Enterprise AI on AWS with Bedrock & AgentCore
AWS Partner Network members who build production AI systems on Bedrock, not just proof-of-concepts. We integrate Claude, Llama, and Titan models with your existing AWS infrastructure through AgentCore, Lambda, SageMaker, and the services your team already knows.
Technology Partners
Recognized by Clutch
What We Build with AWS Bedrock & AgentCore
From Bedrock integration to multi-agent systems on AgentCore, we deliver enterprise AI solutions on AWS that scale.
Bedrock LLM Integration
Production applications using Claude, Llama, Titan, and other foundation models through AWS Bedrock. We handle model selection, prompt optimization, streaming responses, guardrails configuration, and the IAM policies that keep your AI deployments secure within your AWS organization.
AgentCore Multi-Agent Systems
Build and deploy AI agents with AWS AgentCore that coordinate across your enterprise systems. We design agent architectures with tool use, memory, and orchestration that scale automatically and integrate with your existing AWS services.
Enterprise AI Security & Compliance
AI deployments that meet enterprise security requirements. We implement VPC endpoints for Bedrock, KMS encryption for model inputs and outputs, CloudTrail logging for audit compliance, and Bedrock Guardrails to prevent prompt injection and harmful content.
Serverless AI Pipelines
Event-driven AI processing with Lambda, Step Functions, and Bedrock. We build serverless architectures that process documents, classify content, generate summaries, and route decisions at scale without managing infrastructure.
Knowledge Bases & RAG on Bedrock
Production RAG systems using Bedrock Knowledge Bases with OpenSearch, Aurora, or Pinecone as vector stores. We handle document ingestion, chunking strategies, embedding model selection, and retrieval optimization for accurate, sourced AI responses.
Model Fine-Tuning & Customization
Custom model training on SageMaker with deployment through Bedrock. We handle data preparation, training infrastructure, evaluation pipelines, and A/B testing between base and fine-tuned models to validate improvements before production rollout.
Why AWS AI Projects Need Senior Cloud Engineers
AWS Bedrock makes it easy to call an LLM. Making it production-ready on AWS is a different challenge entirely. You need IAM policies that follow least-privilege without blocking your AI pipeline. You need VPC endpoints so model traffic stays off the public internet. You need KMS encryption for data at rest and in transit. You need CloudTrail logging that satisfies your compliance team. And you need all of this to work together without turning your deployment into a three-month security review.
AgentCore is AWS's answer to multi-agent orchestration, but it is new and the documentation is sparse. Most teams that try it end up with fragile agent workflows that break under load or accumulate costs they did not expect. We have built agent architectures on AWS that handle thousands of requests daily, with proper error handling, cost controls, and the observability needed to debug agent decisions in production.
As AWS Partner Network members, we have direct access to AWS solution architects and early access to new services. When you hit a limitation in Bedrock or AgentCore, we know whether it is a configuration issue, a known limitation with a workaround, or something we need to escalate to the AWS team. This access saves weeks of debugging and dead ends.
Our Tech Stack
We work across the AWS AI ecosystem and integrate with the tools your team already uses.
AWS AI Projects We Have Delivered
Real results from production AWS Bedrock deployments.
AI Sales Assistant on AWS
Built a Bedrock-powered sales assistant with RAG using Knowledge Bases and OpenSearch. Deployed on Lambda for serverless scaling with full CloudTrail audit logging for enterprise compliance.
Read Case StudyMulti-Agent System on AgentCore
Designed a multi-agent architecture on AWS AgentCore where specialized agents handle research, analysis, and reporting. Integrated with existing AWS services through IAM roles and VPC endpoints.
Read Case StudyAI-Powered Dynamic Pricing
Built a dynamic pricing system using SageMaker for model training and Bedrock for real-time inference. Lambda functions process pricing decisions at scale with sub-second latency.
Read Case StudyHow We Work
A straightforward process from first call to production deployment.
Discovery Call
We start with a 30-minute technical conversation to understand your data, your users, and your constraints. No sales pitch. We dig into what you have tried, what failed, and what success looks like.
Architecture Proposal
Within a week, we deliver a detailed technical proposal: system architecture, technology choices with rationale, estimated timeline, and cost breakdown. You will know exactly what we plan to build and why.
Build & Ship
We build iteratively with weekly demos. You see working software from week one, not slide decks. Every PR is reviewed, every decision is documented, and we transfer knowledge continuously so your team can maintain what we build.
Frequently Asked Questions
Ready to Build Enterprise AI on AWS?
Tell us about your AWS AI project and we will respond within 24 hours with an initial assessment. Whether you need Bedrock integration, AgentCore agents, or help migrating existing AI to AWS.
Get a Free Assessment
Describe your AWS AI project and we'll assess how Bedrock and AgentCore can power your enterprise AI.