Data and AI consulting
Data and AI, from strategy to production
We build governed data platforms and put analytics and AI into production on AWS, Microsoft Azure or Google Cloud.
Point of view
ARCHR builds the data and AI systems organizations run their decisions on: engineered for production, designed to scale, and governed with security and human oversight in mind.
What we do
- 01
Data foundations
Lakehouses, pipelines, data products and a governed catalog on AWS, Microsoft Azure or Google Cloud.
- 02
AI systems
Machine learning, generative AI and agents that are evaluated, monitored and reviewed by people.
- 03
Governance and cloud
Landing zones, security, privacy and AI governance that keep platforms auditable and resilient.
Reference architecture
One governed path from data to decisions
- 01
Sources
Operational systems, files, documents, events and external data wherever they live today.
Operational databases
- AWSAmazon RDS and Aurora
- Microsoft AzureAzure SQL Database
- Google CloudCloud SQL and AlloyDB
Files and documents
- AWSAmazon S3
- Microsoft AzureAzure Blob Storage
- Google CloudGoogle Cloud Storage
SaaS and APIs
- AWSAmazon AppFlow
- Microsoft AzureAzure Logic Apps
- Google CloudGoogle Application Integration
- G
Governance and catalog
Ownership, policy, lineage, quality and access control wrap every layer inside.
Catalog and access
- AWSAWS Lake Formation
- Microsoft AzureMicrosoft Purview
- Google CloudGoogle Dataplex
Identity and keys
- AWSAWS IAM and KMS
- Microsoft AzureMicrosoft Entra ID and Key Vault
- Google CloudCloud IAM and Cloud KMS
Lineage and quality
- All threeOpenLineage, dbt tests and Great Expectations work across all three clouds.
- 02
Ingestion and streaming
Batch, change data capture and event pipelines that land data reliably and on schedule.
Streaming
- AWSAmazon Kinesis or MSK
- Microsoft AzureAzure Event Hubs
- Google CloudGoogle Pub/Sub
Batch and ELT
- AWSAWS Glue
- Microsoft AzureAzure Data Factory
- Google CloudGoogle Dataflow
Change data capture
- AWSAWS Database Migration Service
- Microsoft AzureAzure Data Factory
- Google CloudGoogle Datastream
- 03
Lakehouse and warehouse
- Raw
- Refined
- Curated
Medallion layers of raw, refined and curated data on open table formats.
Storage
- AWSAmazon S3
- Microsoft AzureAzure Data Lake Storage
- Google CloudGoogle Cloud Storage
Warehouse
- AWSAmazon Redshift
- Microsoft AzureAzure Synapse or Fabric
- Google CloudGoogle BigQuery
Open table formats
- All threeDelta Lake and Apache Iceberg run on all three clouds.
- 04
Analytics and semantic layer
Shared metric definitions feed dashboards, self-service analytics and planning.
BI and dashboards
- AWSAmazon QuickSight
- Microsoft AzurePower BI
- Google CloudLooker
Semantic layer
- All threedbt metrics or Cube sit in front of any warehouse.
- 05
ML and GenAI
Models, retrieval, agents and evaluation, with guardrails and human review.
Machine learning
- AWSAmazon SageMaker
- Microsoft AzureAzure Machine Learning
- Google CloudVertex AI
Generative AI
- AWSAmazon Bedrock
- Microsoft AzureAzure OpenAI Service
- Google CloudVertex AI
Vector search for RAG
- AWSAmazon OpenSearch Service
- Microsoft AzureAzure AI Search
- Google CloudVertex AI Vector Search
Guardrails and evaluation
- AWSAmazon Bedrock Guardrails
- Microsoft AzureAzure AI Content Safety
- Google CloudVertex AI evaluation
- 06
Applications and agents
Assistants, workflows and apps that put results in front of people, with review where it matters.
Agent frameworks
- AWSAmazon Bedrock Agents
- Microsoft AzureAzure AI Foundry
- Google CloudVertex AI Agent Builder
App hosting
- AWSAWS Lambda or ECS
- Microsoft AzureAzure Container Apps
- Google CloudGoogle Cloud Run
How we work
Four steps from idea to production
Discover
Agree goals, current state and a roadmap.
Pilot
Test one real use case with your own data.
Build
Ship secure, observable, production-grade solutions.
Evolve
Monitor, measure and extend as needs change.
Technology
Open formats. Any cloud. Governed by default.
We recommend what fits your estate across AWS, Microsoft Azure and Google Cloud, and we stay vendor-neutral.
- AWS
- Microsoft Azure
- Google Cloud
- Databricks
- Snowflake
- Microsoft Fabric
- Apache Spark
- dbt
- Power BI
- Terraform
Industries
FAQ
Frequently asked questions
Which clouds and tools do you work with?
We work on AWS, Microsoft Azure and Google Cloud, and with tools such as Databricks, Snowflake, Microsoft Fabric, Apache Spark, dbt, Power BI and Terraform. We recommend what fits the estate you already run rather than a single vendor. The technology page lists more examples.
What does ARCHR do?
ARCHR is a data and AI consulting firm. We help organizations plan, build and run data platforms, analytics and AI solutions, from strategy and data modernization to cloud, analytics, AI agents and governance.
Do you only work in financial services?
No. We work across industries, including financial services, insurance, healthcare and life sciences, retail and consumer, manufacturing, energy and resources, the public sector and not-for-profits, and professional services and technology. The same six services and the same governance-first method apply in each.
How do you approach AI governance?
We design governance, security, privacy and human oversight into data and AI systems from the start, in every industry, so decisions can be explained, reviewed and audited. We aim to support alignment with the rules that apply to you, but we do not provide legal advice.
How does an engagement start?
Most engagements begin with a conversation and a short assessment to agree goals and priorities, followed by a focused pilot to validate the approach before scaling. Use the contact page to talk to an architect.
Let's talk about your data and AI priorities
Tell us what you are trying to solve. We aim to reply within one business day.