A DATA & AI CONSULTANCY
The data foundations that make AI actually work. Built by one senior team. Shipped in weeks.
A free 2-minute AI-readiness score — see where your data is holding AI back.
THE PROBLEM WE EXIST FOR
Everyone bought AI. Almost nobody fixed the data underneath it. That's the gap between a pilot and a P&L impact.
KEEP SCROLLING
Technology partners
Certified where it counts — cloud architecture and observability, delivered to partner standard.
THE DATAVEXA FRAMEWORKS
Three proprietary accelerators. One operating sequence.
Not generic consulting phases — codified playbooks we've refined across engagements. Each ships with its own diagnostic score, deliverables, and timeline.
Clarity
"Do we agree on what the numbers mean?"
Removes metric conflict. When leadership dashboards disagree, the problem isn't model performance — it's definitions, ownership, and governance. One KPI dictionary, named owners, an approval lineage, and a single trusted dataset for the board.
Foundations
"Can we produce trusted data reliably?"
Removes slow, fragile, manual delivery — because adding another tool doesn't fix delivery discipline. Monitored pipelines with SLAs, reusable models, and tests in the pipeline. Zero to analytics-ready in weeks, not a two-year rebuild.
CORE
"Can we run it at scale without breaking the budget or the rules?"
Removes cloud execution risk. FinOps cost allocation, latency baselines for critical workloads, and compliance controls tied to architecture — the guardrails that decide whether AI is affordable and defensible in production, not just possible in a pilot.
CASE STUDY — CLIENT ANONYMIZED
A FAST-SCALING US RETAIL-COMMERCE PLATFORM
$M/mo
migrated with zero disruption.
The core platform rebuilt underneath a fast-scaling US retail-commerce business — while the numbers people trust never moved. Net-sales parity held to −0.007% straight through the migration.
Reporting ran on a legacy warehouse the business had outgrown. Dashboards were slow, definitions drifted, and new revenue surfaces had no measurable attribution.
Rebuilt the core platform on Redshift, migrated the flagship dashboards behind a governed KPI layer, and shipped one conformed attribution model — plus an AI data assistant over a semantic layer.
Migration completed with headline parity to seven decimal places. Every sale line now carries one attribution source — reporting the business can build on.
AI WITH RESPONSIBILITY
Fix the data.
The AI part gets easy.
AI doesn't fail because the models are weak. It fails because the data underneath isn't trusted, isn't reliable, or isn't affordable to run. We remove those constraints in order — so when you turn AI on, it works, and keeps working.
Advanced AI
Trusted · reliable · affordable. Assistants, forecasting, anomaly detection — deployed on ground that holds.
…so AI runs at scale, safely
…so AI gets fresh, correct data
…so AI reasons over agreed meaning
AI scans schemas and reports to surface conflicting definitions, drafts pipeline and test code, and models cost impact before a workload switches on. The grunt work is automated — the judgement stays human.
Every framework leaves your organisation more AI-ready than it found it — trusted definitions, a dependable serving layer, and cost and compliance guardrails. We don't lead with AI. We remove the constraint that would make it fail, then scale it on top.
THE WHOLE THESIS
Fix the data. The AI part gets easy.
Every model, forecast, and assistant runs on the same foundation. Get the trust, reliability, and cost of that foundation right, and everything downstream stops being a fight.
DELIVERED ACROSS
Regulated, consumer, and scale-up. Same operating sequence.
We work with enterprise and scale-up data teams across Europe and North America. Every engagement below is real and anonymized — sector labels only, the work speaks, the names stay private.
Top-5 German grocery retailer
Data-quality framework and internal DQ reporting; Syniti-to-Databricks migration with defect tracking.
GROCERY · DQGlobal FMCG multinational
Embedded Foundations squad delivering the data-foundation layer of a group-wide, multi-year roadmap.
CONSUMER · FOUNDATIONSGlobal pharmaceutical leader
Active data-engineering delivery inside a regulated pharma environment — governed, audited, compliant.
PHARMA · REGULATEDTalent-assessment scale-up
Zero to self-serve analytics: a full Power BI reporting product delivered end to end for leadership.
SCALE-UP · REPORTINGIberian industrial group
Data & AI transformation programme — strategy through to platform foundations, one team throughout.
INDUSTRIAL · STRATEGYGlobal medical-device manufacturer
A self-serve data marketplace — data-as-a-product with contracts, ratings, and a GenAI discovery assistant over a governed catalog.
MEDTECH · DATA MARKETPLACEINDUSTRIES WE SERVE
Where the readiness sequence pays off — the use cases that become possible once the data underneath is trusted.
Grid, assets, and supply
Discovery to compliance
Governed, one view, real-time
Retail, telco & infrastructure
HOW AN ENGAGEMENT ACTUALLY RUNS
“Weeks, not months” is a schedule, not a slogan.
WEEK 01
Diagnose & align
The diagnostic, run with your leadership. KPI conflicts mapped, owners named, one metric set agreed. Everyone sees the same problem by Friday.
WEEK 02
Build the baseline
Source-of-truth model stood up. First reliable pipeline in production — modest, tested, and already useful.
WEEK 03
Ship outcome №1
A trusted dashboard live, adoption tracked. Leadership sees the same number everywhere it looks — for the first time.
THEN
Compound
Foundations and CORE extend the baseline. Every week ends with something shipped — no discovery theater, no 40-page decks.
WHAT LANDS IN YOUR HANDS
Not decks. Working artifacts.
Every metric defined once, owned by a name — not a committee.
One number leadership can repeat in the boardroom without a caveat.
A monthly report your CFO actually looks forward to.
OPERATING PRINCIPLES
Hold us to these.
Clarity before scale
Shared understanding before automation. We don't ship models onto data nobody trusts — in your company or ours.
Pragmatic over perfect
First shipped outcome in 2–3 weeks. If a week ends without something concrete in production, ask us why — or fire us.
Frugality & efficiency
We treat your cloud invoice like our own money. No unnecessary complexity, no open-ended retainers, no tool evangelism.
Business first, always
Problems and outcomes before technology. When we leave, your team can run everything we built.
AI with responsibility
We accelerate value only when the data underneath is trusted. AI amplifies what is already there — it will not fix a broken process, it will scale it.
Collaboration & listening
The best outcomes come from diverse perspectives. We build capability inside your team, not dependency on ours.
THE SENIOR TEAM
The people who scope it, build it.
Fixed-scope engagements. No open-ended retainers. No junior leverage model — this is the whole team, and it's who shows up.
Luis Laginha
FOUNDER · DATA STRATEGY LEAD
20+ years of data strategy and delivery for global enterprises in pharma, retail, energy, and supply chain.
AWS SOLUTIONS ARCHITECT · CSM
Hugo Lopes
DATA & CLOUD ARCHITECT
A decade of cloud-native platforms — secure, automated, and measurably cheaper to run.
AWS SOLUTIONS ARCHITECT, PROFESSIONAL
Paulo Fernandes
SENIOR DATA ENGINEER
Ships the pipelines — AWS, Terraform, Snowflake. Production-grade from week one.
AWS · AZURE · SNOWPRO · MIT xPRO
FROM OUR INSIGHTS
FAQ
Common questions
Why do most AI projects fail?
AI rarely fails because the models are weak. It fails because the data underneath is not trusted, not reliable, or not affordable to run. Datavexa removes those constraints in order — trust, then reliability, then cost and safety — so AI works in production and keeps working.
What does Datavexa do?
Datavexa is a data & AI consultancy. One senior team both advises and builds, delivering three readiness frameworks — Clarity (metric trust), Foundations (reliable delivery), and CORE (cost, performance, compliance) — that make advanced AI trusted, reproducible, and affordable.
How long does an engagement take?
Weeks, not months. Engagements are embedded, outcome-accountable, and fixed-scope, with a first shipped outcome typically in two to three weeks.
Where should we start?
A 9-question diagnostic decides the right entry point: Clarity if definitions are the problem, Foundations if delivery is fragile, CORE if cloud cost or compliance is the constraint. Most organisations need one or two rungs, not all three.
Know where
you stand.
Nine questions. Two minutes. A blunt answer on what to fix first.
OR TRY THE 60-SECOND PRE-CHECK