AWS MLOps and LLMOps
The production layer behind LLM applications on AWS — Bedrock integration, RAG grounded in your data, agents and tool use, evals, guardrails, prompt logging, and the model routing, caching, and cost controls that keep AI systems honest and affordable once they're live.
Principles that drive the engineering
Six rules we hold for AI application infrastructure. They're why we can ship production-grade AI features without handing your customer data to a third party or shipping a guardrail-free feature that Legal has to pull.
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Your team owns modeling, we own the infra
Data scientists and ML engineers do what they do best. We build the SageMaker platform, the Bedrock integration, and the production wiring underneath — so their models actually ship.
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Prompts are code, not configuration
Prompts live in version control, get reviewed in pull requests, and move through environments like any other code artifact. Changes are tracked; regressions are catchable.
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Guardrails before features
Content filtering, PII redaction, jailbreak defenses, and cost caps wired in before the first user sees the feature. Legal doesn't hold the release; guardrails ship it.
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Retrieval quality beats model choice
A cheaper model with the right context beats an expensive model with bad retrieval. We invest in the RAG layer and the evaluation harnesses, not in chasing the latest foundation model.
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AI observability is its own discipline
Quality, latency, cost, drift, and safety — tracked separately, alerted on separately. AI systems fail in ways that standard application monitoring doesn't catch.
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Ship AI features behind flags
Every AI feature rolls out to a cohort, measured against a baseline, rolled back if quality regresses. The first incident should look like a flag flip, not a hotfix.
AWS MLOps and LLMOps, end to end
Four modes shape every AI application we put in production: integrate, retrieve, guard, observe. Together they turn a model into a feature customers can use.
Bedrock integration, agents & tool use
Foundation-model integration, streaming, tool use, and agent patterns behind guardrails, prompt logging, and cost caps — with fallbacks, retries, and a paper trail your auditors can follow.
Retrieval-augmented generation
RAG pipelines against your data — OpenSearch, Kendra, or vector stores — with evaluation harnesses instead of "looked good in the demo."
Guardrails and safety
Bedrock Guardrails, content filters, PII redaction, jailbreak defenses, and policy enforcement — so AI features can ship without legal holding the release.
Feature pipelines to the feature store
The ingestion and transformation jobs that feed your feature store — the data engineering under the model. Your team defines features; we deliver them reliably.
SageMaker platform for your models
SageMaker endpoints, auto-scaling, IAM, networking, and release tooling your data-science team deploys onto. We run the platform; your team owns the modeling.
Observability & cost optimization
Quality, latency, cost, and drift signals wired into CloudWatch and your on-call — plus model routing and caching so the bill scales with value, not just usage. When an AI feature regresses, an engineer finds out before a customer does.
What we reach for, and why
Bedrock is our default for LLM access — multiple model vendors behind one API, with guardrails native. Direct API to a model vendor only where the customer's data-handling contract already covers it.
OpenSearch for general-purpose vector + keyword; Kendra where managed semantic search fits the use case. Evaluation harnesses run against both.
Bedrock Guardrails as the native default — content filters, PII redaction, topic-denial, jailbreak defenses. Policies in code, reviewed like any other safety surface.
SageMaker as the AWS-native platform where your data-science team deploys models — we operate the infra, they own the modeling. Lambda + Step Functions for orchestration around it.
S3 and ECR for model and feature artifacts. Feature pipelines built on our data-engineering stack — dbt, Glue, Kinesis — feeding the feature store reliably.
CloudWatch for infra-level; custom quality, cost, and drift metrics on top. Prompt logs and evaluation results retained for audit and regression-testing.
The way a project actually runs
From AI use-case scoping to production-grade feature in four phases, each anchored to a measurable quality bar before it ships.
Scope the use case
What's the AI feature, who uses it, what's the quality bar, and what does failure look like? Define guardrails, evaluation criteria, and cost ceilings up front.
Integrate & retrieve
Wire up Bedrock (or your chosen model). Build the RAG layer against your data. Prompts in version control, evaluation harnesses in CI.
Guard & evaluate
Guardrails wired in. Evaluation suites run per deploy. Cost caps, rate limits, and rollback paths validated before the feature reaches real users.
Operate & optimize
Quality, cost, and drift tracked continuously. Feature flags control rollout. Evaluation regressions produce a durable fix, not a permanent workaround.
Seen in production
Case studies coming soon.
Part of these solutions
MLOps and LLMOps are the engine of AI Enablement — they deliver the Application Delivery and Optimization & Governance pillars — and they stand alone for teams adding AI features to existing products without a broader engagement.
Ready to put AI into production safely?
Tell us what AI features you need in production. We'll scope the Bedrock integration, the RAG layer, the guardrails, and the observability — so the AI work actually ships and stays honest.
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