A fixed-path generative AI development program
AI Launchpad
Your AI Use Case, in Production in Weeks
AI Launchpad packages our generative AI development services into one proven path from idea to shipped: RAG development, semantic search, and agentic workloads built on your data — in your cloud or on your premises — and built to stay running. No science projects, no demos that never ship.
Plan Your Launch
Recent Launches
max-security.com
screenshot soon
Agentic RAG
SCOUT AI
An AI agent over Max Security's proprietary intelligence — OpenSearch, Bedrock, and Claude. Cut briefing time by 79%, saving analysts 7 hours a week.
Read the story →
screensteps.com
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Hybrid Semantic Search
ScreenSteps Search
A hybrid vector + keyword framework on Elastic Cloud, Bedrock, Nova, and Cohere V4 that dramatically improved search precision.
Read the story →
chatmarket.store
ChatMarket
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AI-Native Commerce
ChatMarket
A conversational storefront where shoppers discover and buy products by chatting instead of browsing categories.
Visit site →
neverblink.ai
screenshot soon
AI for Database Ops
NeverBlink
AI-powered monitoring and maintenance for Elasticsearch, OpenSearch, and ClickHouse clusters — an SRE that never blinks.
Visit site →The Launch Sequence
One repeatable path, four stages. Every engagement runs the same sequence — that's how it stays fast.
T−3
Use-Case Fit
Pick the workload with the clearest ROI. Scope, data audit, and success metrics before any code is written.
Week 1
T−2
Build on Your Data
Retrieval, pipelines, and agent logic — engineered against your real data from day one, in your cloud or on-prem.
Weeks 2–5
T−1
Harden for Production
Evals, guardrails, cost and latency tuning, observability. Ready for real traffic, not just a demo.
Weeks 6–8
LIFTOFF
Launch and Run
Go live, hand over, and keep it flying — support, optimization, and iteration after launch.
Ongoing
What We Build
AI Agents & Agentic Workloads
Production-ready AI agents that do real work on your data — orchestration, tool calling, evaluation, and guardrails included. AI agent development services →
RAG Development Services
Grounded, accurate answers over your documents with hybrid vector and keyword search — custom RAG pipelines or RAG as a service, run for you. Semantic search & enterprise AI search →
GenAI on AWS, Fast-Tracked
From idea to launch on Bedrock, SageMaker, and OpenSearch — as an AWS Advanced Tier Services Partner with the AI Services Competency. GenAI & RAG fast track →
AI Strategy & Optimization
Already have AI in production? We optimize accuracy, latency, and cost per query — and get stuck POCs unstuck. AI strategy consulting →
10+ years
Production data & search systems
Weeks
From idea to production, not quarters
100%
Built on your data
Yours
Your cloud or on-premises
Word from Mission Control
What the teams behind our launches say once their systems are live.
“
The early feedback has been great… Our clients have told us the chatbot is a practical, high-impact tool that's already becoming a key part of how their analysts operate day to day.
Dror Becker
CEO, Max Security · SCOUT AI
“
We needed more than a search vendor — we needed a partner who could understand how our users think. BigData Boutique's mix of AI innovation and infrastructure know-how delivered exactly that.
Executive Team
ScreenSteps · Hybrid AI Search
“
BigData Boutique's understanding of hybrid retrieval and their ability to merge semantic and keyword search were key. The new architecture doesn't just improve results — it changes how users trust the search itself.
Executive Team
ScreenSteps · Hybrid AI Search
Ready to Take Your AI to Production?
Bring a use case — we'll tell you in one call whether it's ready to build, and what it takes to get it to production.
AI Engineering Insights
Frequently Asked Questions
What do generative AI development services include?
Generative AI development services cover the full path from use case to production system: data pipelines and indexing, retrieval and RAG development, LLM integration, agent orchestration, evaluation and guardrails, and cost and latency tuning — delivered on your data, in your cloud or on-premises.
What is RAG as a Service?
RAG as a Service means we build and operate your retrieval-augmented generation stack for you: ingestion, hybrid vector and keyword search, context assembly, and the LLM layer. You get grounded answers over your documents without hiring a search engineering team.
How long does it take to get to production?
Weeks, not quarters. The Launch Sequence runs use-case fit in week 1, builds on your real data in weeks 2–5, and hardens for production — evals, guardrails, observability — in weeks 6–8, followed by launch and ongoing support.
Related AI Resources: Generative AI Consulting · AI Agent Development Services · LLM Development Services · AI Readiness Assessment · GenAI & RAG on AWS · What is Agentic AI?
