Private GenAI Search & RAG for eCommerce Using Bedrock & OpenSearch
SnapTec builds private, VPC-only GenAI search solutions using Amazon Bedrock and OpenSearch to deliver sub-second search, high relevance, and policy Q&A for eCommerce and B2B, without customer data ever leaving your own environment.
AWS Advanced Consulting Partner | VPC-Only, Data Never Leaves Your Environment
What This Solution Actually Does
A lot of "AI search" implementations quietly send customer queries and product data to a third-party API outside your control, which is a real problem the moment your catalog, pricing, or customer data includes anything remotely sensitive. We built our GenAI search practice specifically to avoid that tradeoff: a private, VPC-only architecture using Amazon Bedrock and OpenSearch that delivers genuinely fast, relevant AI-powered search and Q&A, with your data never leaving your own AWS environment.
This is a Retrieval-Augmented Generation, RAG, architecture: instead of a language model answering from general training knowledge, it retrieves relevant information from your actual product catalog, documentation, or policies through OpenSearch, then uses Amazon Bedrock's foundation models to generate a precise, grounded answer based specifically on that retrieved content. For a customer or internal team member asking a question, the result is an answer that's actually accurate to your business, not a plausible-sounding guess from a general-purpose model.
When Private GenAI Search Makes Sense
This is the right investment once your product data, documentation, or support volume have real complexity.
Your Catalog or Documentation Has Real Complexity
Technical B2B catalogs, large support knowledge bases, and complex policies are where retrieval-grounded answers meaningfully outperform keyword search.
You Handle Payment-Adjacent or Proprietary Data
For businesses handling payment-adjacent data, proprietary pricing, or B2B contract terms, VPC-only architecture is frequently a compliance requirement, not a nice-to-have.
You've Validated Simpler Search Isn't Sufficient
This isn't a starter project. It's the right investment once you've confirmed a simpler search or support solution genuinely isn't enough.
Your Support Volume Has Outgrown a Static FAQ
Policy and support questions grounded in your actual current policies, not an outdated FAQ page, reduce repetitive support volume at scale.
Where This Applies in eCommerce and B2B
Four places retrieval-grounded AI search delivers real value, not a generic chatbot bolted onto the site.
Natural-Language Product Search
Letting customers describe what they need in their own words and get accurate, relevant results grounded in your real catalog data, not keyword matching alone.
Policy and Support Q&A
Answering customer questions about shipping, returns, and warranty terms accurately and consistently, grounded in your actual current policies rather than an outdated FAQ page.
Internal Knowledge Assistants
Letting sales and support teams query product specs, past orders, and internal documentation in plain language instead of digging through a wiki.
B2B Catalog and Pricing Q&A
Answering complex product compatibility or specification questions that would otherwise require a human specialist, particularly valuable for technical or industrial catalogs.
Why VPC-Only Architecture Matters
Keeping this entirely within your own VPC means sensitive business information never transits to an external, third-party AI API.
Data Never Transits to a Third-Party API
Customer queries, product data, and any sensitive business information stay entirely within your own VPC, end to end.
Frequently a Compliance Requirement
For businesses handling payment-adjacent data, proprietary pricing, or B2B contract terms, that's not a nice-to-have.
You Retain Full Control
Over data retention, access logging, and exactly which internal systems the AI can and can't see.
AWS Advanced Consulting Partner
The implementation is designed and run by engineers who hold current AWS certifications, not generalists reading a dashboard.
How We Build This
A structured process from data assessment to production rollout.
Data & Use Case Assessment
We identify what data sources (catalog, documentation, policies, past support tickets) actually need to be searchable and what questions the solution needs to answer well.
OpenSearch Index & Retrieval Design
We architect the retrieval layer, indexing your data in OpenSearch with the structure and relevance tuning needed for accurate retrieval before generation happens.
Bedrock Model Selection & Grounding
We select and configure the appropriate foundation model through Bedrock, with prompting and grounding configured to keep answers strictly tied to retrieved data rather than the model's general knowledge.
VPC Security Configuration
The entire pipeline is deployed within your VPC with proper access controls, so no data leaves your environment at any point in the process.
Testing & Accuracy Validation
We test against real queries your team or customers actually ask, validating accuracy and tuning retrieval before any production rollout.
This Is Advanced Work, and We're Direct About That
This isn't a starter project, and we won't pretend it is. It's the right investment once your product data, documentation, or support volume have real complexity, and once you've validated that a simpler search or support solution genuinely isn't sufficient. If you're earlier in that journey, our OpenSearch implementation or our work on becoming the brand AI recommends are more likely the right starting point before a full RAG build.
What This Solution Delivers
The core commitments behind every private GenAI search engagement.
Frequently Asked Questions
Does customer data ever leave our AWS environment?
No. The entire architecture, retrieval through OpenSearch and generation through Bedrock, runs within your own VPC. No customer queries or product data transit to an external third-party API.
How is this different from just using ChatGPT or a similar tool on our product data?
A general-purpose AI tool doesn't have your specific, current product and policy data unless you feed it manually, and using it that way typically means your data leaves your environment. This solution retrieves directly from your own indexed data and keeps everything private, with answers grounded specifically in what's actually true for your business.
How accurate are the answers this generates?
Accuracy depends heavily on retrieval quality and grounding configuration, which is most of the engineering work in a RAG implementation. We test extensively against real queries before rollout specifically to validate this, rather than assuming a generic implementation will perform well on your data.
Is this only relevant for large enterprises?
It's most valuable for businesses with real catalog or documentation complexity, technical B2B catalogs, large support knowledge bases, complex policies, regardless of overall company size. A simple catalog is usually better served by our standard OpenSearch implementation first.
Curious Whether a Private GenAI Search Solution Fits Your Business?
We'll walk through your data, your use case, and your compliance requirements, and tell you honestly whether this is the right investment yet.
AWS Advanced Consulting Partner | VPC-Only, Data Never Leaves Your Environment