4 to 8 weeks from kickoff to working prototype with real enterprise data. Week 1-2: discovery, use case scoping, and data assessment. Week 3-5: architecture design and core build. Week 6-8: integration, testing, and stakeholder demo. The POC delivers a production-grade architecture (not a throwaway demo) that can be scaled directly into full deployment. Timeline depends on data readiness, integration complexity, and approval cycles on your side.

Yes. Our architectures are designed to work with existing ERP, CRM, HRIS, document management, and internal applications — not replace them. We integrate via APIs, event-driven architectures (EventBridge, SQS), database connections, and file-based ingestion. AI agents can read from and write to your existing systems, execute workflows, and surface insights within tools your teams already use. We don’t require data migration or platform replacement.

This is exactly what agentic AI is built for. Unlike simple chatbots that answer questions, AI agents can: plan multi-step tasks, use tools (query databases, call APIs, process documents), make decisions with human approval gates, maintain memory across sessions, and coordinate with other agents. We deploy agents that handle underwriting workflows, procurement approvals, compliance checks, research synthesis, and operational automation — operating within existing governance structures, not outside them.

AWS provides the most comprehensive security infrastructure for AI workloads. Our deployments include: VPC isolation with private subnets, IAM roles scoped per agent and per service, encryption at rest (KMS) and in transit (TLS 1.3), Bedrock Guardrails for content filtering and policy enforcement, Amazon Macie for sensitive data detection, CloudTrail for audit logging, and human-in-the-loop approval gates for high-stakes actions. Every deployment is aligned with the AWS Well-Architected Framework’s security pillar.

An AWS Advanced Tier Services Partner is a consulting firm that has met rigorous AWS requirements for technical expertise, customer delivery, and cloud competency. a21.ai is an AWS Advanced Tier Partner specializing in Generative AI and Agentic AI — meaning we have demonstrated deep capability in designing, building, and deploying production AI systems on AWS infrastructure, with validated architectures and successful enterprise engagements.

Amazon Bedrock is a multi-model platform — it gives you access to Claude, Llama, Mistral, Nova, and others through a single API, with built-in guardrails, fine-tuning, and enterprise security. OpenAI APIs give you access to GPT models only. The key differences: Bedrock runs within your AWS account (data never leaves your VPC), supports model switching without code changes, integrates natively with AWS services like S3, Lambda, and OpenSearch, and provides enterprise features like provisioned throughput and model evaluation. For enterprises already on AWS with data privacy requirements, Bedrock is the more architecturally sound choice.

Yes. Every system we build runs within the customer’s own AWS account. Data stays within your VPC, models are accessed via private endpoints, and there is no data sharing with third-party providers. We implement VPC isolation, private subnets, encryption at rest and in transit, and IAM policies scoped to least privilege. For regulated industries, we additionally support compliance frameworks including SOC 2, HIPAA, and GDPR alignment at the infrastructure layer.

We work across BFSI (banking, financial services, insurance), pharmaceuticals and healthcare, retail and CPG, legal, manufacturing, and technology. Our solutions include underwriting copilots, clinical trial intelligence, demand forecasting, contract analysis, knowledge management systems, and customer-facing AI assistants. The underlying architecture — agentic orchestration, RAG, multi-model routing — is industry-agnostic, but we tailor data pipelines, compliance controls, and workflow integrations to each sector’s specific requirements.

Yes — this is core to our approach. We design systems that route requests to the optimal model based on task complexity, latency requirements, and cost. A supervisor agent might use Claude for complex reasoning, Nova for high-speed simple tasks, and Llama for specialized domain work — all through Amazon Bedrock’s unified API. This avoids vendor lock-in, optimizes cost (often 40-60% savings vs. single-model approaches), and allows you to adopt new models as they become available without re-architecting.

We primarily work with Amazon OpenSearch Service for vector search and RAG pipelines — it provides native vector engine support, hybrid search (keyword + semantic), and integrates directly with Bedrock Knowledge Bases. Depending on requirements, we also implement Amazon Aurora with pgvector for structured + vector workloads, Amazon Kendra for enterprise document search, and DynamoDB for agent memory and session state. The choice depends on scale, latency requirements, and whether you need hybrid retrieval patterns.