Reasoning Readiness

Is your operational data reasoning-ready?

Most industrial AI can answer a question. Reasoning AI has to understand the asset first. We assess whether your telemetry, documentation, and asset context can support that — before you build on top of it.

tempo readiness scan
$
△Historian
✓SCADA
✓Asset hierarchy
✗Documentation links
✗Engineering semantics
△Metadata quality
Reasoning readiness 0%
Recommendation: assessment recommended.
Book your readiness assessment → See the maturity model

Why readiness

Answering isn't understanding.

Most AI tools pointed at your historian will happily produce an answer. They'll explain a vibration spike, extrapolate a forecast, or summarize a fault log — without ever checking whether that answer is consistent with what the asset can actually do.

Reasoning AI needs more than a data feed. It needs to know what's connected to what, what documentation governs the asset's real limits, and which signals can be trusted when one degrades. Most industrial data estates were never built with that in mind — telemetry lives in one system, documentation in another, and asset context nowhere at all.

The Reasoning Readiness Assessment tells you, concretely, which of those foundations you have — and what it would take to close the gap.

Most AI can answer. Reasoning AI must understand.

Maturity model

Six stages between disconnected data and agentic operations.

Readiness isn't binary. It's a path — most industrial organizations sit somewhere in the middle, with strong telemetry and weak semantics, or the reverse. The assessment places you on this path and shows what the next stage actually requires.

Disconnected data
Connected telemetry
Asset context
Operational knowledge
Reasoning layer
Agentic operations

Engagements

Three tiers, each building on the last.

Start with an assessment. Move to the TEMPOral knowledge layer once you know where you stand. Bring us in for RunnerTau — our time-series reasoning models, optionally fine-tuned for your application — when you're ready to run reasoning on your own assets.

TIER 1

Reasoning Readiness Assessment

"Can your existing stack support reasoning AI?"

  • Current architecture review
  • Data landscape assessment
  • Documentation maturity
  • Asset knowledge assessment
  • Gap analysis
  • Use cases ready today
  • Use cases that will fail — and why
  • Readiness score
Output A practical report showing where reasoning can deliver value today and what stands in the way.

2–3 weeks

TIER 2

TEMPOral Knowledge Layer

Everything in the Assessment, plus the knowledge layer that makes you agentic-ready.

  • Reasoning-ready target architecture
  • Auto-constructed knowledge / topology layer (e.g. ISO 15926)
  • Documentation strategy
  • Integration priorities
  • Data governance recommendations
  • Phased implementation roadmap
  • Prioritized use cases
  • Business case
Output A production TEMPOral knowledge layer, and a company-wide plan for building on it.

4-6 weeks

TIER 3

RunnerTau for Your Application

We bring RunnerTau to your assets — optionally fine-tuned for peak performance.

  • Data integration
  • Knowledge layer construction
  • Asset schema modelling
  • Documentation ingestion
  • Semantic mapping
  • RunnerTau deployment (7B or 4B)
  • Optional fine-tuning for your application
  • Validation and initial reasoning workflows
Output RunnerTau reasoning over your own assets, on a production-ready knowledge layer.

Scoped after Tier 2

Where this leads — general agentic reasoning readiness

This assessment prepares your data foundation for TEMPO and RunnerTau, our industrial reasoning agent and time-series reasoning models — or for any other reasoning agent you may already have in your ecosystem. Tier 3 builds the same reasoning layer that TEMPO — or any agent — runs on, so the foundation carries directly into whichever path you choose.

Find out where you stand.

Book a readiness assessment, or ask us a question first — either way, we'll respond directly.

We respond to every message.