Services for Startups
Data strategy, architecture, governance, ML, and advisory — scoped for early-stage constraints.
The same five services we offer every client, framed for the constraints of an early-stage company: limited engineering capacity, a foundation that has to be extensible rather than complete, and a budget that has to justify itself.
Most early engagements start with strategy or advisory, and expand into architecture once the sequence is clear.
Data & AI Strategy
A roadmap connecting your data assets to specific business outcomes, with sequencing and cost attached to each step.
At seed and Series A the risk is building infrastructure for a scale you have not reached. We focus on the smallest foundation that supports the next eighteen months, and name explicitly what to defer.
Typical deliverables
- Current-state assessment and capability gaps
- Prioritised initiative roadmap with effort and dependency mapping
- Business-case model for the top initiatives
Data Architecture & Engineering
Warehouse, pipeline, and platform design — and the implementation work to get it running in production.
We build the stack a small team can actually operate: managed services over bespoke infrastructure, and a design that will not need replacing the first time headcount doubles.
Typical deliverables
- Target-state architecture and migration path
- Pipeline implementation with tests and monitoring
- Runbook and handover to your team
Data Governance & Quality
Ownership models, quality controls, and metric definitions that make data trustworthy enough to make decisions on.
Lightweight by design. Enough definition and ownership that your numbers agree with each other, without a governance process that a ten-person team will quietly abandon.
Typical deliverables
- Data quality scorecard and monitoring
- Ownership and stewardship model
- Metric definitions and a maintained business glossary
ML & AI Product Development
Machine learning and AI systems taken from prototype to something that runs reliably and can be maintained.
We are candid about when a model is not yet the answer. Where it is, we build the smallest production-grade version and the evaluation harness that tells you whether it is working.
Typical deliverables
- Feasibility assessment and evaluation framework
- Production model deployment with monitoring
- Model documentation and retraining process
Executive & Team Advisory
Standing advisory support for leaders making data and AI decisions, including hiring, vendor selection, and technical due diligence.
Often the most useful engagement before you have a data team: help defining the first hire, evaluating vendors, and avoiding decisions that are expensive to unwind.
Typical deliverables
- Scheduled advisory sessions with written follow-ups
- Vendor and build-versus-buy assessments
- Role definitions and interview support for data hires
Not sure which of these you need? That is usually the first conversation.