Meet TAI - Industrial AI grounded in your digital twin
TAI · AI Infrastructure

The brain behind the connected worker.

TAI bridges the worker, the digital twin, IoT, and your knowledge bases, across web, mobile, and AR. Grounded in your documents, cited on every answer, and deployable wherever your data must live.

Outcome · Productivity
80%
Productivity uplift with a connected-worker solution
Outcome · Frontline
70%
More productive frontline workers with digital tools at hand
Outcome · Quality
50%
Fewer workplace errors thanks to immersive guidance

Six ways TAI works your twin, one async backbone.

Chat, Tagging, Knowledge, Decision, Vision, and On-site AR share the same performance plumbing, async-first by design, so heavy AI work never blocks the worker.

TAI Chat

Conversational facility intelligence, a grounded assistant scoped to your twin, with multi-turn tool use and file grounding.

Schema-aware queries PDF / P&ID grounding Typed viewer actions

TAI Tagging

Automated asset recognition inside the twin, 3D segmentation, OCR, and identifier extraction that maps every asset to your enterprise hierarchy.

3D segmentation OCR from signage Maps to Functional Locations

TAI Knowledge

Turns documents and expert video into step-by-step work instructions, flows, and training checks, anchored to real 3D coordinates on the asset.

Multimodal ingestion Per-step positioning Anchored to the twin

TAI Decision

Watches operations and recommends the next action with the evidence behind it, the alert, the trend, the history, and the procedure, ready for a supervisor to approve into a work order.

Anomaly detection Evidence-backed Human-in-the-loop

TAI Vision

Reads a photo of a leak, gauge, or worn part, grounds it to the right asset from where you stand in the twin, and returns a likely diagnosis, without bluffing a spec it can’t verify.

Image understanding Grounded to the asset Ready-to-log WO

On-site AR

Puts the same intelligence on the floor, look at the real pump through the AR app and TAI knows exactly which asset it is and what’s open on it.

AR overlays Asset recognition Hands-free

One conversational surface, three integration lanes.

One brain orchestrating files, structured data on your facility, and live sensors.

Connected Worker Tablet · AR · Mobile
TAI · the brain
Chat Tagging Knowledge Decision Vision On-site AR
Digital Twin + Plans
IoT Telemetry
SAP / Knowledge Bases

Grounded by design, guarded by default.

Layered defenses on every AI call, so the assistant can read your facility, and nothing else.

Database
Curated SQL allowlist with a read-only database role, writes are blocked at the query layer and at the role itself.
Prevents data injection
Tooling
Statement timeouts, row caps, and result-size limits on every tool call.
Prevents runaway context
Storage
Presigned, scoped asset access, AI workers never hold long-lived credentials.
Least-privilege access
Citations
Every answer cites the SOP, drawing, or asset record it came from.
No invented procedures
Tracing
Correlation IDs propagate through every queue job and downstream AI call.
End-to-end observability

Heavy AI, without the wait.

Queued & prioritised

  • Per-surface job queues with independent concurrency, heavy 360°/3D work never starves chat.

Push, not poll

  • Per-job channels deliver results the moment they finish, no client retries, no wasted requests.

Read-replica isolation

  • AI queries run on dedicated read replicas, your operational database never feels the load.

Scoped media pipeline

  • Asset manifests are presigned and time-limited; media is resampled on the fly per device.

Cloud, region-pinned, or fully air-gapped.

We architect against capabilities, not vendors, every provider is swappable behind its service interface, and every cloud component has a self-hosted equivalent.

Pluggable AI providers

Reasoning, multimodal, segmentation, and 3D-generation models each sit behind a service interface. Models and vendors swap without touching queues, workers, or product surfaces.

Local AI + local spaces

On-premise, local language models replace hosted ones behind the same interface, and in-house Gaussian Splats serve the spatial layer entirely on your network. Nothing leaves the building.

Same code, swapped deps

The platform deploys identically on Docker in your data centre, storage, queues, monitoring, and secrets all map to self-hosted equivalents.

Air-gapped Docker-native Same interface No SaaS dependency

See TAI on your own facility.

Bring a P&ID, an SOP binder, and a hard question. We’ll show you a grounded answer.

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