Services
Data and AI services, from strategy to production
Six connected services that take an organization in any industry from a clear roadmap to governed, production-grade data and AI, on AWS, Microsoft Azure and Google Cloud.
What we do
End-to-end data and AI services
Each service can stand alone or combine with the others. Most engagements start with strategy and a focused pilot, then add platform, analytics, AI and governance work as priorities become clear.
Strategy & Design
A current-state assessment, target architecture, operating model, business case and roadmap, plus a focused pilot that can show value quickly.
Learn moreData Modernization
Data warehouse, data lake and lakehouse platforms, pipelines, streaming, master data, a metadata catalog and data products across structured and unstructured data.
Learn moreCloud
Landing zones, identity, infrastructure as code, deployment pipelines, containers, observability and cost control on AWS, Microsoft Azure and Google Cloud.
Learn moreAnalytics & Reporting
Business intelligence, self-service analytics, a governed semantic layer, regulatory reporting and forecasting, built around how people decide.
Learn moreAI, Agents & Machine Learning
Machine learning, machine learning operations, generative AI, retrieval-augmented generation and agentic workflows, with evaluation, guardrails and people in the loop.
Learn moreGovernance, Security & Privacy
A governance operating model, access control, privacy by design, audit evidence and AI governance, enforced in tooling where possible.
Learn moreWhich service do you need?
Organizations usually arrive with a problem rather than a service name. The table below maps common situations to a sensible starting point. Most situations involve more than one service, and we are happy to say plainly when a smaller step is enough.
| If this describes your situation | Start with |
|---|---|
| We have many data and AI ideas and no agreed priorities, owners or budget. | Strategy & Design |
| Reports disagree, data is trapped in separate systems, or legacy platforms are expensive to run. | Data Modernization |
| Environments are built by hand, cloud costs are rising, or we are planning a move to AWS, Microsoft Azure or Google Cloud. | Cloud |
| Leaders cannot get trusted numbers, and business users cannot explore data without help. | Analytics & Reporting |
| Pilots never reach production, or we want to adopt generative AI or agents with proper evaluation. | AI, Agents & Machine Learning |
| We need clear data ownership, access control, privacy practice or an approval path for AI. | Governance, Security & Privacy |
| A regulator, auditor, board or client has asked how we use data and AI. | Governance, Security & Privacy, often with Strategy & Design |
| We are merging systems or consolidating customer and product records. | Data Modernization, with master data management |
How engagements are structured
Start small, validate, then scale
We follow a four-step method: discovery and strategy, pilot and validation, build and scale, and optimize and evolve. Scope, owners and success measures are agreed at each step, so you can see results before committing to the next one.
- Fixed-scope discovery and pilot phases that produce something concrete
- Delivery shaped around your stack, budget and in-house team
- Governance, security and human oversight designed in, not added at the end
- Handover and documentation so your team can run what we build
Technologies
Tools we commonly work with
We deliver on AWS, Microsoft Azure and Google Cloud and recommend what fits your estate, often what you already own. These are examples of technologies we commonly work with.
Cloud
The three major clouds we design and build on, shaped around the estate you already run.
- AWSAmazon Web Services: broad infrastructure, data and AI services.
- Microsoft AzureMicrosoft's cloud, closely tied to Microsoft 365, Fabric and Entra ID.
- Google CloudGoogle's cloud, known for BigQuery, Dataplex and Vertex AI.
- Cloud object storageAmazon S3, Azure Data Lake Storage and Google Cloud Storage as lake foundations.
Data platforms
Warehouses, lakes and lakehouses where governed data is stored, modeled and shared.
- DatabricksLakehouse platform for data engineering, SQL analytics and machine learning.
- SnowflakeCloud data platform for warehousing, sharing and analytics.
- Microsoft FabricUnified analytics platform with OneLake, pipelines and Power BI.
- Google BigQueryServerless data warehouse with built-in machine learning.
- Amazon RedshiftCloud data warehouse in the AWS ecosystem.
- Azure Synapse AnalyticsAnalytics service combining warehousing and big data on Azure.
- Delta LakeOpen table format that adds reliability to data lake storage.
- Apache IcebergOpen table format for large analytic tables across engines.
Data engineering
Pipelines that move, transform and orchestrate data in batch, as change data capture and as streams.
- Apache SparkDistributed engine for large-scale batch and stream processing.
- dbtSQL-based transformation, testing and documentation for analytics models.
- Apache AirflowWorkflow orchestration for scheduled and dependent data jobs.
- Apache KafkaDistributed event streaming platform for real-time data.
- Azure Data FactoryManaged integration service for ELT/ETL pipelines on Azure.
- AWS GlueServerless data integration and catalog service on AWS.
- Google DataflowManaged batch and streaming processing based on Apache Beam.
- FivetranManaged connectors that load source data into a warehouse.
- DebeziumOpen source change data capture for operational databases.
- Apache FlinkStateful stream processing for low-latency event pipelines.
Analytics and BI
Dashboards, self-service analytics and a shared semantic layer so teams use the same definitions.
- Power BIMicrosoft's BI and dashboarding platform with semantic models.
- TableauVisual analytics and dashboards for exploration and reporting.
- LookerGoverned BI built on a modeled semantic layer.
- Amazon QuickSightServerless BI and embedded analytics on AWS.
- Apache SupersetOpen source data exploration and dashboards.
- CubeHeadless semantic layer that serves consistent metrics to any tool.
AI and GenAI
Machine learning, generative AI and agents, built with evaluation, guardrails and human review.
- Azure OpenAI ServiceLarge language models delivered through Azure with enterprise controls.
- Amazon BedrockManaged access to foundation models, agents and guardrails on AWS.
- Google Vertex AIGoogle Cloud platform for machine learning and generative AI.
- Amazon SageMakerManaged service to build, train and deploy machine learning models.
- Azure Machine LearningAzure service for training, deploying and monitoring models.
- Hugging FaceOpen models, datasets and libraries for machine learning.
- LangChainFramework for building retrieval and agent workflows.
- MLflowExperiment tracking and model registry for MLOps.
- Vector databasesEmbedding stores such as pgvector, Azure AI Search and OpenSearch for retrieval.
Governance and security
Catalogs, lineage, access control and key management that make data and AI auditable.
- Microsoft PurviewData catalog, classification and governance across Microsoft estates.
- Unity CatalogUnified governance for data and AI assets on Databricks.
- CollibraData governance and catalog platform for policies and stewardship.
- AWS Lake FormationFine-grained access control for data lakes on AWS.
- Google DataplexData management and governance across Google Cloud data.
- Azure Key VaultManaged storage for secrets, keys and certificates on Azure.
- AWS KMSManaged encryption keys for services and applications on AWS.
- Microsoft Entra IDIdentity and access management for people and workloads.
- OpenLineageOpen standard for collecting data lineage across tools.
- Great ExpectationsOpen source framework for data quality checks.
DevOps and IaC
Infrastructure as code, CI/CD and observability so platforms are repeatable, reviewed and resilient.
- TerraformInfrastructure as code that works across all three clouds.
- BicepAzure-native language for declarative infrastructure.
- AWS CloudFormationAWS-native templates for repeatable infrastructure.
- GitHub ActionsCI/CD workflows that live next to the code.
- Azure DevOpsBoards, repositories and pipelines for delivery teams.
- KubernetesContainer orchestration for portable, scalable workloads.
- DockerContainer packaging for consistent builds and deployments.
- OpenTelemetryOpen standard for traces, metrics and logs.
These tools are examples of what we commonly work with, listed as text only. We are vendor-neutral and recommend what fits your estate. Product names belong to their owners, and their appearance here does not imply a partnership, endorsement or certification.
The same services, across industries
The six services are the same in every sector. What changes is the data, the rules that apply and the questions that matter most. We work with financial services as well as insurance, healthcare and life sciences, retail and consumer, manufacturing, energy and mining, the public sector, and professional services and technology. Browse the industries to see how we approach each.
Not sure where to start?
Tell us about the problem and the systems involved. We will suggest a sensible first step, which is often a short discovery session. You can reach us through the contact form.
This page is general information about ARCHR services. It is not legal, financial or investment advice, and it does not assure compliance with any regulation.
FAQ
Frequently asked questions
Can we buy one service on its own?
Yes. Each service can stand alone, and many clients start with a single assessment or pilot. The services are designed to connect, so adding another later does not mean starting over.
Do you work on all three major clouds?
Yes. We deliver on AWS, Microsoft Azure and Google Cloud, and we shape each design around your existing stack, skills, contracts and budget.
Do you replace our team?
No. We work alongside your people and plan for handover, so your team can run and extend what we build.
How do you decide where to start?
We start from the problem and the systems involved. Often the right first step is a short discovery session or a focused pilot, and the table above shows the usual entry points.
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.