Engineering an AI engine for enterprise-grade decision intelligence

A governed AI engine for fast, actionable, auditable decisions

AgileEngine drove the implementation of the core scoring logic of the client’s flagship product. To ensure accuracy and accountability, the scoring layer uses deterministic calculations, with a separate LLM layer then translating precise math into business recommendations. In addition to the decision engine, our implementation covers the vital data processing and integration foundations, such as model scaffolding infrastructure, RAG pipeline, and Jira write-back.

Key deliverables

• Deterministic, GPT-independent scoring engine leveraging predictable, auditable calculation logic
• Context-bounded RAG pipeline with a filter-first retrieval strategy, fixed token budget, and full retrieval logging
• OpenAI GPT-5 integration with controlled parameters and governance guardrails for recommendation language
• Closed-loop Jira write-back with auditable comments and versioned decision history
• Unified data model laying the groundwork for Jira, Asana, Monday.com, and Airtable integrations
• Recommendation card user interface and data issues panel
• Synthetic Jira dataset test harness for benchmarking time-to-decision and decision velocity pre-rollout, with accept, modify, and dismiss events captured as ground-truth data for future model refinement
• ML staffing-inference scaffolding and NLP maturity-scoring hooks
• Per-tenant organization model supporting enterprise SaaS requirements
• Cloud infrastructure and access-control foundation built with a defined scaling path from static hosting to containerized microservices and then serverless auto-scaling

Technologies

AWS (Lambda, Amazon SQS, Amazon SNS, Amazon RDS, PostgreSQL, Amazon S3, AWS Amplify, Amazon Cognito, AWS IAM, Amazon CloudWatch), REST API, OpenAI GPT-5, Jira, JSON