Capitol

Capitol AI staff embed with your teams to understand the actual workflows, data environments, and decision points where AI can deliver measurable value. Our approach is model-agnostic and infrastructure-aware by design: we help you build governed, composable AI systems on platforms like Amazon Bedrock. Multi-agent pipelines, retrieval-augmented workflows powered by Bedrock Knowledge Bases, human-in-the-loop decision support governed by Bedrock Guardrails, and more. Whether you're a federal agency navigating FedRAMP and mission-aligned requirements or an enterprise making sense of a fragmented vendor landscape, we bring the technical depth to build these systems and the institutional fluency to land them in organizations where change is hard and the stakes are high.

We staff engagements with specialized roles built for the full AI delivery lifecycle. Our AI Product Experts bring deep context engineering expertise: designing the prompt architectures, agent orchestration patterns, and retrieval strategies that determine whether an AI system gives your team useful answers or useless ones. They architect Bedrock Agent workflows with action groups, knowledge base integrations, and guardrail configurations so that your agents operate within enforceable boundaries. Not just permission to access tools, but controls on when and how they use them. AI/ML Engineers and ML Engineers handle the hard infrastructure underneath: inference optimization across Bedrock's model catalog, including Cohere embedding and reranking models for high-precision retrieval, model routing between foundation models, fine-tuning with Amazon SageMaker, custom classifiers, embeddings pipelines backed by S3 Vectors or OpenSearch Serverless, and evaluation harnesses that measure whether your system is actually improving over time. Data Pipeline Engineers build the ingestion, transformation, and orchestration layers that feed your AI systems clean, timely, governed data: AWS Glue for serverless ETL, Step Functions for workflow orchestration, Lambda for event-driven processing, all wired to structured and unstructured sources across S3, Amazon RDS, Redshift, Aurora, and beyond.

On the delivery and experience side, we ensure AI actually reaches users and earns their trust. Product Engineers and Frontend Engineers build the full-stack applications that put AI capabilities into critical contexts, containerized on Amazon EKS for the scale and deployment flexibility that production AI workloads demand, connected to Amazon Cognito for identity, API Gateway for secure exposure, and CloudWatch for the observability layer that keeps production systems honest. Our Experience Designers specialize in human-in-the-loop interfaces where users can inspect AI reasoning, override decisions, and build confidence in the system over time. And our Forward Deployed Engineers (FDEs) are the tip of the spear: embedded with your team, owning end-to-end integration from IAM and SSO permissions architecture to Bedrock Knowledge Base data connectors, RDS database configuration, and CloudWatch production monitoring. They configure Bedrock Guardrails for content filtering and PII detection, wire up CloudTrail audit trails for compliance, and ensure your VPC boundaries and KMS encryption meet the security posture your organization requires. They're technical enough to write code and operational enough to translate business requirements for the engineering team.