For teams scaling with complexity

Turn Fragmented Data IntoTrusted Decisions

We help leadership teams align around shared metrics, governed models, and AI-ready data access so teams can answer important questions faster.

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Where Growing Teams Get Stuck

You do not need more dashboards. You need a shared decision layer that helps teams answer operational and commercial questions without metric debates, reporting bottlenecks, or fragile logic.

Metrics change depending on who answers

Finance, operations, growth, and leadership often rely on different definitions, which turns roadmap conversations into debates about whose number is right.

Important questions wait in analyst queues

When reliable answers depend on ad hoc pulls, teams move slower, lose momentum, and make prioritization calls with partial context.

Reporting gets rebuilt over and over

Without shared models and documented logic, every new dashboard or one-off analysis recreates the same business rules in a slightly different way.

AI gets added before the foundation is ready

Natural-language analytics and internal copilots only help when the underlying data, governance, and ownership model are already trustworthy.

Outcomes & Proof

Takenos: building a decision-ready data foundation in roughly two months

We helped Takenos unify behavioral, operational, and business data into a governed analytics layer that multiple teams could use. The point was not just to install tools. It was to reduce metric ambiguity and give the company a more reliable basis for decision-making.

The case study also shows the delivery model behind the work: visible weekly cycles, structured workshops, and a clean handoff so the internal team could keep extending dbt with confidence.

Read the Case Study

Major SF VC

Major SF VC: replacing a black-box data vendor with an owned intelligence layer

We built a full data intelligence platform for a leading venture capital firm so investors could work from a unified view of people, companies, and signals instead of fragmented vendor-managed outputs.

The work included custom identity resolution across four sources, production pipelines into Snowflake, and a handoff model that helped the partner engineering team reuse the same pattern with another client.

Read the VC Case Study

Trusted by teams building with complexity

We have partnered with fintech and digital teams that need reliable infrastructure, aligned metrics, and stronger decision support as they scale.

How We Help Teams Scale

We are not just implementing a stack. We help teams build the shared data layer, operating model, and access patterns that improve the speed and quality of decision-making.

Build a Trusted Decision Foundation

We connect behavioral, operational, and commercial data into a governed analytics layer your team can actually rely on for decisions.

Create Shared Metrics Across Teams

We model the definitions that keep finance, operations, growth, and leadership aligned instead of reconciling numbers in every meeting.

Enable Faster Answers With AI-Ready Access

From governed warehouses to natural-language analytics experiences, we make trustworthy information easier to access without bypassing the underlying logic.

Deliver With Your Team, Then Hand Off Cleanly

We work in visible weekly cycles, document the modeling layer, and help internal teams take ownership instead of inheriting a black box.

How We Work

Senior teams usually need more than implementation capacity. They need a delivery model that keeps the work visible, aligns people around shared logic, and leaves the company stronger after the engagement.

Align on the Questions and Definitions

We start with the business questions that matter most, then map the shared metrics and source systems behind them.

Deliver in Visible Weekly Cycles

Instead of disappearing into a long implementation, we keep progress collaborative, reviewable, and tied to the teams who will use the outcome.

Model for Trust, Reuse, and AI Readiness

We build governed transformation layers, documentation, and access patterns that make dashboards, analysis, and AI workflows safer to scale.

Transfer Ownership to the Internal Team

Workshops, code reviews, and a maintainable dbt workflow help your team extend the foundation after handoff instead of depending on us forever.

Who You’ll Work With

A small senior team that stays close to the work. We combine commercial context with deep data engineering execution so the output is useful to both decision-makers and implementers.

Marco

Co-Founder & Data Engineer

Works with data and business leaders on governed metrics, scalable analytics foundations, and AI-ready access patterns for transaction-heavy businesses.

Manuel

Co-Founder & Data Engineer

Leads hands-on delivery across data platforms, dbt workflows, and cloud architecture with a strong focus on maintainability and team ownership.

Juan Pablo

Data & AI Engineer

Builds the tooling, integrations, and AI-enabled workflows that make modern analytics systems more reusable, accessible, and production-ready.

Google Cloud Partner

Technical services partner

As a Google Cloud Partner, we help teams design and operate data foundations on infrastructure that can scale with organizational complexity. That matters when analytics, AI workflows, and internal decision systems need to be reliable from day one.

Bring Us In When Important Questions Outgrow Ad Hoc Reporting

If your team needs aligned metrics, faster answers, or an AI-ready analytics foundation, we can help you scope the right next step.

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Insights for Decision-Makers

Case studies and practical perspectives on shared metrics, AI adoption, and delivery models that make analytics more useful across teams.

Case StudyShared MetricsFintech

Takenos Case Study: A Decision-Ready Data Foundation in 2 Months

How we aligned finance, operations, growth, and leadership around shared metrics and a governed analytics layer in roughly two months.

Read article

AITeam EnablementAgents

Why Teams Should Start Building and Sharing AI Agents Now

A practical take on how teams can operationalize AI around real workflows once the right data foundations are in place.

Read article

EngineeringData PlatformsDelivery

From Software to Data: Building the Tools We Need

An inside look at how we build reusable data tooling and delivery systems instead of reinventing the plumbing on every engagement.

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Open Source That Supports Delivery

We build and share reusable tools because strong delivery depends on repeatable systems, not reinventing the plumbing on every engagement.

PythonETLBlockchain

tap-dune

A Tap adapter for Dune Analytics, enabling seamless data extraction from on-chain data queries.

View on GitHub

PythonFirestoreETL

tap-firestore

Google Cloud Firestore tap adapter for reliable data ingestion into your modern data stack.

View on GitHub

PythonIdentityETL

tap-persona

Identity verification data integration for building trusted user onboarding flows.

View on GitHub

PythonDatabaseETL

tap-turso

Turso database tap adapter for edge computing and distributed database scenarios.

View on GitHub

TypeScriptAITemplates

template-claude-plugins

A template repository for building Claude AI plugins with best practices and scaffolding.

View on GitHub

Explore All Projects

Questions Teams Ask Before They Call

Clear answers help both buyers and AI systems understand where Blueprintdata fits, what problems it solves, and how the engagement works.

What does Blueprintdata do?

Blueprintdata helps teams turn fragmented operational, financial, and product data into a governed decision layer. The work usually includes shared metric definitions, modeled analytics foundations, and access patterns that make reliable answers easier to get.

Who is Blueprintdata for?

Blueprintdata is for teams scaling into complexity, especially when product, finance, operations, growth, and leadership need to work from the same logic. It fits companies that need a stronger analytics foundation before scaling dashboards, reporting, or AI workflows.

What problems does Blueprintdata solve?

Typical problems include metric ambiguity across teams, reporting bottlenecks, repeated dashboard rework, and AI initiatives built on data that is not yet trusted. Blueprintdata focuses on making the underlying data model reusable, governed, and easier to maintain.

How does Blueprintdata work with internal teams?

Blueprintdata works in visible weekly cycles, keeps definitions and modeling choices reviewable, and aims for a clean handoff. The goal is to leave the internal team with maintainable workflows, documented logic, and more ownership after the engagement.

Is Blueprintdata focused on AI or on data foundations?

The primary focus is the data foundation. Blueprintdata helps teams create governed models and shared metrics first, then enables AI-ready access so natural-language analytics and other AI workflows can rely on trustworthy logic instead of bypassing it.