Source mapping and data contracts
We inventory product events, SaaS exports, and operational databases, then define contracts for grain, keys, and freshness before pipelines are written.
Project Data
Give every project a data layer that holds up under questions.
We design warehouses, pipelines, and metric definitions so product, ops, and leadership share one version of the work, not a folder of conflicting spreadsheets.
workspace / production
live
Active tenants
128
Job success
99.4%
p95 latency
142ms
01The work
Pipelines, warehouses, and metrics that operators can trust on Monday morning.
Project data is the operational record of how a product or program is actually performing: events, tickets, shipments, revenue, quality. Codeaxes does not start with a dashboard theme. We start with the questions stakeholders already argue about, then design sources, models, and contracts that can answer them repeatedly. Ingestion is scheduled and observable. Transformations are versioned. Quality tests fail the pipeline when grain or nulls drift. The warehouse is documented so a new analyst can find the grain of a table without a hallway conversation.
02Problems
What this service is for.
01
Each team has a different count for the same KPI, and nobody can reconstruct the definition.
02
Event tracking exists in the product but never lands in a warehouse the business can query.
03
Analysts rebuild the same joins in notebooks because modeled tables were never owned.
04
A vendor export is treated as truth until a finance close exposes missing rows.
05
PII and access rules are informal, so useful data stays locked in the most cautious team.
06
Dashboards look finished, then break silently when an upstream schema changes.
03Capabilities
What we actually deliver.
Ingestion and orchestration
Batch and streaming pipelines with retries, late-arriving data handling, and alerts when a source stalls, not a cron job nobody watches.
Warehouse modeling
Layered models, staging, core, marts, with documented grain so product, finance, and operations can share tables without forking logic.
Quality, lineage, and access
Tests on uniqueness, accepted values, and volume, plus lineage and role-based access so a broken metric is diagnosable and PII stays scoped.
Metrics and decision surfaces
Metric catalogs, operator dashboards, and exports into the tools teams already live in, with owners named for each number.
Analytics engineering handoff
dbt or equivalent projects, runbooks, and review habits so your team can extend models without waiting on the original authors.
- ▹ Source inventory and data contract pack for priority entities
- ▹ Orchestrated ingestion into a warehouse or lakehouse you control
- ▹ Modeled tables with tests, documentation, and named owners
- ▹ Metric definitions aligned to product and finance questions
- ▹ Dashboards or semantic layer for the roles who make weekly decisions
- ▹ Access model, lineage notes, and a runbook for failed jobs
04Workflow
How this engagement runs.
01
Question and grain workshop
We list the decisions the data must support, the grain of each fact, and the arguments that currently happen in spreadsheets.
02
Contract and architecture
Sources, PII boundaries, warehouse choice, and SLAs for freshness are written down before transformation code multiplies.
03
Pipeline and model build
Ingestion, tests, and core models ship in vertical slices so one trustworthy domain is live before the next is attempted.
04
Decision surfaces
Dashboards and metric APIs are validated with the people who will use them, including what they will do when a number looks wrong.
05
Operate and extend
Alerting, ownership, and a backlog for the next domains, so the platform does not freeze as a one-off project.
05Fit
Industries and stack.
Industries
7 sectors
- SaaS
- Ecommerce
- Logistics
- Fintech
- Healthcare
- Education
- Manufacturing
Typical stack
Tools we actually operate
06Use cases
Where this shows up in production.
Product analytics warehouse
App events land in modeled sessions and funnels with the same user keys CRM uses, so activation debates end with a query, not a slide.
Operations control metrics
Orders, exceptions, and SLA clocks from multiple systems share a grain, so a late shipment has one status, not three.
Finance-grade revenue tables
Billing, refunds, and recognition rules are modeled with tests, so monthly close is a review, not a reconstruction.
Multi-source customer 360
Support, product, and billing identities are resolved with documented match rules and an audit of what could not be matched.
- One documented grain for each core fact table
- Pipelines that fail loudly instead of drifting quietly
- Metrics with owners, not orphan dashboards
- Access and PII rules that let more people use data safely
- A modeling project your analysts can extend after handoff
07Work
Related case studies.
SaaS and Technology
Northline Analytics
Northline needed one analytics workspace model for self-serve teams and enterprise accounts without maintaining parallel codebases or fragile permission hacks.
- TypeScript
- Node.js
- PostgreSQL
Provisioning timeHours → minutes for standard enterprise workspaces
Open study
Logistics and Mobility
RouteWise Mobility
RouteWise built a dispatch platform that prioritizes exceptions, supports dispatcher overrides, and keeps drivers productive when connectivity is unreliable.
- TypeScript
- Go
- PostgreSQL
Dispatcher focusExceptions surfaced before routine stop noise
Open study
Ecommerce and Retail
Harbor Commerce
Harbor Commerce needed a headless storefront and inventory layer that kept web, mobile, and marketplace channels aligned through peak promotional periods.
- TypeScript
- Next.js
- Shopify Hydrogen
Catalog consistencySingle source across web, app, and marketplace feeds
Open study
Manufacturing and Enterprise Operations
AxisOps Manufacturing
AxisOps digitized work orders and quality checks on the plant floor while keeping ERP production orders as the system of record.
- TypeScript
- React
- Node.js
Floor visibilitySupervisors see open exceptions without floor walks for every question
Open study
08FAQ
Questions about Project Data.
Is this the same as AI automation?
No. Project Data is the warehouse, pipelines, and metric layer. AI automation consumes that layer when language or unstructured work is involved. Many clients need the data platform first, otherwise the model is guessing on dirty joins.
Do you replace our existing BI tool?
Only if it cannot serve the questions. We often keep Looker, Metabase, or similar and put effort into modeled tables and metric definitions underneath, which is where most of the pain actually lives.
Can you work with data we cannot move to the cloud?
Yes. We design for warehouse location, VPC peering, and on-prem sources. Residency constraints belong in the architecture, not as a surprise after vendor signup.
How do you keep pipelines from becoming a second undocumented codebase?
Contracts, tests, and docs are part of definition of done. Pull requests review model grain and owners the same way application code reviews behavior.
What is Project Data at Codeaxes?
It is the warehouse, pipeline, and metric layer for how a product or program is performing. We model grain, tests, and owners so teams stop arguing from disconnected spreadsheets. It is not the same as AI automation, though automation often depends on this layer.