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Alvorance

Technology consultancy

Software, data and measurement that support decisions.

A consultancy for software engineering, data engineering and marketing measurement. Defined scope, phased delivery and accountability for the outcome.

Practices

Three areas of work.

Engineering, data and measurement inside the same project, so the systems and the numbers tell the same story.

Software engineering

From design to operation: products and platforms built to last beyond the first release.

  • New products from scratch, with architecture that survives year two
  • Legacy modernization without stopping the business
  • CI/CD, observability and testing pipelines your team takes over afterwards
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Data engineering

A data platform you can trust: ingestion, modelling and governance that support decisions.

  • A modeled lakehouse or warehouse, with data contracts and tests
  • Versioned, monitored pipelines with traceable lineage
  • Governance and cataloging that outlive whoever built them
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Analytics & tracking

Marketing measurement that survives cookie blocking, privacy audits and the question “where did this sale come from?”.

  • GA4 and server-side GTM implemented from a measurement plan written before the code
  • Consent Mode v2 and privacy compliance without losing conversion signal
  • Conversions API and attribution that reconcile with finance
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Where we work

Situations that usually start a project.

If one of them describes where you are, the matching practice is just below.

Business leadership

  • “Every department brings a different number for the same question”
  • “We can't state the return on what was already built”

Technical leadership

  • “The team burns the sprint firefighting legacy”
  • “Shipping a release is a risk event, not a routine”

Marketing and growth

  • “The ad platforms and the CRM disagree on how many sales happened”
  • “Privacy compliance was requested and nobody knows the impact on measurement”

Reference architectures

How we structure the most common projects.

We publish the models we use and the decisions behind them, so you can assess our approach before hiring.

Modern data platform

Data scattered across spreadsheets, production databases and manual exports. Every report is rebuilt from scratch and nobody can audit where a number came from.

Sources → Ingestion → Layers → Transformation → Consumption

Server-side tracking with consent

Blockers and cookie limits wiped out conversion volume. Platforms disagree with each other and legal is asking for privacy compliance.

Browser → Own collector → Policy → Destinations → Warehouse

Delivery pipeline

Deploy is a risk event scheduled for Friday night. There is no trustworthy test environment and the first news of failure comes from the customer.

Commit → Integration → Ephemeral environment → Production → Observability

See the architectures

How we work

Projects in phases, with defined scope and timeline.

Each phase has a duration, a deliverable and a decision point before the next one.

  1. 01

    Discover

    1 week

    Conversations with the people who feel the pain and the people who maintain the system. What exists, what already failed, and why.

  2. 02

    Define

    1 to 2 weeks

    Scope cut down until it fits a first useful increment. Acceptance criteria written before any estimate.

  3. 03

    Design

    1 to 2 weeks

    Architecture, data contracts and measurement plan. The design is reviewed with your team before it becomes code.

  4. 04

    Deliver

    2-week cycles

    Releases to production every cycle, rather than a single switch-over at the end. Problems surface early, while they are still cheap to fix.

  5. 05

    Transfer

    2 weeks

    Onboarding for your team, an operations runbook and documentation. The stated goal is that the operation runs without depending on us.

See the methodology

Let's talk about your project.

An initial conversation to understand the context and assess how we can help.