Get your AI application into production on AWS — and put it to work.
Most AI prototypes never ship. Getting an LLM application to production on AWS takes four layers — a Bedrock landing zone, retrieval grounded in your data, the application engineering, and the cost and governance discipline to run it. That's what AI Enablement delivers, built into your stack.
Where AI applications get stuck on the way to production
A working prototype is the start, not the finish. We build the layers that get it to production on AWS — and keep it reliable, safe, and affordable.
Your prototype works — but it won't pass a production review
The demo is convincing. Then security asks where the model runs, how data is scoped, what the guardrails are, and where the logs go — and there's no environment with answers. We stand up the AI Landing Zone: Bedrock access over PrivateLink, IAM scoping, guardrails, logging, and cost controls — so the prototype has somewhere safe to ship.
An AWS landing zone built for AI workloads
The model answers confidently — and wrong
It retrieved the right document on demo data and misses on real customer data. The gap is grounding: ingestion, a current vector store, governance, and evals that catch regressions before users do. We build the retrieval layer your LLM grounds on — current, governed, and tested.
Reports from months to hours — New York Blood Center
Your Bedrock bill climbs and nobody can explain it
Usage grew, cost grew faster, and there's no model routing, no caching, and no view of what's driving spend. We wire in cost engineering, model routing, caching, and guardrails tuning — so the bill scales with the value the application delivers, not just with traffic.
Cost that scales with value, not just usage
From prototype to production, in four layers
AI Enablement is the umbrella. Each pillar is delivered by the Polymath engineers who do that work — and packaged as a way to start.
The AI Landing Zone
The opinionated AWS base every AI workload runs on — Bedrock access over PrivateLink, IAM scoping, guardrails, logging, and cost controls. Delivered by our Cloud and DevOps engineers.
Explore AI Landing Zone →Retrieval grounded in your data
Ingestion, vector stores, RAG, freshness, lineage, and governance — so the model grounds on current, trustworthy data, not demo data. Our data engineering, evolved for LLMs.
Explore RAG-in-a-box →The application around the model
Streaming, tool use, agents, evals, observability, and CI/CD — the engineering that turns a model into an application your customers can rely on. Delivered by our MLOps and software engineers.
Explore LLM App Modernization →Affordable and compliant in production
Cost engineering, model routing, caching, guardrails tuning, and audit — so the application stays affordable and compliant once it's live. Start with a free AI Cost Audit.
Get a free AI Cost Audit →Proof in production
3–6 months → hours
Reporting turnaround at New York Blood Center
150M
Files migrated in one week for an AI startup — 50% faster than expected
5-Domain
AI Data Readiness Framework — our data audit methodology
Live
Our own production AI agent pipeline, running on AWS
150 Million Files Moved in One Week — 50% Faster Than the Customer Expected
A voice AI startup had built its infrastructure on Azure — and was hitting the limits. They needed better GPU capacity for their ML models, higher reliability, stronger file-security posture, and a clean migration of 150 million files without disrupting their customers. We designed the AWS architecture and completed the full migration in twelve weeks — including moving every one of those 150 million files inside a single week.
Read the full storyPolymaths delivered complex modules across business domains, collaborated with our internal IT teams, implemented Agile and DevOps processes - all with a lean budget.
Michele Scaggiante
VP and CIO, New York Blood CenterHow we get AI into production
Stand up the foundation
The AI Landing Zone — Bedrock over PrivateLink, IAM scoping, guardrails, logging, and cost controls. The opinionated base your AI workloads run on, built as code and yours to keep.
Ground it in your data
Our AI Data Readiness Framework audits the data first — then ingestion, vector stores, and RAG against your governed data, with freshness, lineage, and evals so retrieval holds up on real customer data, not just the demo set.
Ship the application
Streaming, tool use, agents, evals, observability, and CI/CD. The engineering between a model that works in a notebook and an application your customers can rely on.
Run it well
Cost engineering, model routing, caching, guardrails tuning, and audit — so it stays affordable and compliant. Handed over as a platform your team can run, or run by us.
Book an AI Production Readiness Review
Tell us where your AI is stuck — the prototype that won't pass a production review, the retrieval that misses on real data, the Bedrock bill nobody can explain. We'll map the gaps across the four pillars and come back with a plan to get to production on AWS.
Book a ReviewRelated capabilities
MLOps / LLMOps
Bedrock integration, RAG, agents and evals, guardrails, and the cost, routing, and observability discipline behind LLM applications in production.
Data Engineering
Ingestion, warehousing, pipelines, vector stores, and governance on AWS — the data and retrieval layer analytics and AI both run on.
Cloud Engineering
Landing zones — including the AI Landing Zone for Bedrock workloads — IaC, DMS database migration, release engineering, and cost governance. The AWS discipline under every migration, modernization, and AI build.
Software Engineering
Full-stack cloud-native software on AWS — web apps, APIs, microservices, IoT backends, SDKs. Serverless and container services, typed end to end.
Frequently Asked Questions
Totally fine. We work with clients at every stage—from legacy migrations to full AI integrations. If you’re still moving to the cloud or need to clean up your data first, we’ll help you get there.
No. Our team handles the technical lift—so you don’t need in-house specialists to start seeing value from your data. You bring the goals, we’ll bring the execution.
Yes. Security is a core part of how we work.
- We only work within your environments—we never move or store your data outside your cloud account.
- We follow AWS Well-Architected security best practices and industry standards to ensure encryption, access control, and auditing are properly implemented.
- If you have compliance requirements (like HIPAA, SOC 2, or ISO 27001), we’ll align our approach to meet them—from IAM roles to logging and monitoring.
Our team has deep experience designing secure, auditable, cloud-native architectures that meet both technical and regulatory expectations.
Yes. We focus exclusively on AWS. We’re deeply experienced with AWS’s AI/ML services and data stack, and we help teams migrate to, build on, and optimize for the AWS cloud. If you’re currently on another provider, we can help you plan and execute a smooth move to AWS.
Have a question we didn’t answer? Send us a message, and we’ll get back to you promptly!
Let's get your AI to production.
Tell us where your AI is stuck today. We'll map the gaps across the four pillars, scope the work, and come back with a plan to get to production on AWS — with us or not.