Discuss your Data & AI setup

Fractional Data & AI leadership

Senior Data & AI ownership, before a full-time leadership hire makes sense.

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.

Discuss your Data & AI setupHow the fractional model works

Where the work sits today

Priorities
founder / product
Architecture
CTO, between sprints
Analytics
whoever asked last
AI initiatives
experiments, unowned
Build vs. buy
deferred

The problem is rarely missing technical hands. It is missing senior ownership of the whole picture.

€1.85B+

Revenue-scale analytics experience

Professional experience

74k+

Records processed in AI demo systems

Working demo

~€850k

Revenue uplift modelled in scoring workflow

Modelled

40+ hrs/yr

Manual work automated in internal process

Internal system

Metrics include anonymised professional experience, internal systems, and modelled demo scenarios. No figure describes a realised Meniva client result.

Recognition

When companies call Meniva

Four situations, not four services. If more than one is recognisable, the conversation is usually about ownership.

  1. 01

    Data & AI work is distributed across several people, but nobody owns the full picture.

    Founder / CEO
  2. 02

    The CTO is carrying Data & AI decisions alongside engineering.

    CTO
  3. 03

    Analysts or engineers exist, but senior architecture and prioritisation are missing.

    Data team
  4. 04

    AI initiatives exist, but nobody can confidently decide what is worth building.

    Product / Ops

The ownership layer

What senior Data & AI ownership actually covers

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.

  • priorities
  • architecture
  • metrics
  • build / buy
  • implementation
  • governance
  • handover

Ways to work together

Engagements

Not a pricing ladder. Fractional lead → prioritise → build → stay involved · defined problem → sprint → implementation → handover · sprint → handover.

01

Fractional Data & AI Lead

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.

02

Data & AI Sprint

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.

03

Implementation

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

Decisions first, then systems

  1. 01

    Understand the decisions

    Which business and technical decisions are currently blocked, and who is carrying them.

  2. 02

    Prioritise the work

    What is worth building now, what waits, and what should not be built at all.

  3. 03

    Design and deliver

    Architecture plus hands-on implementation, with your engineers and analysts involved.

  4. 04

    Transfer or stay involved

    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

Systems you can click into

Inspectable systems with the evidence boundary stated. Each one is labelled by what it proves, and what it does not.

All work →

CLIENT 01 / GREEHILL / APPROVED REFERENCE

GreeHill
WeSpeakAI

Data engineering & architecture consulting · via WeSpeakAI

Production ML/data platform assessment & handoff engineering

Evidence-backed architecture, reliability and capacity assessment for a production GPU-backed ML/data platform, followed by implementation work on a cross-repository preprocessing handoff.

What this demonstrates

Production evidence can be turned into prioritised engineering decisions, explicit acceptance criteria, reproducibility controls and a safer producer/consumer boundary across Python, Airflow, Kubernetes, PostgreSQL and Azure.

When this pattern is useful

A production data/ML platform works, but architecture knowledge, reliability controls or cross-system ownership have become difficult to reason about.

See engagement →Consulting → implementation

PRODUCT 01 / CONTOUR / OWN PRODUCT · LIVE

Founder-built AI learning platform

Contour

A production learning platform and application that combines structured lessons, practice, progress instrumentation and AI tutoring into one product.

What this demonstrates

Meniva can own a product end to end: product framing, architecture, implementation, measurement and iteration - not only a narrow model integration.

When this pattern is useful

A knowledge or learning product needs a real application layer, measurable learner journeys and AI that is embedded in the workflow rather than bolted on as chat.

Open Contour →Owned product

SEC 02 / TASTETREND / DEMO AVAILABLE

Restaurant review intelligence

TasteTrend Analytics

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.

Inspect →Sprint → implementation

Labs & R&D

Prototypes with the boundary visible

Lab 01 · prototype

Scoutbound

Agentic prospecting workflow. Research leads, keep evidence, score fit, export to CRM.

Lab 02 · under development

Revon

ML-ready lead scoring workspace for enrichment, routing, review, and RevOps automation.

Lab 03 · R&D

AI Research Intelligence

Local RAG for AI research. Search papers, rerank evidence, synthesize source-backed answers.

// Client & professional systems experience

Production work beyond the demo environment.

Meniva now includes approved named consulting experience as well as Bálint’s earlier employed work. GreeHill is shown with permission; the engagement was delivered through WeSpeakAI. Earlier enterprise examples remain anonymised. No confidential client code, data, commercial information or internal implementation detail is reproduced here.

  • data foundations
  • analytics & BI
  • ML & forecasting
  • AI automation
  • RAG & AI workflows
  • data engineering

EXP 01 · NAMED CLIENT

GreeHill - production ML/data platform engineering

Architecture, reliability and capacity assessment plus cross-repository handoff implementation for a production GPU-backed ML/data platform. Engagement delivered through WeSpeakAI.

EXP 02

Retail price & promotion analytics

Decision workflows over commercial and market data.

EXP 03

Large-scale PySpark pipelines

Reliable transformation and quality controls at enterprise scale.

EXP 04

Power BI decision products

Governed semantic models and operational dashboards.

EXP 05

Forecasting, metadata & statistical-data modernisation

Reproducible models, schema, quality, lineage and publishing workflows.

Bálint, founder of Meniva

Operating model

Founder-led. Senior attention stays close to the work.

Meniva is a founder-led Data & AI practice run by Bálint. The person framing the business problem is the person making the architecture decisions and, where useful, writing the implementation. Client consulting and Meniva’s own product work stay close to the same senior operator - nothing is delegated down a bench.

  • Business framing
  • Analytics judgment
  • Data & AI architecture
  • Implementation depth
  • Direct senior accountability
  • Documentation and handover

5+Years in Data & AI

20+Projects delivered

Professional references

People who have worked with Bálint

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.”
Csaba MezeiCommunications Designer & Marketing Manager
“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.”
Gáspár Horváth, MBAFounder @ Miutcank
“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.”
Anna Laura Selmeci, JDFounder & Digital Legacy Specialist

Insights

Read CtrlPlane

The professional writing and research layer of the Meniva ecosystem: analysis of AI, data work, technology organisations and the changing infrastructure of knowledge work.

Open CtrlPlane →

AI Operating Model

AI adoption is not transformation

Why buying AI licenses is not the same as redesigning how work, data and responsibility flow through an organization.

Read on CtrlPlane →

Engineering Systems

The bottleneck moved from coding to review

Agentic coding makes code cheaper to produce, but validation, ownership, security and architecture become more important.

Read on CtrlPlane →

Labour Market

The Hungarian IT market after the AI shock

A research-based look at junior pressure, seniority inflation, productivity expectations and the changing economics of software work.

Read on CtrlPlane →

Objections

The questions buyers actually ask

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.

// Next step

Discuss your Data & AI setup

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.

Start the conversationRead the fractional model first

Good fit

  • Real data and system complexity already exists
  • Data & AI work is emerging faster than ownership
  • Decisions matter more than extra hands

Not a fit

  • Staff augmentation or body-shopping
  • A single dashboard with no decision behind it
  • An AI project chosen before the problem