Data concierge · By Gabriel Fernandes

Data Concierge

Your data,
tailored.

AI consulting that begins with your data: I build dbt warehouses, dashboards your team actually trusts, and the governance that lets you adopt AI safely.

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Built data at

LeafwellTales Inc.HSTKOutbackVitaconHousiBluefitClubMed

What I do

Four pillars, one commitment: data your team can trust.

Data Warehouse with dbt

Layered modeling, automated tests, living documentation and CI/CD, a foundation that grows with your team, not against it.

  • Dimensional modeling
  • Tests & data contracts
  • dbt docs documentation
  • CI/CD and environments

Dashboards & BI

From metric definition to delivery in Metabase, Power BI or Tableau. One KPI per name, one source of truth.

  • KPI definition
  • Semantic models
  • Executive dashboards
  • Self-service for the team

Governance & AI-readiness

Data catalog (Atlan), metric normalization, and the workflows that prepare your foundation for generative AI on internal data.

  • Data catalog (Atlan)
  • Data contracts & ownership
  • End-to-end lineage
  • LLM/RAG-ready foundation

AI training for companies

Workshops and hands-on tracks that get your team using AI day-to-day with judgment, safety and real outcomes.

  • Tracks for leadership and operations
  • Hands-on with the team's tools
  • Prompts, workflows and guardrails
  • Responsible AI use policy

What it looks like

Dashboards your team trusts.

An interactive preview of a typical dashboard I ship: filters, comparisons, and metrics that hold up under any slice.

Revenue over time

CurrentPrevious period
Range
Segment
8.6%

Revenue

$248.4k

4.2%

Active customers

1,280

0.6%

Data quality

97.4%

0.3%

Freshness

1.4h

47k52k57k63k68k-30d-23d-14d-7dtoday
Illustrative data · For demo purposes only30 days

Revenue by channel

Direct sales42%
  • Direct sales42%104.3k
  • Partners26%64.6k
  • Self-serve18%44.7k
  • Marketplace14%34.8k

Top accounts by revenue

  • Acme LogisticsEnterprise
    Revenue: 48.2kQuality: 99.1%
  • Northwind FoodsEnterprise
    Revenue: 41.8kQuality: 98.6%
  • Globex EnergyEnterprise
    Revenue: 38.9kQuality: 99.3%
  • Stark RoboticsEnterprise
    Revenue: 33.1kQuality: 98.2%
  • Vandelay ImportsEnterprise
    Revenue: 27.6kQuality: 97.9%

AI consulting & readiness

Before you plug in AI, fix the foundation.

AI models are only as good as the data feeding them. Companies that rush to ship a copilot without fixing the foundation end up with inconsistent answers, hallucinated metrics, and lost trust. I do the inverse.

Without a trustworthy foundation

  • ✕Duplicated metrics produce contradictory answers.
  • ✕No lineage means no one knows where numbers come from.
  • ✕LLMs hallucinate on poorly defined KPIs.
  • ✕Internal adoption stalls because trust evaporates.

With the base ready

  • →One source of truth per metric.
  • →Catalog + lineage that LLMs can query.
  • →Governance that filters what AI is allowed to see.
  • →Fast adoption because the team trusts the data.
Let's talk about AI + dataExplore AI consulting

How we work

Four steps. No vague promises, no consulting theater.

  1. 01

    Diagnose

    Sessions with the team, a read of the current stack, and a map of what's broken.

  2. 02

    Design

    Proposed architecture, scoped deliverables, success metrics, and timeline.

  3. 03

    Implement

    I build alongside your team, not in silence inside a black box.

  4. 04

    Handoff & training

    Documentation, runbooks, and training so the team can run it without me.

Notes

Writing on data, dbt and AI.

· AI consulting

AI consulting for Brazilian mid-market companies: where to start without burning the budget

AI consulting for mid-market companies is rarely about training a model. It's judgment plus a data foundation plus one narrow, clear-ROI case before you spend.

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· AI

AI agents for small and medium businesses: where to start, and why most pilots stall

Only 23% of organizations are scaling agentic AI, and ~95% of GenAI pilots show no P&L impact. For an SMB, the realistic starting point is one narrow agent on cleaner data, not transformation.

Read post
· dbt

Analytics engineering for small teams: the six stages most teams skip past

A staged maturity model for analytics engineering for small teams, with the operational signal that tells you it's time to move and the failure mode at each step.

Read post

Next step

Let's talk about your situation.

30 minutes, no commitment. You tell me where you are; I tell you if I can help, and if I can't, I'll point you to someone who can.

Book a call