Custom engineering
Data platforms, BI & AI-ready data
Modern data platforms
Choose the platform that fits your size and skills, and set it up so it stays governed as it grows.
What we deliver
- Snowflake, Databricks, Microsoft Fabric and Google BigQuery, or PostgreSQL for early-stage teams
- Lakehouse designs on open table formats such as Apache Iceberg and Delta Lake, so your data is not locked to one vendor
- Platform selection for companies outgrowing spreadsheets or a single application database
- Environments, access and compute set up as code
Compliance built in
- Encryption, network isolation and single sign-on configured from the start
- Access roles designed around data sensitivity, not one-off requests
- Data kept in the regions your obligations require, including the US and EU
Ingestion & change data capture
Bring data from your apps, ERP, CRM and files into one place, reliably and on time.
What we deliver
- Managed connectors such as Fivetran, Airbyte and native platform tools for SaaS applications and databases
- Change data capture (CDC) from operational databases, so updates arrive without heavy nightly loads
- Custom pipelines for APIs, files and partner feeds
- Orchestration, retries and alerts on failures
Compliance built in
- Sensitive fields tagged and masked on arrival
- Scoped, rotated credentials for every source
- Load records kept for reconciliation
Data modeling & transformation
Turn raw tables into clean, documented models that finance, operations and product teams can rely on.
What we deliver
- Layered models from raw data to reporting-ready tables, built with dbt or native tools
- Version-controlled transformations with code review and automated tests
- Shared definitions for customers, revenue, orders and headcount
Compliance built in
- Changes reviewed and released through a pipeline with approvals
- Financial reporting models reconciled to the general ledger
Data quality & lineage
Catch bad data before it reaches a dashboard or a model, and show exactly where every number came from.
What we deliver
- Tests for freshness, completeness, uniqueness and business rules
- Data observability with alerts routed to the owning team
- Column-level lineage from source system to report
- Data contracts between the teams that produce and use data
Compliance built in
- Lineage that answers auditor and regulator questions about reported figures
- Quality issues logged with owners and resolution
Data governance & privacy
Make data easy to find and safe to use, with access matched to how sensitive it is.
What we deliver
- Data catalog and classification with Databricks Unity Catalog, Microsoft Purview, Snowflake Horizon or similar
- Row- and column-level security, masking and tokenization
- Data owners, stewards and access request workflows
- Retention and deletion rules applied across the platform
Compliance built in
- Personal and health data handled in line with HIPAA and GDPR
- Access reviews and query logs kept as evidence your auditor can use
- Support for data subject requests: find, export and delete
Semantic layer & BI
One set of metric definitions behind every dashboard, so the board pack and the sales report agree.
What we deliver
- Power BI, Looker and Tableau, plus analytics embedded in your own product
- A semantic layer that defines each metric once for BI tools and AI assistants alike
- Executive, finance, operations and customer-facing dashboards
- Self-service reporting for business users, with guardrails
Compliance built in
- Row-level security so each person sees only the data they should
- Certified datasets kept separate from experiments
AI-ready data
AI is only as good as the data behind it. We prepare governed, well-described data that agents and models can use safely.
What we deliver
- Document pipelines for retrieval: parsing, chunking and embeddings
- Vector search in the platform you already run (Snowflake, Databricks, BigQuery, PostgreSQL with pgvector) or a dedicated vector database
- Feature pipelines and training datasets for machine learning
- Metadata and metric definitions that help AI assistants query data correctly
Compliance built in
- Source permissions carried into indexes and embeddings
- Sensitive data kept out of training sets and prompts unless approved
- Dataset versions recorded for each model
Real-time streaming
Act on events as they happen, whether payments, orders, sensor readings or patient updates, instead of waiting for tonight's batch.
What we deliver
- Apache Kafka, Confluent, Amazon Kinesis, Azure Event Hubs and Google Cloud Pub/Sub
- Stream processing and real-time tables for operational dashboards and alerts
- Event-driven integrations between applications
Compliance built in
- Encryption, schema validation and access control on every stream
- Retention and replay settings aligned with your obligations
How we engage
Outcomes
Numbers people agree on
One set of definitions behind every report, reconciled to the systems of record.
Answers for auditors
Lineage, access logs and quality checks show where each figure came from and who could see it.
Data ready for AI
Governed, documented data that agents and models can use without exposing what they shouldn't.
A platform that grows with you
Open formats and infrastructure as code, from a first warehouse to an enterprise lakehouse.
Typical deliverables
- Current-state data map and a platform recommendation
- Target architecture on Snowflake, Databricks, Microsoft Fabric or BigQuery
- Ingestion pipelines and documented data models
- Data quality tests, lineage and alerting
- Catalog, classification and access design
- Semantic layer and priority dashboards
- Retrieval and vector search pipelines for AI use cases
- Runbooks and a data ownership model
Engagement model
- Assess
Map what you have
A fixed-scope review of your data sources, platform, reporting and controls. Discovery typically takes 1–2 weeks and ends with an architecture and a phased plan.
- Build
Deliver in phased releases
A first working release, typically a governed platform with priority dashboards, within 6–10 weeks, then further sources and use cases in phased releases.
- Run
Keep data trustworthy
Ongoing monitoring of pipelines and quality, cost reviews, access reviews and new reports as needs change.
Example use cases
- A startup setting up its first warehouse and investor metrics from Stripe, HubSpot and its product database
- A mid-sized business replacing spreadsheet reporting with Power BI on Microsoft Fabric
- A scale-up adding customer-facing analytics to its product, with row-level security
- A hospital group building patient-flow dashboards with HIPAA-aligned access controls
- An enterprise consolidating legacy warehouses onto Databricks with Apache Iceberg tables
- A financial institution preparing governed document and transaction data for AI agents
Insights
Related insights
Want numbers everyone trusts?
Tell us where your data lives and what you need from it. We'll suggest a sensible first step, typically a short assessment.