Odaptos partnered with Futuralis to build a scalable Generative AI and MLOps platform on AWS. Using Amazon Bedrock, SageMaker, AWS Lambda, Aurora PostgreSQL with PGVector, and ECS Fargate, the solution enabled real-time AI interactions, semantic search, automated model training, and secure model lifecycle management.
Odaptos lacked a centralized platform for hosting and managing AI models.
Model training, testing, and deployment processes required greater automation.
The existing platform did not have a scalable vector search and RAG framework.
Multiple AI services and ingestion workflows needed reliable coordination.
Odaptos required model versioning, approval workflows, traceability, and rollback capabilities.
Enabled fast and context-aware conversational AI experiences.
Simplified model training, evaluation, approval, deployment, and rollback.
Improved knowledge retrieval using embeddings and vector-based search.
Serverless and managed AWS services minimized infrastructure management.
Private networking, IAM controls, monitoring, and event-driven services supported enterprise-grade operations.
Odaptos needed a centralized and scalable AI/ML platform to support growing AI workloads, knowledge retrieval systems, and future model training requirements. The existing architecture lacked a fully integrated MLOps framework capable of managing AI inference, vector-based retrieval, model hosting, and automated training pipelines in a secure and scalable manner.
Key challenges included:
• Lack of centralized AI model hosting architecture
• Limited automation for AI model training and deployment
• No scalable semantic search and retrieval pipeline
• Difficulty managing long-term AI context and embeddings
• Need for secure and scalable AI-powered APIs
• Lack of event-driven document ingestion and metadata extraction
• Requirement for AI orchestration across multiple AWS services
• Need for governance, version control, and rollback mechanisms for AI models
Odaptos partnered with Futuralis because of its expertise in AWS AI/ML architectures, MLOps automation, serverless application development, and cloud-native AI infrastructure engineering.
AWS provided the managed AI and machine learning services required to build a scalable, automated, and secure AI ecosystem while minimizing infrastructure management overhead.
Futuralis designed a phased AI/ML transformation strategy focused on:
• Real-time AI-powered backend orchestration
• AI model hosting and lifecycle management
• Retrieval-Augmented Generation (RAG) architecture
• Semantic search and vector embedding pipelines
• Automated SageMaker training pipelines
• AI governance and model versioning
• Event-driven automation and scalable AI workflows
Futuralis designed and implemented a modern Generative AI and MLOps platform on AWS using Amazon Bedrock, SageMaker, Lambda, Aurora PostgreSQL with PGVector, ECS Fargate, and serverless event-driven workflows.
Phase 1 – Real-Time AI Backend Platform:
• Implemented a serverless AI-powered backend using Amazon Bedrock Claude 3, API Gateway, AWS Lambda, DynamoDB, SQS, and Aurora PostgreSQL with PGVector
• Developed REST APIs for AI interactions, session tracking, and contextual retrieval
• Built CI/CD pipelines using Bitbucket for infrastructure and backend deployments
• Implemented Infrastructure-as-Code using AWS CloudFormation
• Configured secure networking, IAM policies, logging, and monitoring
Phase 2 – Knowledge Ingestion & Semantic Search:
• Developed AI-powered document ingestion pipelines using Amazon S3, Amazon Textract, and SageMaker Processing Jobs
• Built RAG-based semantic retrieval architecture using LangChain and Amazon Titan Embeddings
• Implemented vector embedding storage using Aurora PostgreSQL with PGVector
• Designed Step Functions and ECS Fargate workflows for transcript chunking, indexing, and retrieval
• Enabled event-driven execution using Amazon EventBridge
Phase 3 – Automated MLOps & Fine-Tuning:
• Implemented SageMaker Pipelines for preprocessing, training, evaluation, and deployment automation
• Created model registry, approval workflows, rollback mechanisms, and model version tracking
• Automated Bedrock model import workflows using Lambda and Systems Manager Parameter Store (SSM)
• Enabled continuous fine-tuning and live model refresh without downtime
• Built automated data ingestion workflows for transcripts and training datasets
Odaptos is an AI-driven interview and assessment platform focused on delivering intelligent candidate evaluation and conversational AI capabilities. As the platform evolved, Odaptos required a scalable and automated AI/ML infrastructure capable of supporting real-time AI interactions, semantic search, knowledge ingestion, and continuous AI model training on AWS.
