Our technology · what's actually behind the answers

A curated expert system — not a chatbot with a costume.

Every dxpert capability — the chat, the diagnostic, the free tools, the UNS Generator, the agents — draws on the same core: a curated AI model grounded in a governed knowledge graph dedicated to digital transformation, Industry 4.0/5.0, and AI for industrial competitiveness.

A modern industrial control room with unified data displays
The problem with generic AI here

On this topic, the average answer is a failed answer.

Frontier AI models like Claude and ChatGPT are trained on the mean of everything ever written. For most topics, that's fine. For industrial digital transformation, fifteen years of mostly-failed programs means the mean of the literature is the failure — the vendor decks, the stalled pilots, the reference-model posters.

dxpert takes the opposite bet: reverse-engineer the small set of operators who actually succeeded, encode what separated them from everyone who stalled, and let the model answer from that — and only that.

The core asset

A governed knowledge graph, dedicated to one domain.

Hundreds of interlinked, versioned knowledge nodes — curated by review, not scraped by crawler. It is a living asset: continuously extended and revised as the evidence base grows.

A dense network graph of glowing knowledge nodes in domain clusters, linked through bright amber hub nodes
ARCHITECTURE

What good looks like

A practical, data-centric Digital Platform architecture — connect & collect, contextualize & normalize, expose the now, expose the past — with the Unified Namespace as the spine and the standards (ISA-95, OPC UA, MQTT/Sparkplug, AAS, IEC 62443) as support models, never the architecture.

PATTERNS & ANTI-PATTERNS

What separated winners from the stalled

Distilled from studied exemplar operators — the moves that compounded, and the failure patterns that repeat so reliably you can name them from across the room.

PLAYBOOKS

How to actually execute

Sequenced, costed execution paths — how to fund a foundation, in what order to build it, and how to keep OT/IT convergence governed instead of political.

Every node is reviewed before it enters the graph, versioned after, and revised when the evidence changes. Nothing gets in for being popular; things get in for having worked.

The design principle

Deterministic where it must be. Generative where it helps.

Industrial credibility dies the first time a tool improvises. So each capability uses exactly as much AI as its job can tolerate — and no more.

CapabilityWhat runs it
Ask dxpert (chat)The curated model, grounded in the knowledge graph. Answers come from the architecture and the evidence behind it — vendor-neutral by construction, because there's nothing in the graph to upsell.
Free diagnostic & agent APIA deterministic scoring engine built on the same architecture: identical input, identical verdict, every time. Safe for your agents to cache and compare. API docs →
Board-ready roadmapThe method behind the diagnostic, extended: your inputs scored and synthesized by the engine, sequenced into a fundable roadmap — and the first paid step, ahead of the generator and agents below. Commission roadmap →
Free validatorsStandards grammars (Sparkplug B, UNS conventions) encoded as deterministic rules that run in your browser. No model in the loop, nothing uploaded. Try them →
UNS GeneratorThe architecture as code: your equipment hierarchy in, the four platform capabilities out — standard topics, Sparkplug mapping, payload schemas, deployable pipeline export. Buy export →
AgentsAI agents that consume the generator's schemas as a hard contract — grounded in your namespace, not in vibes. Get agents →
Governance

Built so you can trust it with an expensive decision.

Why you can trust thisPut it to the test — ask dxpert