Fractional Data & AI leadership
Your company has real data, systems and AI work, but no single senior owner across priorities, architecture and delivery. Meniva helps decide what is worth building — and can build it with you.
Hire the capability before you build the function.
The problem is rarely missing technical hands. It is missing senior ownership of the whole picture.
€1.85B+
Revenue-scale analytics experience
Professional experience74k+
Records processed in AI demo systems
Working demo~€850k
Revenue uplift modelled in scoring workflow
Modelled40+ hrs/yr
Manual work automated in internal process
Internal systemMetrics include anonymised professional experience, internal systems, and modelled demo scenarios. No figure describes a realised Meniva client result.
Recognition
Four situations, not four services. If more than one is recognisable, the conversation is usually about ownership.
Data & AI work is distributed across several people, but nobody owns the full picture.
Founder / CEOThe CTO is carrying Data & AI decisions alongside engineering.
CTOAnalysts or engineers exist, but senior architecture and prioritisation are missing.
Data teamAI initiatives exist, but nobody can confidently decide what is worth building.
Product / OpsThe ownership layer
Select a role to see what it carries today, and what tends to fall between roles.
Fragmented ownership today
CTO
Owns today
Data & AI decisions, carried alongside the engineering roadmap.
What falls through
Architecture choices get made under delivery pressure, then inherited.
Senior Data & AI ownership
One senior layer above the roles, accountable for the decisions between them.
Ways to work together
Not a pricing ladder. Fractional lead → prioritise → build → stay involved · defined problem → sprint → implementation → handover · sprint → handover.
Primary01
01
Continuing senior ownership of the Data & AI picture, at a fraction of a full-time leadership commitment.
See the engagement →When it is useful
Data & AI decisions are becoming consequential, but a full-time senior hire is premature.
What Meniva owns
Priorities, architecture, build-vs-buy decisions, delivery oversight, and hands-on work where it helps.
What to expect
A Data & AI direction that is decided, documented and moving — with one senior person accountable for it.
Bounded02
02
A bounded engagement around one concrete business or technical problem.
See the engagement →When it is useful
A specific problem needs to be clarified, scoped and moved forward without opening a consulting programme.
What Meniva owns
Problem framing, architecture, prototyping and validation for one bounded question.
What to expect
A decision you can act on, with the technical work that supports it — or a clear reason not to build.
Delivery03
03
Larger hands-on delivery once the right problem and direction are understood.
See inspectable work →When it is useful
The direction is agreed and the system needs to be built, integrated and handed over.
What Meniva owns
Analytics systems, data platforms, BI, automation, forecasting and ML, applied AI, RAG and AI workflows.
What to expect
A system in real use, with documentation, and a team that can operate it without Meniva.
How Meniva works
01
Which business and technical decisions are currently blocked, and who is carrying them.
02
What is worth building now, what waits, and what should not be built at all.
03
Architecture plus hands-on implementation, with your engineers and analysts involved.
04
Documentation, handover and self-sufficiency — or ongoing fractional leadership where useful.
Not every AI idea should become an AI project. Step 02 exists to say so.
// Selected work
Inspectable systems with the evidence boundary stated. Each one is labelled by what it proves, and what it does not.
SEC 01 / PIACRADAR / SYNTHETIC DATA
Hackathon market-intelligence workflow
LLM market-intelligence workflow. One question becomes hypotheses, evidence, and an action plan.
What this demonstrates
One strategic question can be turned into a reviewable decision package: hypotheses, evidence references, actions and validation tasks.
When this pattern is useful
Executive questions are answered ad-hoc and the reasoning behind the answer cannot be reviewed.
SEC 02 / TASTETREND / DEMO AVAILABLE
Restaurant review intelligence
AI RAG over restaurant reviews. Ask a question, inspect the evidence, open the demo.
What this demonstrates
Retrieval-augmented answers over unstructured text, with the retrieved evidence kept inspectable next to the answer.
When this pattern is useful
A team needs answers out of documents or reviews and cannot accept unsourced output.
SEC 03 / NULLFAL / DEMO AVAILABLE
Technical learning product demo
AI learning product with practice tracks, RAG explanations, progress events, and a live demo.
What this demonstrates
A structured product built on top of retrieval and event tracking, rather than a chat interface over a model.
When this pattern is useful
Internal knowledge or enablement needs structure, progress and explanation that can be audited.
Lab 01 · prototype
Agentic prospecting workflow. Research leads, keep evidence, score fit, export to CRM.
Lab 02 · under development
ML-ready lead scoring workspace for enrichment, routing, review, and RevOps automation.
Lab 03 · R&D
Local RAG for AI research. Search papers, rerank evidence, synthesize source-backed answers.
// Professional systems experience
Alongside Meniva-built demos, Bálint has delivered data and decision systems in employed roles across retail analytics, large-scale data engineering, BI, forecasting, experimentation, and statistical-data modernisation. Examples are anonymised and separated from Meniva IP; no client code, data, names, or confidential results are reproduced here.
EXP 01
Decision workflows over commercial and market data.
EXP 02
Reliable transformation and quality controls at enterprise scale.
EXP 03
Governed semantic models and operational dashboards.
EXP 04
Reproducible models, evaluation, and decision framing.
EXP 05
Schema, quality, lineage, and publishing workflows.
Operating model
Meniva is run by Bálint. The person framing the business problem is the person making the architecture decisions and, where useful, writing the implementation. Nothing is delegated down a bench.
5+Years in Data & AI
20+Projects delivered
Professional references
Professional references. Not Meniva client testimonials, and not descriptions of Meniva project outcomes.
“You'll absolutely love working with Bálint because he is always ready to transform data into fully accessible and actionable insights. When I worked with him on a project, I was impressed by his professional skills and attention to detail.”
“I recommend Bálint for his exceptional data analysis skills. He consistently delivers quality analysis, demonstrating thoroughness and attention to detail. His commitment to continuous improvement makes him a valuable contributor to any team.”
“Bálint was extremely helpful and flexible from the very first moment. I could reach him anytime, he responded quickly, and always suggested up-to-date solutions. What I especially appreciated was that he didn't leave me on my own after the website was finished, but continued to support me in using it.”
Insights
The professional writing and research layer of the Meniva ecosystem: analysis of AI, data work, technology organisations and the changing infrastructure of knowledge work.
AI Operating Model
Why buying AI licenses is not the same as redesigning how work, data and responsibility flow through an organization.
Read on CtrlPlane →Engineering Systems
Agentic coding makes code cheaper to produce, but validation, ownership, security and architecture become more important.
Read on CtrlPlane →Labour Market
A research-based look at junior pressure, seniority inflation, productivity expectations and the changing economics of software work.
Read on CtrlPlane →Objections
No. Meniva works where the data function is just starting and where analysts and engineers are already in place. The gap being filled is senior ownership across the work, not headcount inside it.
It is senior ownership before that hire makes sense — and often the way the role gets defined properly. When you do hire, the priorities, architecture and documentation are already in place to hand over.
Yes. Most engagements run through the existing team: decisions and architecture are owned externally, implementation is shared, and your people keep the systems afterwards.
Not necessarily. Not every AI idea should become an AI project. Part of the work is separating the ideas worth building from the ones better solved by a simpler system, or by not building anything.
A Sprint is bounded around one concrete problem and ends with a decision and the work that supports it. Fractional leadership is continuing ownership across priorities, architecture and delivery, with no single defined endpoint.
Then the engagement says so. The output becomes the decision itself: what needs to be true before building, what the alternatives cost, and what to do in the meantime.
Yes. Documentation and handover are the default so you are not dependent on Meniva. Ongoing fractional involvement is available where the decision load justifies it.
International engagements are scoped per situation, so there is no fixed price list here. Scope, cadence and cost are agreed in the first conversation before any work starts.
// Next step
A conversation about what you have, what is unowned, and whether senior external ownership is the right answer right now. If it is not, that is a useful answer too.
Good fit
Not a fit
