through PromptLogic
engine = LogicEngine(provider=BedrockProvider(region="us-east-1"))
# Define a prompt with structured logic validation
logic_chain = engine.create_chain(
prompt="Analyze quarterly fiscal reports",
schema=FiscalOutputModel,
strict_mode=True
)
result = logic_chain.execute()
# Result is now a validated Python object, not just text
PromptLogic is scaling its infrastructure. We are currently heavy users of p4d.24xlarge instances for custom model fine-tuning and Amazon Bedrock for global inference.