Tag: Life Sciences

Customer

As part of a GPT-4 to LLaMA2 migration, Neo Analyst—an Antler-backed enterprise SaaS analytics startup, set out to modernize its AI architecture for large-scale enterprise adoption. The platform delivered NLP-driven analytics and recommendations but faced growing resistance from enterprise customers due to reliance on proprietary LLMs, high inference costs, and strict compliance requirements. To unlock enterprise growth, Neo Analyst needed an open, compliant, and scalable AI foundation without compromising performance.

Business Objective

Neo Analyst aimed to:

  • Replace GPT-4 with an enterprise-compliant open LLM (LLaMA2)

  • Maintain or exceed GPT-4-level accuracy and reasoning quality

  • Meet strict GDPR and SOC2 compliance requirements

  • Reduce AI inference and infrastructure costs at scale

  • Enable multi-agent orchestration for advanced analytics workflows

  • Build a serverless, scalable AWS-native architecture

  • Accelerate AI adoption across enterprise customer workflows

Together, these goals defined the roadmap for a GPT-4 to LLaMA2 migration aligned with enterprise readiness.

Scope of Services

BXI Technologies partnered with Neo Analyst to execute an end-to-end AI platform transformation.

Enterprise Compliance Readiness

  • Implemented GDPR-aligned data governance and privacy controls

  • Established SOC2 alignment across security, availability, and confidentiality

  • Secured AI workflows and agent communication channels

GPT-4 to LLaMA2 Migration

  • Replaced all GPT-4 modules with hosted LLaMA2 7B models

  • Performed instruct-tuning and fine-tuning to replicate GPT-style reasoning

  • Benchmarked accuracy and output quality to meet or exceed GPT-4 performance

Multi-Agent AI Architecture

  • Designed agent-based orchestration supporting:

    • AI data analyst

    • Recommendation engine

    • Query interpreter

    • Insights generator

  • Enabled real-time coordination between agents for coherent analytics

AWS-Native, Serverless Architecture

  • Rebuilt the platform using AWS Lambda-based microservices

  • Enabled auto-scaling, fault tolerance, and high availability

  • Applied native AWS IAM, encryption, and security policies

This GPT-4 to LLaMA2 migration delivered a cost-efficient, enterprise-ready AI platform.

Benefits

  • Open-source AI architecture aligned with enterprise expectations

  • Strong compliance posture supporting regulated customers

  • Reduced AI inference and infrastructure costs

  • Improved platform reliability and scalability

  • Advanced analytics powered by coordinated AI agents

  • Faster onboarding of enterprise customers

Impact

  • Full migration and AWS hosting completed in 8 weeks

  • System uptime increased from 80% to 99%

  • 30% reduction in AI inference and cloud costs

  • SOC2 and GDPR compliance achieved for enterprise deployment

  • Performance matched or exceeded GPT-4 for analytics use cases

  • Enabled enterprise deals previously blocked by GPT-based architecture