AWS Bedrock is our platform of choice when customers want multi-model access — Claude, Llama, Titan, Mistral — inside their existing AWS security perimeter.
Bedrock lets us route per-task to the best model without a new procurement cycle each time. It sits inside the customer's AWS account with VPC endpoints, KMS keys and IAM controls the security team already trusts.
T7 handles model access requests, guardrail configuration, Knowledge Bases (managed RAG) and Agents for Bedrock end-to-end.
Same interface for Claude, Llama, Mistral, Titan — route per task on quality and cost.
Managed RAG with S3 sources, OpenSearch or pgvector, per-tenant filtering.
Tool-using agents wired to Lambda actions and API destinations, with tracing.
Content and topic guardrails applied consistently across every model in the stack.
Production-grade GPT, Claude, Gemini and open-source LLMs — grounded in your data.
Explore serviceMulti-agent architectures that plan, use tools and complete complex tasks.
Explore serviceCustom AI chatbots trained on your data — web, WhatsApp, Slack and beyond.
Explore serviceClaude Sonnet and Haiku for most reasoning workloads, Llama for cost-sensitive open-source use, Titan Embeddings for retrieval.
Bedrock for foundation models via API; SageMaker when we need to train, fine-tune or host custom models. We often combine both.
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