Deploy foundation models, build RAG pipelines, and orchestrate AI agents — all within your AWS account. Data stays private. Compliance stays intact. AI moves fast.
Our AWS and AI architecture teams also deliver solutions using AI Development, Generative AI Development, Cloud-Native Development to deliver robust, future-proof applications.
From model selection and RAG development to agent orchestration and production deployment on AWS.
Deploy Claude, Titan, Llama, and Mistral models through Bedrock's managed API — no infrastructure management, with built-in scaling and pay-per-token pricing.
Retrieval-Augmented Generation using Bedrock Knowledge Bases, OpenSearch Serverless, and S3 — connecting foundation models to your enterprise data.
Build autonomous AI agents that can orchestrate multi-step workflows, call APIs, query databases, and take actions using Bedrock's agent framework.
Fine-tune foundation models on your proprietary data using Bedrock's managed fine-tuning — without exposing your data to third-party providers.
Implement Bedrock Guardrails for content filtering, PII redaction, topic avoidance, and grounded responses — keeping your AI outputs safe and compliant.
End-to-end AI architecture on AWS — combining Bedrock with Lambda, Step Functions, SageMaker, and DynamoDB for production-grade AI systems.
Bedrock gives enterprises the power of foundation models with the security, compliance, and operational maturity of AWS.
Unlike third-party AI APIs, your data never leaves your AWS account. Critical for HIPAA, SOC 2, and financial services compliance.
Access Claude, Titan, Llama, Cohere, and Stability AI through a single API — switch models without rewriting integration code.
Bedrock handles auto-scaling, load balancing, and model serving infrastructure — you focus on building features.
Pay-per-token pricing with provisioned throughput options. No upfront GPU costs or reserved capacity surprises.
AWS Bedrock is a fully managed service that gives you API access to leading foundation models (Claude, Titan, Llama, Mistral, and others) without managing any infrastructure. It includes features for model customization, RAG, agent orchestration, and guardrails — all within your AWS account.
The key difference is data residency and enterprise control. With Bedrock, your data stays within your AWS account and is not used for model training. You also get VPC endpoints, IAM access control, CloudTrail logging, and the ability to switch between multiple model providers through a single API.
Yes. Bedrock Knowledge Bases provide managed RAG infrastructure — including document ingestion, chunking, embedding generation, and vector storage via OpenSearch Serverless. We configure the full pipeline and optimize retrieval quality for your specific document types.
A typical project includes: architecture design (1–2 weeks), Bedrock setup with IAM and VPC configuration (1 week), RAG or agent development (2–4 weeks), guardrails and testing (1–2 weeks), and production deployment (1 week). Total timeline is 6–10 weeks depending on complexity.
Yes. Bedrock is HIPAA-eligible, SOC 2 compliant, and supports PCI DSS workloads. Data is encrypted in transit and at rest, model invocations can be logged via CloudTrail, and VPC endpoints keep traffic off the public internet.