Sound familiar?
Data challenges across every growth stage
From fragmented early-stage data to compliance gaps, real-time requirements and governance debt that emerge as teams scale — the problems that hold back data-driven decisions at every stage.
Data scattered across disconnected tools
Unified data pipelines pull from every source — CRM, product, payments, marketing — into a single warehouse your whole team can query.
No single source of truth
dbt data models create a consistent, documented layer of business metrics — so every team works from the same definitions and numbers.
Manual reports eating team time
Automated dashboards and scheduled reports replace manual spreadsheet work — freeing analysts for actual analysis rather than data wrangling.
Data not structured for ML or AI
ML-ready feature stores and clean, labelled datasets that your data science team can use immediately — no data prep backlog blocking model development.
What we offer
Data engineering across the full analytics and AI stack
Six capability areas from raw ingestion to production dashboards and AI-ready infrastructure.
Data pipeline & ETL engineering
Reliable, monitored pipelines from every data source — SaaS tools, databases, event streams — into your warehouse. Custom connectors where Fivetran or Airbyte don't cover it.
- Batch & real-time streaming (Kafka, Flink)
- Pipeline monitoring & alerting
Data warehouse & lakehouse architecture
Design and implementation of Snowflake, BigQuery, Redshift or DuckDB warehouses — with cost-optimised partitioning, access controls and documentation standards.
- Schema design & data modelling
- Cost governance & query optimisation
Analytics engineering (dbt)
Business-ready data models, documented metrics layer and tested transformations that turn raw warehouse data into consistent, trusted analytical datasets.
- dbt project setup & model lineage
- Metrics store & semantic layer
BI dashboards & self-serve reporting
Production-grade dashboards in Looker, Metabase, Power BI or Tableau — built on your clean data models so reports are always accurate and self-serve for the whole team.
- Executive & operational dashboards
- Analyst training & self-serve enablement
AI-ready data infrastructure
Feature stores, embedding pipelines, vector databases and the data reliability guarantees your ML and LLM teams need — so data prep never becomes the bottleneck for AI product development.
- Feature engineering, vector stores & LLM datasets
- Training / serving data split patterns
Data quality, governance & cataloguing
Automated data quality tests, lineage documentation and a data catalogue — so your team trusts the numbers and your compliance posture (GDPR, CCPA, HIPAA) is always audit-ready.
- dbt tests & data observability
- GDPR/CCPA compliance & data catalogue
How we work
A data platform delivery approach built for startups
From audit to operationalised platform — predictable phases with clear outputs at each stage.
Audit
Map your current data landscape — sources, tools, quality and gaps — before recommending anything.
Model
Design the target data architecture — warehouse choice, data model, ingestion strategy and reporting layer — agreed before build.
Build
Pipelines, warehouse, dbt transformations and dashboards — iterative delivery with working data at every sprint.
Operationalise
Monitoring, alerting, documentation and team enablement so your platform runs reliably without us in the loop.
What you receive
A fully owned, documented data platform
No black boxes. Every pipeline, model and dashboard is documented, tested and yours to own and extend.
Data audit
- Data landscape map & source inventory
- Quality assessment & gap analysis
- Tool & stack recommendations
Data platform
- Warehouse setup & ingestion pipelines
- dbt project with documented models
- Production dashboards & alerting
Handover & enablement
- Runbooks & pipeline documentation
- Data catalogue & ownership matrix
- Analyst training & self-serve guides
Outcome-based pricing
Tell us the outcome.
We'll price to it.
Fixed base fee to deliver. Variable fee paid only if the metric lands — we carry that risk.
Outcomes we price to
Baseline and target agreed during scoping. Measured at 60–90 days post-delivery using your own data.
Technology
Modern data stack, best-fit for your scale
We work across the tools your team already uses — and will recommend the right additions for your stage, not the most expensive ones.
Warehouses
Snowflake
BigQuery
Redshift
DuckDB
Databricks
Ingestion & transformation
Airbyte
dbt Core / Cloud
Stitch
BI & visualisation
Looker / LookML
Metabase
Power BI
Tableau
Superset
Orchestration & quality
Airflow / MWAA
Prefect
Great Expectations
Monte Carlo
Engagement models
Clear pricing to fit your data maturity
Whether you need a quick data audit or a full platform build, we scope engagements clearly upfront with no surprise billing.
Fixed price
Data audit
Data landscape map, source inventory, quality assessment and tool recommendations — delivered within one week.
Time-boxed
Data platform build
6–10 weeks to a production-ready warehouse, pipelines, dbt models and dashboards. Fixed scope, fixed price.
Monthly retainer
Analytics engineering
Ongoing analytics engineering support — new models, dashboard additions, pipeline maintenance — for teams without an in-house data engineer.
Custom pipeline or off-the-shelf tool?
Run the numbers before committing to a data stack decision.
Explore more
Related services
Let's talk
Book a free 30-minute discovery call
Tell us about your data landscape, the decisions you're struggling to make, and what reliable data would change for your business.
- No obligation — just a conversation
- Scoping proposal within 48 hours
- Can start within 2 weeks of sign-off
Not ready to book? Browse the Playbook first →