Build Production-Ready AI Agents and RAG Pipelines
From POC to production-ready AI agents on your data. BigData Boutique helps you design, build, and deploy agentic workloads that deliver real business value—not just impressive demos. Our engineers bring deep experience in RAG pipelines, agent orchestration, and enterprise AI deployment.
Whether you're building intelligent assistants, automated research tools, or complex multi-agent systems, we ensure your agentic workloads are reliable, scalable, and production-hardened. We bridge the gap between AI experimentation and enterprise-grade deployment.
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Featured Case Studies
Max Security partnered with BigData Boutique to build SCOUT AI, an AI-powered agent that makes their proprietary intelligence instantly accessible. The solution combines OpenSearch, AWS Bedrock, and Anthropic Claude to deliver fast, accurate answers with visual insights — cutting briefing time by 79% and saving analysts 7 hours per week.
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ScreenSteps partnered with BigData Boutique to build a hybrid search framework combining Elastic Cloud, AWS Bedrock, Nova, and Cohere V4. The new architecture dramatically improved search precision, strengthened feedback loops, and reduced searches that failed to produce the correct result.
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The Challenge
Building AI agents that work in demos is easy. Building AI agents that work reliably in production is hard. Organizations struggle with hallucinations, inconsistent outputs, poor retrieval quality, security concerns, and the gap between prototype and production-grade systems. Most AI POCs never make it to production.
Agentic workloads introduce unique challenges: complex orchestration logic, tool integration, evaluation and testing frameworks, guardrails, observability, and cost management at scale. Success requires expertise that spans AI/ML, data engineering, search technology, and enterprise software development—a rare combination that most teams lack.
We Can Help
Discovery & POC
We start with understanding your use case, data landscape, and business objectives. We build a focused proof of concept that validates the approach, demonstrates value, and identifies technical risks early—before committing to full-scale development.
Architecture
We design the architecture for your agentic workload, including agent orchestration patterns, RAG pipeline design, tool integration strategy, evaluation framework, and deployment architecture. Every decision is informed by production requirements and real-world constraints.
Implementation
Our engineers build your agentic workload with production-grade code quality. We implement RAG pipelines, agent logic, tool integrations, evaluation suites, and observability—all with proper testing, documentation, and knowledge transfer to your team.
Production Hardening
We harden your agentic workload for production with guardrails, security controls, cost optimization, scaling strategies, and comprehensive monitoring. We ensure your AI agents perform reliably and safely at scale, with clear runbooks and escalation procedures.
Why Choose BigData Boutique
Data + AI Expertise
Agentic workloads sit at the intersection of data engineering, search technology, and AI/ML. Our unique combination of 15+ years in search and data with modern AI expertise means we build agents that are grounded in solid data foundations—not just LLM wrappers.
Production Focus
We don't just build POCs. We deliver production-ready systems. Every decision we make is informed by production requirements: reliability, scalability, security, cost management, and observability. We bridge the gap that stops most AI projects from reaching production.
End-to-End Delivery
From initial discovery through production deployment and ongoing support, we handle every aspect of your agentic workload. No need to coordinate multiple vendors or bridge gaps between research and engineering - we deliver the complete solution.
Ready to Build Agentic AI Workloads That Work?
Schedule a consultation with our AI engineering team to discuss your agentic workload requirements. We'll evaluate your use case, assess technical feasibility, and outline a path from POC to production-ready AI agents on your data.