NVIDIA Inception Program
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14 OctMADE Competence CenterWe spoke at MADE, Politecnico di MilanoWe spoke at MADE, Politecnico di Milano: “Beyond Dashboards”, with Miraitek

No more reports. Just decisions.

Everyone has data. The wall between data and decisions is thicker than ever. We configure squads of agents that map your processes and take it down.

  • Rows to act on
  • Numbers re-checked on the source
  • Sandbox in Europe
DECISIONS READY17 TO ACT ONPREPANALYSISCHECKSDECISIONSSTARTDECISIONSdelYOUR SQUAD OF AGENTS
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Delo

Introducing Delo

A squad of agents on your data. It works like a team and comes back with decisions.

Delo is a squad of agents that works on your data like a team of analysts. Each one has a role: a cost analyst, a maintenance lead, an ML expert. All of it in an isolated sandbox, on the real data.

What you get is not a report to read but a list of decisions: each one with the rows to act on (the real identifiers), the value in euros when the data allows it, who found it and who should receive it. Usually in about 15 minutes, 30 at most.

Why “Delo”? Davide, Edoardo, Leonardo, and an o. We chose it the day before walking on stage at MADE: we had to call it something. The talk at MADE →

What one decision looks like

Not a chart to interpret. A row to act on.

  1. The rows to act on, with real identifiers
  2. The value in euros
  3. Who found it and who challenged it
  4. Who should receive it
ActionNowMaintenance

Roll back firmware 2.3.1 on the 38 M-24 compressors.

SerialSiteOverloads · 30 dLast overload
M24-0117Plant 227Oct 26, 04:12
M24-0203Plant 224Oct 27, 22:40
M24-0088Plant 119Oct 25, 13:05
M24-0141Plant 318Oct 27, 06:51
34 more rows CSV
Value
€46,300
unplanned stops avoided, 12 months
Found by
Maintenance leadchallenged byDevil’s advocate
Verified · serials re-read from source

Send to Head of maintenance

How a squad works

Two waves, a judge, a narrator.

Every member has a role. The first wave looks for facts; the judge takes stock; the second wave challenges them; what holds becomes a decision.

  1. data lake

  2. FIRST WAVE

    • Energy manager · Finds facts
    • Data guardian · Checks the data
    • Senior Data Scientist · Statistics, with confidence
    • ML expert · Models that beat a guess
  3. Judge takes stock

  4. SECOND WAVE

    • Devil’s advocate · Challenges
    • Cost analyst · Costs and waste
    • Energy lead · Costs and waste
    • Maintenance lead · Reliability and maintenance
  5. Judge decides what goes in
  6. Narrator writes the summary
  7. decisions

See it work

One investigation, line by line

  1. 0:00❯southwind investigate blackbox.parquet --lens overloads
  2. 0:01e2bsandbox up · network off · /data read-only
  3. 0:04playbook1,218,604 events · 412 compressors · baseline KPIs
  4. 0:05Orchestrator: 4 weak signals, 4 sandboxes
  5. 0:05sandbox-1 · firmware 2.3.1✓ following
  6. 0:05sandbox-2 · night shift· following
  7. 0:05sandbox-3 · temperature· closed
  8. 0:06sandbox-4 · operator· closed
  9. 0:06sandbox-1 · hypothesis: overloads follow a firmware update
  10. 0:07ev = pd.read_parquet("/data/blackbox.parquet")
  11. 0:07ovl = ev[ev.event == "OVERLOAD"]
  12. 0:08by_fw = ovl.groupby(["model", "fw"]).serial.nunique()
  13. 0:08by_fw.sort_values(ascending=False).head(3)
  14. model fw
  15. M-24 2.3.1 38
  16. 2.2.0 4
  17. M-31 2.3.1 3
  18. Name: serial, dtype: int64
  19. 0:15Counter-check: same effect on other models?
  20. 0:19M-31, M-12 on firmware 2.3.1· no increase
  21. 0:24Grounding every number the agent cites
  22. 0:2638 compressors · serials re-read from source✓ match
  23. 0:27×9 overloads per compressor✓ match
  24. 0:28Night shift +40%✗ dropped
  25. 0:31report.json written3 actions · 1 claim dropped

report.json

01 / The discovery

Overloads concentrate on M‑24 compressors running firmware 2.3.1.

×9overloads per compressor after the update

✗ Night shift +40%: not reproducible on source rows

✓Finished in 31s

How an investigation runs
A finding on 412 industrial compressors

How Delo works

From dataset to decision, in four steps

Connect a dataset, let the agents explore, keep only what survives verification.

Pick a dataset

ERP exports, telemetry, warehouse and sales tables. No migration, no new dashboard.

Every source in one catalog

Excel, CSV, Parquet, Postgres or Snowflake: profiled and documented before any analysis.

Use cases

Built for the data that runs operations

See which quotes close, and why.

Quotes, discounts, response times and customers, cross-checked to find where deals slip away.

Book a demo

Questions agents ask on their own

  • Response time

    How much win rate does a slow reply cost?

  • Discounts

    Do bigger discounts actually close more deals?

  • Margin

    Which won quotes quietly erode margin?

  • Dormant accounts

    Who keeps asking for quotes but stopped ordering?

Go deeper

Found a weak signal? Dig into it.

Every report opens a conversation. Ask why, compare periods, drill into a site: answers are computed on the same data, with the evidence attached. And your questions steer the next runs, so the orchestrator learns where to look.

Get a personalised walkthrough

We run Delo on a dataset you choose and walk you through what it finds.

Book a 15-minute demo

The question we always get

“Sure, but what about my data?”

Fair question. Here’s where it runs and which model gets to see it.

It stays in Europe.
The code that justifies every decision runs in an isolated sandbox in Europe. Agents install nothing and start no other agents; the internet is reachable only from the sites your squad allows.
You pick the model.
The architecture is model-agnostic: Amazon Bedrock in your own AWS tenant, the big AI labs under a dedicated agreement, or models running on your side, down to a tinybox under your desk.
Checks before publishing.
Automatic checks run before an analysis is published: every figure in the summary must be in the report and every euro value must tie back to its rows. What fails is corrected or flagged for review.
Read our privacy policy →

Zoom in on one agent

“Fine, but how does an agent forecast?”

Take the forecaster. Its lead is a question like “how many stops next month, per line”. It pulls the series from the data lake and, inside the sandbox, has the best time-series foundation models at hand, Chronos-2 by Amazon, TimesFM-3 by Google and Timestral by Mistral among them. It tries them on the past, keeps the one that errs least, and hands back the forecast with how far to trust it.

15 OctClaudeSouthwind is part of Claude for Startups

Programs and stages where we’ve presented Southwind

Blog

What we learn building agents that investigate on their own

  1. Jun 16, 2026

    Case study: hidden signals in a warehouse

    One public warehouse dataset. Seven investigative branches. Five operational risks that only made sense together.

    Case study · 7 min read

  2. Jun 09, 2026

    Data problems do not respect org charts

    Companies divide ownership by team. Data moves through relationships. The expensive problems appear in between.

    Essay · 2 min read

  3. May 20, 2026

    Non-determinism is not a bug

    Exploration is not noise. It is how an autonomous system finds the path nobody predefined.

    Essay · 2 min read

  4. May 12, 2026

    From monitoring to understanding

    A shift from reactive monitoring to systems that investigate every hypothesis in the background, at the speed data changes.

    Essay · 2 min read

  5. Apr 13, 2026

    The perimeter problem

    Every dashboard draws a boundary around what gets seen. The risk is everything useful that falls outside it.

    Essay · 3 min read

Start with your data

A focused demo on a dataset you choose. Then a two-week sprint on your real data.

Try Delo on your dataBook a 15-minute demo