Ingestion
Batch and streaming connectors for products, SaaS tools and third-party APIs, with replay when something goes wrong.
Data engineering
Data engineering services for teams that need to trust their numbers
When two dashboards disagree, nobody uses either. We build the pipelines, the warehouse and the definitions that make one number true across the company, and shape that data so AI features can use it without a second migration.
Overview
Most AI and analytics problems are data problems. We connect the sources, build the pipelines, model the warehouse and put quality checks in front of the numbers people make decisions with, then expose it all as a knowledge layer your products and models can query.
- Ingestion & pipelines
- Warehouse & data modeling
- Data quality & observability
- AI-ready knowledge layers
Problems we solve
- Numbers disagree
- Two dashboards, two answers, and no agreed definition of the metric underneath them.
- Pipelines break silently
- A source changes shape and nobody finds out until a report looks wrong a week later.
- AI has nothing to stand on
- Models cannot ground answers in data that is undocumented, duplicated and scattered across systems.
What we build
Transformation & modeling
Version-controlled transformations and a warehouse model with definitions your business actually agrees on.
Quality & observability
Tests, freshness checks and alerting on the pipeline, so failures surface before decisions are made on them.
Activation & AI preparation
Serving layers, APIs and embeddings that make the same trusted data available to reports, products and models.
Typical use cases
- Consolidating product, billing and CRM data into one warehouse
- Replacing spreadsheet reporting with governed metrics
- Preparing a retrieval layer for AI assistants
- Migrating a legacy ETL stack without downtime
How we deliver
- 01
Discovery
A scoped session to agree the problem, the constraints and the metric that will move.
- 02
Design & architecture
The smallest structure that solves it, written down before anyone writes code.
- 03
Build
Two-week increments with something reviewable at the end of each one.
- 04
Run & improve
Monitoring, iteration and a handover your own engineers can carry.
Technology we use
- Python
- PostgreSQL
- SQL
- ETL pipelines
Proof from the portfolio
Products that turn continuous input (statements, checks, delivery events) into something a person can act on and verify.
AI matching platform
Work Transcendence
A knowledge graph of what a person knows, where every claim keeps the sentence that proves it, matched to real work requirement by requirement.
See the projectMonitoring SaaS
Server Inspector
Uptime, SSL, domain and API monitoring that tells you something broke before your customers do.
See the projectEmail infrastructure
PigeonAtlas
Email sending for applications: an HTTP API and an SMTP endpoint, Resend-compatible, with your own DKIM key and newsletters that can never delay a password reset.
See the projectFrequently asked questions
- Do we need a warehouse before AI?
- You need trustworthy, documented data. That is often a warehouse, but for a first AI use case a focused pipeline into a retrieval store can be enough.
- Can you work with our existing stack?
- Yes. We work with what is already in place and replace pieces only where the cost of keeping them is higher than the cost of changing.
- Who owns the platform afterwards?
- You do. Everything is version-controlled, documented and handed over with your team involved in building it.
How to buy this
Data work usually starts as a scoped project, or as part of an AI build.
Related capabilities
Started with Lovable, Bolt, v0 or Cursor?
Run the free production-readiness audit first
If your app was generated by an AI builder, the first question is not which service you need. It is what the codebase is hiding. Paste the URL, get the report in a minute. If one thing is broken, we fix that single problem rather than selling you a project.
Keep going
Where to go next
- Read the case studiesChallenge, solution and measured outcome from delivered engagements.Continue
- Calculate project costA guided technical discovery that returns a budget range, timeline and team shape.Continue
- Book a strategy callForty-five minutes with the engineers who would actually build it.Continue
Data engineering
Talk to us about Data engineering
Tell us the problem and the deadline. You will speak to an engineer, not a sales team. We typically reply within 48 hours.
