TECHNOLOGY
As conversation is becoming the interface, time‑series data is Industrial AI’s major blind spot
CONVERSATIONS
& AGENTS
User interface
Industrial intelligence’s major roadblock
INTELLIGENCE
LAYER
STORAGE
/ INFRA
Industrial data: raw time series / historians
Spocky VS Claude Opus 5.5
77 devices queried on historical data
Swipe to compare →
Answer time
Tokens sent to the LLM
Cost per question
~ estimated past the LLM's context limit.
The architecture
15+ years of expertise led to this winning architecture
The LLM orchestrates. The engine executes.
Why it works in production
Built for industrial data, not for demos
Conversation = open question + full history in seconds
Fast and deep is easy to claim. Check it yourself.
Ask the live demo anything: years of weather history, and how Spocky handles your data.
01
Up to 90% fewer tokens
The maths runs in the engine, next to the data. Only 10–20 points ever reach the LLM.
02
Deterministic & explainable
Every answer comes from code you can read, re-run and audit.
03
Full history, on the fly
Years of raw data at full resolution, across every asset, answered in seconds.
04
Cloud, hybrid or on-prem
Data-source agnostic. The LLM never holds credentials or a connection to your systems.
The engine
- Warp 10
- Open source since 2012
- 1,300+ time-series functions
- 50+ patents
Ready to see what your data can tell you?
Book a 30 min call (opens in a new tab)Security & deployment
Your data stays where it is
Spocky goes to your data. Your data never goes to the LLM.
Our cloud, yours, or on-prem
Including inside your own cloud tenant.
Read-only by default
Writing back to CMMS, ERP or devices is opt-in, with a person confirming.
The model never connects
The LLM holds no credentials and no connection to your systems.
Your security team needs the details? Request the security pack (opens in a new tab)
How a pilot runs
From first question to live agent in 3 weeks
No six-month data project first. We start from your data as it is today.
Week 0
Pick one use case
The questions your team asks every week, and what a good answer saves.
5 questions and 1 KPI agreed
Week 1
Read-only access
To the historian, database or files you already have.
First answers on your data
Week 2
Your context
KPIs, thresholds and your experts' rules, saved as reusable skills.
Answers in your units
Week 3
A live agent
Running in your team's routine. Then the next use case.
Agent live, every morning at 8:00
Typical when the data already sits in a historian or a database. Your data team keeps control of quality.
See it answer on a real machine
Pick a live demo on public data, then bring the question your team asks every morning.