UNIVERSAL MULTIMODAL MAINTENANCE ASSISTANT

The repair manual that talks backhands-free, on the shop floor.

FixSense turns thousand-page manuals and hours of training video into a voice-first AI mentor. Ask out loud while your hands stay on the job — get the spec, the diagram, and the exact video moment, in seconds.

4-layer
Multimodal architecture
100%
Officially-licensed data
2 interfaces
Technician & enterprise
immersive_copilot.session BAY 04 · NOISE 86dB
Technician · voice "What's the torque on the front caliper bracket bolts for this model?"
FixSense · grounded answer

Front caliper bracket bolts — tighten in a cross pattern, two stages. Source: authorized service manual, §Brakes 4.2.

110N·m ±5 · final stage
Air-drop · caliper removal 04:12 / 11:38
JUMP → 04:12
Grounded in licensed data from Authorized manuals Product manuals Industry standards Annotated field capture
The status quo

Frontline knowledge is trapped in the wrong formats.

Senior expertise doesn't scale. The manual is thousands of pages, the video runs long, and the technician's hands are already busy.

PAIN / 01

Inefficient knowledge carriers

Traditional manuals exist as thousand-page PDFs and training videos are time-consuming, leaving frontline information retrieval extremely slow.

PAIN / 02

Talent development dilemma

A shortage of senior technicians meets long onboarding cycles — the traditional master–apprentice model is severely limited in efficiency.

PAIN / 03

Constrained work environment

On-site, technicians have limited use of their hands and face high-noise interference — making it hard to consult paper or electronic materials while working.

One platform, two interfaces

Built for the person on the floor and the team that runs it.

The technician gets a voice-first copilot. The enterprise gets a knowledge factory and skills evaluation. The same knowledge base underneath.

Technician interface

Immersive maintenance copilot

Pure voice interaction and real-time Q&A. Ask while you repair — the system pushes relevant drawings and multimodal information in the moment.

  • Pure voice interaction — hands stay free
  • Precise video retrieval & "Air-drop" to the key timecode
  • Fault-simulation training with Socratic feedback
Enterprise interface

Knowledge factory & evaluation

Batch-upload PDFs and video to rapidly build an enterprise-exclusive AI knowledge base — then scientifically evaluate skill levels and learning progress.

  • Knowledge factory — batch ingest, auto-built base
  • Comprehensive data-asset management
  • Evaluate employee skill level & learning progress
Under the hood

Four layers, from spoken question to grounded answer.

The Knowledge Refinery is the core technical barrier — a multimodal ETL pipeline aligning manuals, diagrams, and video on both the time axis and semantic space.

InteractionLAYER 01
Web Dashboard for the enterprise and Mobile / Tablet App for the technician, supporting multimodal input such as photos and voice.
Web DashboardMobile / TabletVoice + Photo
Agent CoreLAYER 02
Intent recognition, tool calling (video-playback tool, test generator), and workflow orchestration for the teaching agent.
LangChainLangGraphTool routing
Knowledge RefineryLAYER 03 · CORE BARRIER
Multimodal ETL: OCR, table & circuit-diagram extraction, Whisper ASR speech-to-text with LLM keyframe recognition — aligning time axis and semantic space.
Whisper ASROCRKeyframe recognition
Data LayerLAYER 04
Hybrid knowledge base — a Vector DB for semantic recall plus a Knowledge Graph for complex component logic and fault causality.
Pinecone / MilvusNeo4j Graph
The defensible asset

In the multimodal RAG era, compliant & scarce data is the moat.

Four pillars of licensed, authoritative data — the underlying technical documents that ensure every answer is legal, authoritative, and standards-compliant.

PILLAR 01

Officially authorized manuals

Data access only with brand authorization — legal, authoritative source documents via deep cooperation with authorized maintenance centers.

PILLAR 02

Product user manuals

Broad consumer and commercial product guides and instructions, building a solid foundational knowledge base.

PILLAR 03

Industry standards

Commercially procured specifications and standards, so all guidance complies with safety and operational guidelines.

PILLAR 04

Exclusive annotated capture

High-standard annotated datasets from the training system, plus automated collection via robots — data that self-evolves.

The path to the technical skyline

Three stages: foundation, validation, then a vertical model of our own.

Stay agile while accumulating the core technology that becomes the data flywheel.

01
MVP · est. 1–2 months

Data foundation

Multimodal knowledge-base construction

Fully advance the automated multimodal ETL pipeline, aligning manuals, video, voice, and industry standards on both the time axis and semantic space.

GoalRun the minimum closed loop: upload manual → RAG Q&A — currently supporting text and simple images.
02
Video integration · est. 3–4 months

Commercial validation

MVP on top-tier third-party VLMs

Pair leading third-party Vision-Language Models with the scarce knowledge base from Phase 1 to build the MVP quickly, run commercial trials, and gather real feedback.

GoalIntroduce the video pipeline for "Air-drop"; develop the technician-side voice-interaction prototype.
03
Pilot promotion · est. 5–6 months

Technical skyline

Exclusive vertical model & performance leap

Decouple from third-party general models. Pre-train and fine-tune FixSense's exclusive vertical-domain model, optimized for edge inference in weak- or no-network repair-shop environments.

GoalPerfect the enterprise backend; onboard seed users for RLHF so experts correct data into the model — forming the data flywheel.

Lay the foundation for the industrial maintenance brain.

The most efficient path from technology to the shop floor. Bring FixSense to your team.