We build and run production AI workloads on AWS, from data pipelines through Bedrock-powered models, so your AI project survives contact with real users and real data.
Netrix Global is an AWS Premier Tier Services Partner, engineering AI workloads that outlive the pilot.
Four things, one team: the pipelines that feed your models, the models themselves, the generative AI applications built on top, and the operations that keep it all running after everyone else has moved on.
A model is only as good as what feeds it. We build the AWS-native pipeline that gets your data into shape first.
Data lake architecture on S3 and Lake Formation
ETL and ELT pipelines built with AWS Glue
Data quality, lineage, and access governance
Migration off legacy warehouses onto AWS
SageMaker pipelines for training, tuning, and deployment, with monitoring built in from day one, not bolted on after.
SageMaker training and tuning pipelines
Feature stores and model versioning
Drift monitoring and scheduled retraining
Cost and performance tuning on inference
Generative AI applications built on Bedrock foundation models, wired into the systems your team already uses.
RAG pipelines and knowledge retrieval
Agent orchestration on Bedrock
Prompt and guardrail engineering
Integration with Microsoft 365 and internal apps
Once it's live, someone has to run it. We watch the model, the cost, and the data after everyone else moves to the next project.
Production monitoring and cost management
Incident response for AI workloads
Scheduled retraining and evaluation
Ongoing access and security review
Most AWS AI projects don't fail on the model. They fail on data access, ownership, and monitoring once the demo is over and real users show up.
WHAT WE SEE, WHAT WE DO
A team spins up SageMaker or Bedrock, gets a working demo, and leadership asks when it ships. That's usually where it stalls: no governed data feeding it, no one accountable for the model once it's live, no plan for cost or drift. We start with the AWS Premier Tier accreditation and a cross-functional team, so the same engineers who build the pipeline are still there monitoring it six months later.
4
AWS AI services we build with daily: SageMaker, Bedrock, Glue, QuickSight
Premier
AWS partner tier, audited and current
1
team: data, cloud, and AI engineers together
24/7
AI operations coverage after go-live
Five things that are true about how we work, whether or not you ask.
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Cross-functional by design. Data, cloud, and AI engineers on one team, not three vendors passing a ticket.
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We build past the demo. A model that answers questions in a slide deck is easy. Production data, real users, and real failure modes are the actual work.
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AWS Premier Tier, audited. Our accreditations are current and reviewed, not a badge from three renewals ago.
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Governance built in, not bolted on. Access controls, data lineage, and model monitoring from day one, so security isn't a retrofit once the board asks about it.
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Engineers stay after launch. AI Operations keeps watching the model, the cost, and the data after everyone else has moved to the next project.
The ones we hear most from IT leaders looking at AI on AWS.
Get an AWS AI Readiness Assessment: a straight look at your data, your AWS environment, and what it would actually take to run this in production.
Get an AWS AI Readiness AssessmentNo slide deck. Just an engineer walking through your AWS environment and telling you where it stands.