Data engineering and integration for records that agree across the business.

Finance and operations report the same numbers. We build pipelines from ERP, CRM, accounting, and field systems into one data layer, with shared definitions, quality checks, and history that reporting, applications, and AI can rely on.

Scope a data project How the work runs

Who hires us for this

Finance, operations, and technology leads whose reports come from exports, whose systems disagree about the numbers, or who want reporting, an application, or an AI feature that the current data cannot support. Also companies migrating data between systems or consolidating after an acquisition.

Why now

Two reports from two systems give two answers to the same question.
A reporting or AI project stalled because the data was not there, or not trusted.
A migration or an acquisition means records have to move and match.

What this covers

What data engineering and integration covers.

Pipelines from operational systems

Records from ERP, CRM, accounting, and field systems brought into one place on a schedule, with failures visible.

A shared data layer

Definitions agreed once: what a customer, an order, a job, and a margin mean, so every report uses the same ones.

Quality and reconciliation

Checks that catch missing, duplicate, and disagreeing records, with an owner for each exception.

Data migration

Records mapped, cleaned, and moved between systems, reconciled at every step.

History

Changes kept, so the business can see what a record looked like last quarter.

The foundation for reporting and AI

Dashboards, applications, and models built on data that has been checked, not on exports.

A decision, not a pitch

Exports, a BI tool on the sources, or a data layer?

There are three ways to get numbers out of your systems. The right one depends on how many sources you have, how much they disagree, and how much rides on the answer.

Consideration	Exports and spreadsheets	A BI tool on the source systems	A shared data layer
Fits when	One source, occasional questions	A few sources that already agree	Several sources, definitions that differ, decisions that depend on the numbers
Definitions	Whoever built the spreadsheet	Per dashboard	Agreed once, used everywhere
Quality	Unchecked	Unchecked	Checked, with owners for exceptions
History	None	Whatever the source keeps	Kept
Our advice	Fine until it is shared	Fine for a few sources	When reports, applications, or AI depend on it

ERP / accounting
CRM
Field / operations
Pipelinevalidate · reconcile
Exception queue
Data layerdefinitions · history
Reports
Applications
AI features

One pipeline, one set of definitions, and every report or feature built on records that have been checked.

When companies call us

If this sounds like your business, here is where we start.

Finance and operations report different revenue

Agreed definitions, one pipeline from both systems, and reconciliation that shows where they differ.

A dashboard project stalled on the data

The data layer first: sources connected, definitions agreed, quality checked; then the dashboards.

An acquired company’s records must be merged

Mapping, matching rules, and a migration reconciled against both systems.

An AI feature needs clean history

The records it needs, with history and quality checks, before the model is built.

How the work runs

Scoped in writing, built in releases, handed over.

01
Inventory the sources
Every system that holds a record the business reports on, and who owns it.
02
Agree on the definitions
What the key records and measures mean, written down with finance and operations in the same room.
03
Build the first pipeline
The sources behind the first set of reports, into the data layer, with quality checks and reconciliation.
04
Reconcile and hand over
Numbers checked against the sources and the old reports; documentation and a runbook for the team.
05
Extend
The next sources and the next questions, on the same foundation.

Deliverables

What you receive.

ScopeAgreed

First release · version 1.2Scope and acceptance

Includes: The core workflow, roles and permissions, and the first integration
Left for later: Reporting and the second integration
Acceptance: Criteria agreed with your team
Price: Fixed before the build begins
Start date: Agreed in writing

Approved by operations Approved by finance

RunbookHanded over

Operations · version 1.0Running the system

01Deploy a releasePipeline
02Roll back a releaseTested
03Restore from backupTested
04Alerts and who respondsCurrent
05Access and secretsBy role

Written scope
What the first release includes, what it leaves for later, the integration decisions, the acceptance criteria, and the price, agreed before development begins.
Release notes
What changed in each release, what was tested, what the security review covered, and how to roll it back.
Documentation and runbooks
How the system is built, how to run it day to day, and what to do when something fails.
Training and handover
Sessions with the people who will use and run the system, and a handover checklist your team signs off on.

Every release ships with tests, a security review, and documentation, so your team can run what we build.

Your first release

What the first release could include.

The scope is written and agreed before development starts. This is the shape it usually takes.

In the first release

A written map of sources, records, and owners
Agreed definitions for the first set of measures
A pipeline from the first sources into the data layer, on a schedule, with failures visible
Quality checks and reconciliation with owners for exceptions
Stored history, documentation, and a runbook

Usually later

Additional sources and measures
Dashboards and reports on the layer
Data for applications and AI features

Systems it may connect

Built to work with what you already run.

ERP and accounting: NetSuite, Acumatica, Sage, QuickBooks
CRM: HubSpot, Salesforce, Zoho, custom
Operational and field systems
Warehouses and databases: PostgreSQL, SQL Server, BigQuery, Snowflake
Reporting tools: Power BI, Looker, Metabase, custom
Spreadsheets, where they are still a source

Multi-location restaurant group

Location managers and finance work from the same sales and labor numbers.

Restaurant groups typically pull sales from the point-of-sale system, labor from scheduling, and costs from accounting, so the numbers location managers quote rarely match the ones finance reports. A data layer starts with agreed definitions for net sales, labor cost, and food cost, then brings those sources in on a schedule with reconciliation checks. Where sources disagree, the difference is shown with its reason and an assignee instead of settled by argument. Location dashboards and data for forecasting come next, built on the same definitions.

Where this shows up

Industries where we do this work.

Examples, not a closed list. We take substantial projects from independent businesses in other industries too.

Logistics and distribution businesses

Orders, stock, and shipment status that agree across the ERP, the warehouse, and the carriers, so customers see where their order is without calling.

Specialty manufacturers

Quotes priced from documented rules, won jobs flowing into the ERP and the shop traveler, and job status visible without walking the floor.

Private equity portfolio companies

Software across the hold period, with a portfolio assessment for the fund and a delivery path for each company.

Another industry entirely

Describe the workflow and the systems around it. Fit depends on the work and the stakes, not the sector.

In plain terms

What is data engineering?

Data engineering builds the pipelines, definitions, checks, and storage that turn the records in operational systems into data a business can rely on for reporting, applications, and AI. Data integration is the part that brings systems together; data migration is the part that moves records between them.

We build the layer that reports and features stand on, sized to the business: a few sources and a warehouse the team can run, not an enterprise platform.

Questions

Questions about data engineering and integration.

What buyers ask before a project starts, answered plainly.

Do we need a data warehouse?

For a few sources, a well-structured database is enough. For many sources and heavy reporting, a warehouse earns its place. We base the recommendation on your inventory of sources, not on a preference.

Who decides what a measure means?

Finance and operations decide together, and we write the definitions down. We facilitate and record; the business makes the calls.

How do you handle bad data?

Quality checks catch missing, duplicate, and disagreeing records and route them to an owner. Nothing is silently corrected.

Can you migrate data between systems?

Yes. Records are mapped, cleaned, moved, and reconciled against both systems, with history kept and nothing deleted without sign-off.

Is this the same as reporting?

It is the foundation for it. Reports and dashboards are built on the layer; see business intelligence and dashboards.

Who runs the pipelines?

Your team runs them, with monitoring, documentation, and a runbook, and we stay available for new sources and definitions.

Scope a data project.

Tell us about the work and the systems around it.
We’ll come back with a written scope for a first release.