// Engagement · primary
Fractional Data & AI Lead
Senior external ownership of the Data & AI picture — priorities, architecture, decisions and delivery — for companies where a full-time senior hire is not yet the right commitment.
What this is
One senior person accountable for the Data & AI direction, working inside your organisation at partial capacity.
What this is not
An advisory retainer with no delivery. A contractor filling a ticket queue. A body-shopped team.
The situation
The company has reached a new stage
01
Data & AI decisions started to matter
Reporting, integrations, forecasting or AI use cases now affect commercial outcomes. The cost of a wrong architecture decision is no longer theoretical.
02
Full-time leadership is still premature
A Head of Data or AI Lead may be too early, too expensive, or too narrow a commitment while the shape of the function is still forming.
03
Without ownership, the work fragments
Pieces sit with the founder, the CTO, engineering, analysts, product and vendors. Each piece is reasonable. The whole is not directed.
The senior layer
What Meniva owns as the fractional lead
Ownership means the decision is made, written down, defensible, and followed through into delivery. Select a domain to see what that looks like in practice.
Prioritisation
A ranked view of Data & AI work with the reasoning attached — what is worth building now, what waits for a dependency, and what should be dropped.
Working arrangement
Meniva works with the organisation you already have
This is not an engagement for companies with no data capability. Most of the value comes from directing capability that already exists — internal or external.
- Your engineersarchitecture agreed, not imposed
- Your analystsdefinitions and models owned
- Your product teamfeasibility answered early
- External vendorsscoped and reviewed
- Your leadershipone accountable counterpart
The early engagement
illustrative, not fixed
PHASE 01
Read the current state
Systems, data flows, reporting, open initiatives, vendor commitments, and who currently decides what.
PHASE 02
Name the blocked decisions
A short list of the decisions that are holding work back, with the information each one actually needs.
PHASE 03
Agree priorities and architecture
What is worth building now, what waits, what stops, and the technical shape of the next step.
PHASE 04
Start delivering, keep owning
Implementation begins with your team, with the same person still accountable for the direction.
// Evidence
The judgment is inspectable
Ownership claims are easy to make. These are the systems and the professional experience behind them, each labelled by evidence class.
Synthetic data / repository fact
PiacRadar
Decision workflows can be structured, evidenced and reviewed instead of answered in one shot.
Inspect the case study →Professional experience
Enterprise data platforms
Large-scale PySpark pipelines, governed Power BI semantic models, forecasting and experimentation — delivered in employed roles, anonymised here.
Working demo
TasteTrend, Nullfal
Retrieval systems and a learning product that can be opened and clicked into, with their limits written down.
Open the work index →Fit
- Real data, systems and AI work already exists
- Decisions are the bottleneck, not capacity
- Someone senior needs to be accountable across the whole picture
- A permanent senior hire is premature, or needs defining first
Not a fit
- Additional hands under someone else's plan
- A fixed feature backlog with the architecture already set
- An AI project chosen before the problem was defined
- Strategy documents with no implementation attached
Discuss your Data & AI setup
One conversation about what exists, what is unowned, and whether fractional ownership is the right answer now. Scope and cadence are agreed before any work starts.