Data platform development services for businesses worldwide

When your data product requirements, compliance posture or cost-at-scale outgrow what the point-tool cohort will bend to. For a lot of teams, Fivetran + Snowflake + dbt + Looker is the right answer. Custom takes over when you need bespoke sources, tight data residency, analytics applications beyond dashboards, or when the per-row and per-seat meter starts to dominate the budget.

Foundational platforms (warehouse, core pipelines, first analytics layer) typically ship in one to two quarters. Data products and analytics applications on top extend from there. Discovery is where we commit to a timeline — before the source audit, any number is guesswork.

Usually yes. Most engagements build on whatever warehouse you already invested in rather than insisting on a migration. We'll only raise a warehouse swap as an option if the current one is a genuine bottleneck on scope or cost.

AWS, Azure and GCP are all on the table. Most of our platform work has run on AWS and GCP; Azure is fine where your organisation has standardised on it. Multi-cloud is possible but usually costs more than it saves — we only recommend it when data residency or resilience requirements genuinely demand it.

We pick the orchestrator around the workload. Airflow, Dagster and Prefect for general batch and event-driven work; native cloud schedulers for simpler cadence-based jobs; streaming tools (Kafka, Kinesis, Pub/Sub) where the source is event-based. The right answer depends on how your team wants to operate it after handover.

Yes — where it earns its place. Forecasting, anomaly detection, classification and document extraction are the recurring shapes. We push back on ML for its own sake; models only go in when the business case survives a simple baseline.

SaaS stacks front-load low on build and back-load high on subscriptions; custom platforms front-load the build and flatten. Break-even depends on data volume, user count, connector count, and how long you plan to run the platform. We can model it against your actual numbers during discovery.

The source, the data model, the orchestration configuration, the runbooks and the documentation. Infrastructure as code means the whole stack can be rebuilt from the repository — no hidden state, no lost knowledge if the original team moves on.

Yes. These engagements usually start with an audit of the current state — sources, transformations, lineage, cost — before committing to forward scope. Sometimes the right answer is to stabilise; sometimes it's to refactor a specific layer; sometimes it's to migrate off. The audit tells us which.