01
DATA ENGINEERING
Everything
starts here.
Before intelligence comes the foundation. We turn fragmented sources into data your systems can depend on.
- Source integration & ingestion
- Document processing
- Pipeline quality & observability
INDEPENDENT DATA & AI ENGINEERING
Intelligence,engineered.
We build data foundations, production RAG
and agent workflows around the way your business actually runs.
Make the complex
work beautifully.
┤ RECENT WORK ├
- 955 court judgments, structured
- 6–8 h manual job, automated
- BigQuery → Snowflake, validated
- Reports that update themselves
THE SPACE BETWEEN IDEA AND OPERATION.
That’s where
we do our best work.
NOVO ATMOS / ENGINEERING PRACTICE
The scattered data. The prototype that won’t scale. The workflow held together by hand. We connect the pieces—and engineer what comes next.
FROM PROBLEM TO PRODUCTION
Good ideas need more than good code.
They need connected data, dependable systems, and intelligence that works beyond the prototype.
01 / FOUNDATIONILLUSTRATIVE EXAMPLE
Everything
02
PRODUCTION RAG
Find the signal.
Keep the evidence.
Build answers around what your business actually knows—with source attribution, access boundaries and evaluation from the beginning.
- Document search & retrieval
- Permissions & citations
- Evaluation & deployment
03
AGENTIC WORKFLOWS
Intelligence.
Put to work.
Connect useful reasoning to the tools your business runs on. Validate the actions. Keep people in control. Plan for the unexpected.
- Tool & API integration
- Human approval boundaries
- Monitoring & recovery
01Data engineering
Everything
starts here.
Before intelligence comes the foundation. We turn fragmented sources into data your systems can depend on.
- Source integration & ingestion
- Document processing
- Pipeline quality & observability
“[He] also demonstrated great initiative by designing an automated validation system to ensure data consistency between Google and Snowflake migrations.”
↳ click a source to break it
ingest12,408
validate12,194
dedupe12,061
conform12,061
publish12,061
214 rows heldrun #1,482 · 3m 12s
✓
orders_clean✓
customers✓
revenue_daily✓
events02Production RAG
Find the signal.
Keep the evidence.
Build answers around what your business actually knows—with source attribution, access boundaries and evaluation from the beginning.
- Document search & retrieval
- Permissions & citations
- Evaluation & deployment
Ask another question…↵
03Agentic workflows
Intelligence.
Put to work.
Connect useful reasoning to the tools your business runs on. Validate the actions. Keep people in control. Plan for the unexpected.
- Tool & API integration
- Human approval boundaries
- Monitoring & recovery
12 / 13 caller scenarios passed on the first run, in our open-source reference build.
Order #4821
“My order arrived damaged. Can I get a refund?”
photo_1.jpgphoto_2.jpg
Reply sent
Approval needed · refunds over £50
Refund £84.00 to card •• 4242 for order #4821
Waiting for the agent…
Recent work
Court judgments, turned into data a legal product runs on.
- 23
- pipeline steps, run from one config
- 955
- judgments in the first full volume
- 39
- fields extracted per case
Rules first, a local LLM only where the text needs reading. Raw data is never touched.
A manual 6–8 hour ingestion job, now running on its own.
- 6–8 h
- manual Excel job, now automatic
- Every
- exchange between systems logged for audit
- Failed
- records set aside with the reason, not lost
Apache NiFi · MongoDB · PostgreSQL · Keycloak
BigQuery to Snowflake, with proof the data survived it.
- SQL
- generated on the fly to check data is complete and correct
- Airflow
- schedules running every validation and data move
- Live
- dashboards tracking transfer and validation
Python · Snowflake · dbt · BigQuery · Airflow
Spreadsheets and forms, turned into tools a team runs on.
- Self-updating
- reports that used to be assembled by hand
- 1
- form-based inventory instead of scattered sheets
Power BI · Google Apps Script · Python · SQLite
An AI receptionist that books real slots and never decides what is true.
- 12 / 13
- caller scenarios passed on the first run
- 1.4 s
- median reply on a recorded call
- $0.05
- model cost for a two-minute call
Open source, built around a fictional clinic.
“[He] also demonstrated great initiative by designing an automated validation system to ensure data consistency between Google and Snowflake migrations. This system proved invaluable in maintaining data integrity throughout the migration process.”
HOW WE BUILD
How we build. Beyond the demo.
01 — 05
- 01 / UNDERSTAND
Define the problem.
Inspect the constraints. - 02 / ENGINEER
Build one complete path.
Test the difficult cases. - 03 / OPERATE
Make failures visible.
Leave a system you can own. - Inside our approach ↗
[He] also demonstrated great initiative by designing an automated validation system to ensure data consistency between Google and Snowflake migrations.
HOW WE BUILD
FROM FIRST PRINCIPLES TO DAILY OPERATIONBuilt to work.
Beyond the demo.
Inside our approach ↗Define the problem.
Inspect the constraints.
Build one complete path.
Test the difficult cases.
Make failures visible.
Leave a system you can own.