Practice Area
We architect retrieval pipelines, fine-tuned classifiers, and agentic workflows on Amazon Bedrock and SageMaker — then ship them with the observability, guardrails, and cost controls production demands.
Overview
We build retrieval-augmented systems, fine-tuned models, and agentic workflows that move from prototype to production-grade deployment on AWS — with the cost guardrails and operational discipline production demands.
How We Work
Fine-tuning runs inside your VPC, so proprietary data doesn't leave your security perimeter. Models are trained, evaluated, and deployed within your private cloud boundary.
We build on Bedrock and SageMaker — but the model artifacts, training data, and inference endpoints stay yours. No proprietary middleware, no opaque overlays.
Built on AWS
We build directly on AWS's AI/ML stack — Bedrock, SageMaker, and the broader Generative AI service catalog — using the performance, security, and pricing primitives the platform exposes to every builder. No proprietary middleware, no opaque overlays.
AWS Solutions for Generative AI
Foundation model access with enterprise guardrails, prompt management, and RAG pipelines built on your private knowledge bases.
Rapid prototyping and deployment of pre-trained models with one-click fine-tuning on your domain-specific datasets.
Semantic search and retrieval architectures powered by OpenSearch and pgvector for high-throughput, low-latency embedding queries.
Domain-specific tuning of foundation models on SageMaker, with optional inference optimization on AWS Inferentia where workload characteristics justify it.
Evaluation harnesses, prompt-injection guardrails, and audit logging embedded in every pipeline — so model behavior is observable, reviewable, and reproducible.
We architect retrieval pipelines, fine-tuned classifiers, and agentic workflows on Amazon Bedrock and SageMaker — then ship them with the observability, guardrails, and cost controls production demands.