Cloud AI data pipelines that ship.
AWS Bedrock for managed AI inference. AWS Glue for governed ETL. Azure AI for multi-model orchestration. We design the data pipeline, deploy the AI layer, and hand you the keys — in your VPC, on your cloud, with full IP transfer.
3
Cloud platforms engineered (AWS, Azure, GCP)
26%
AWS cost reduction on a $53k/month fintech bill
20%
Month-over-month cost optimisation on healthcare AI
AI models are commodity. The pipeline is the product.
Every cloud provider now offers managed foundation models. AWS Bedrock gives you Claude, Titan, and Llama behind an API. Azure OpenAI Service gives you GPT-4o and embeddings. The model is no longer the bottleneck.
The bottleneck is the pipeline: how data gets from your source systems into a governed lake, how it reaches the model with the right context, how the model's output routes back into your workflow, and how you monitor cost, latency, and accuracy in production. That pipeline is what separates a demo from a deployed system.
Most teams stall at the proof-of-concept stage — a notebook calling an API — because the engineering required to move from notebook to production pipeline is a different discipline entirely. That is the gap we close.
Six-layer cloud AI pipeline
Every pipeline we deploy follows this reference architecture. Components swap between AWS and Azure based on client cloud commitment — the pattern stays the same.
Data ingestion — AWS Glue & Azure Data Factory
Raw data lands in S3 or Azure Blob Storage. AWS Glue crawlers auto-discover schema; Glue ETL jobs (PySpark or Python shell) cleanse, deduplicate, and partition data into Iceberg tables. On Azure, Data Factory orchestrates equivalent pipelines with Mapping Data Flows. Both paths produce governed, catalogued datasets ready for AI consumption.
AI inference — AWS Bedrock & Azure OpenAI Service
Foundation models (Claude, Titan, GPT-4o, Llama) run through managed APIs with no infrastructure to provision. Bedrock Knowledge Bases ground responses in enterprise data via automatic RAG. Azure AI Studio provides the same pattern with Azure OpenAI Service and Azure AI Search. Model selection is per-use-case: latency-sensitive extraction runs smaller models; complex reasoning runs frontier models.
Orchestration — Step Functions & Azure Durable Functions
Multi-step AI workflows — ingest, enrich, classify, review — are orchestrated as state machines. AWS Step Functions coordinate Glue jobs, Lambda functions, and Bedrock calls. Azure Durable Functions handle equivalent fan-out/fan-in patterns. Both provide built-in retry, error handling, and observability without custom queue management.
Vector storage & retrieval — OpenSearch & Azure AI Search
Document embeddings are stored in Amazon OpenSearch Serverless (vector engine) or Azure AI Search with vector indexing. Hybrid search combines keyword BM25 with semantic kNN retrieval. The same index serves both traditional search and RAG grounding, so the knowledge base stays unified.
MLOps & monitoring — SageMaker & Azure ML
Custom models train on SageMaker with managed infrastructure and automatic hyperparameter tuning. Azure Machine Learning handles the same lifecycle with registered models, managed endpoints, and A/B deployment. Both platforms feed CloudWatch / Azure Monitor dashboards tracking inference latency, token usage, error rates, and cost per request.
Governance & security — IAM, VPC, and compliance
All AI workloads run inside the client's VPC or Virtual Network. IAM policies enforce least-privilege access to models, data, and endpoints. Data never leaves the cloud boundary. PII detection runs in the pipeline (Comprehend / Azure AI Language) so sensitive data is masked or routed before it reaches a model. Audit trails log every inference call for compliance.
AWS vs Azure: same pattern, different services
| Layer | AWS | Azure |
|---|---|---|
| AI Inference | Bedrock | Azure OpenAI Service |
| ETL / Data Prep | Glue + Glue Crawlers | Data Factory + Synapse |
| Vector Search | OpenSearch Serverless | Azure AI Search |
| ML Platform | SageMaker | Azure Machine Learning |
| Orchestration | Step Functions | Durable Functions |
| RAG Grounding | Bedrock Knowledge Bases | Azure AI Studio + AI Search |
| PII Detection | Comprehend | Azure AI Language |
| Monitoring | CloudWatch | Azure Monitor |
We manage enterprise AWS bills. We also cut them.
Building the pipeline is half the engagement. Keeping the bill under control is the other half. Two real examples from production infrastructure we manage:
RYVYL · NASDAQ: RVYL
26% reduction on a NASDAQ-listed fintech's monthly AWS bill. Right-sizing, Reserved Instances, storage tiering, and orphaned resource cleanup. $168k annualised savings.
Healify.ai · Healthcare AI
Month-over-month cost reduction on a $15-18k/month healthcare AI platform. Active cost monitoring, compute optimization, and pipeline efficiency improvements.
Cloud AI infrastructure for every business size
The same reference architecture scales from a one-week API integration to a multi-cloud enterprise platform. We scope to your budget, your cloud, and your timeline.
AWS Bedrock + Glue + SageMaker · Azure OpenAI + Data Factory + ML
Multi-cloud AI platform with custom models, governed data lake, real-time inference pipelines, and dedicated MLOps team. Full IP transfer, quarterly roadmap cadence.
Learn more →AWS Bedrock + Glue · Azure OpenAI + AI Search
Production AI pipeline in 6 weeks: managed models, ETL orchestration, vector search, and monitoring dashboard. Fixed scope, fixed price.
Learn more →AWS Bedrock Knowledge Bases · Azure AI Studio
Pre-built RAG pipeline grounded in your documents. Managed deployment, no model training required. Internal knowledge assistant or customer-facing Q&A in 3 weeks.
Learn more →AWS Bedrock API · Azure OpenAI API
AI integration sprint: connect your existing application to managed AI models via API. Prompt engineering, guardrails, and deployment in 1 week.
Learn more →Cloud AI infrastructure in production
Every client engagement runs on this pipeline architecture. The implementation details change; the pattern does not.
Sports Analytics
PACE Racing Analytics
Per-stride GPS pipeline on AWS: RabbitMQ ingestion, PostgreSQL tiered storage, AI sectional analysis.
Fintech
RYVYL Payments
Real-time payment infrastructure processing $500M+ annually on cloud-native architecture.
Healthcare
CaptureProof
HIPAA-compliant visual health platform with computer vision AI on cloud infrastructure.
Start building
Ready to move from notebook to production pipeline?
Whether you need a one-week API integration or a multi-cloud enterprise AI platform, we scope to your cloud, your budget, and your timeline. Fixed scope, fixed price.