A VARLIS™ project

Real-time AI guidance for real technical work.

Project Parallax is VARLIS's AI-guided mixed-reality system for hands-busy procedures: recognise the real instrument, align a digital model, guide the next approved action, verify defined checks, and record the workflow outcome.

Mixed reality Quest 3 prototype Android XR / Aura
Prototype capture
The idea

Put on the glasses. Complete the procedure with expert guidance.

Operator at an analytical balance with a mixed-reality overlay showing the weighing workflow steps, live balance checks, and AI confidence AI concept render

Glasses that recognise the equipment in front of the operator, overlay the next approved action, and verify defined physical checks before the workflow progresses. The operator stays in control; the approved procedure leads.

  • Real-time, in-the-moment assistance - not training for later
  • Extends expert guidance to where an expert isn't on site
  • Proven first on analytical instruments; the idea is far broader
Already working

A live mixed-reality build on Quest 3 today.

The prototype already demonstrates the core loop: recognise the balance, align a 3D model to the real device, ask for hands, check glove state, prompt when a requirement is missing, and proceed only when the operator is ready.

Project Parallax mixed-reality guidance concept: an analytical balance moving through a recognise, align, guide and verify workflow with an AI checklist confirming each step AI concept render
Quest 3 prototype

See once, then track.

After registration, continuous vision is not the goal. The system knows where the instrument is and tracks hands relative to known geometry, invoking vision only for brief, specific checks.

  • Balance recognition and model alignment
  • Glove / PPE prompt and confirmation
  • Spatial workflow guidance in the operator's view
  • Next target: full calibration with step-level assessment
Domain advantage

Built from inside the instrument.

The VARLIS founder, gloved, working hands-on inside an analytical instrument in the laboratory
Science, engineering, and software

Not an external software idea applied to an unfamiliar laboratory.

Parallax is being built by a PhD chemist and analytical-instrument engineer trained directly on major manufacturers' hardware, with pharmaceutical laboratory experience and hands-on knowledge of the systems behind the panels.

That background makes it possible to model more than an instrument's appearance: how its components behave, where procedures fail, which cues matter to the operator, and which decisions must remain inside approved procedural control.

Architecture

See briefly, then track. Deterministic logic, separate AI layer.

Parallax separates approved procedure logic from contextual AI assistance. The workflow defines what must happen and what permits progression; AI can explain and retrieve in context without changing that path.

01 Recognise

Find the instrument

Small vision models identify the instrument and the key features needed for spatial registration.

02 Register

Align the model

A 3D representation is matched to the real object, with confirmation and fine-tuning available to the operator.

03 Gate

Verify each step

Procedure data defines the instruction, verification rule, and progression gate for each approved action.

04 Assist

Answer in context

A separate AI guidance layer can answer questions from approved content without changing the validated workflow path.

The wider system

From understanding one instrument to understanding the laboratory.

Parallax begins where digital records meet physical work. Its longer-term direction is a shared spatial and semantic representation of the laboratory: what equipment is present, how its components relate, what procedure is underway, what observable state the instrument is in, and what evidence each action produces.

Physical context

Spatial model

The laboratory, instruments, components, hands, samples, consumables, and tools represented as related objects.

Observable context

Instrument state

Readings, configuration, calibration, and maintenance signals where approved interfaces or sensors permit.

Controlled context

Procedure state

The approved SOP version, current step, required checks, progression gates, and authorised exceptions.

Traceable context

Results & evidence

Operator actions, readings, outputs, timestamps, exceptions, and provenance joined to the work that produced them.

Shared laboratory context

One model of what is here, what state it is in, and what is happening now.

Contextual AI can retrieve, explain, compare, and guide across this model. The deterministic procedure layer remains authoritative, and the scientist remains responsible for every physical action.

Human interface Project Parallax

Guidance anchored to the real instrument, delivered in the operator's view at the moment it is needed.

A staged path from prototype to product category.

01 Working now

One instrument, core guidance loop

A Quest 3 prototype for balance recognition, model registration, PPE checks, and spatial guidance.

02 Next product milestone

One procedure, deeply connected

One deeply modelled instrument and approved procedure connected to defined operational readings and a coherent workflow evidence record designed to support auditability.

03 Longer-term direction

Laboratory-wide context

People, instruments, procedures, results, and compliance evidence connected through one AI-assisted spatial and semantic interface.

The objective is not AI independently controlling the laboratory. It is AI that comprehends the current laboratory context and helps the scientist make the correct next decision.

Platform & port

Built on Quest 3, aimed at optical see-through glasses.

The existing Unity prototype gives the project a practical base. The next step is an Android XR / XREAL Aura version tuned for optical see-through display, camera availability, and enterprise deployment constraints.

Engine

Unity, a port not a rebuild

The prototype is already Unity, a first-class Android XR route via OpenXR. The procedure engine, AR UI, model-alignment logic, and content approach carry across.

Needs the device

Provider, see-through, camera

The device-specific work is the Android XR provider swap, re-tuning overlays for optical see-through, and validating the brief camera-capture checks.

The target

Pilot-ready enterprise app

The aim is a pilot-ready Android XR application for a validated workflow, delivered through enterprise or managed distribution.

Safety & compliance

Assistance, not control - and auditable.

For laboratories and regulated technical work, the line is deliberate: the software supports the user and records workflow events; it does not operate the instrument on the user's behalf.

By design

The human always acts.

Parallax can tell the operator what comes next, point to the right physical area, and confirm that a defined condition appears to be satisfied. It does not press buttons, change parameters, or send commands to the instrument.

Built for the lab

Auditable and private by design.

The intended record is a workflow event trail: what step was reached, what check passed, what prompt was shown. The default design avoids retaining continuous camera footage unless a customer policy requires otherwise.

The scope

Analytical instruments are the proving ground.

The first workflow is a balance calibration path because that is where the domain expertise and prototype are strongest. The same pattern extends to other work where a complex procedure must be completed correctly and an expert is not always on site.

Lab & pharma

Instrument setup, calibration, sample preparation, and compliance checks.

Field service

Guided installation, maintenance, and escalation support for approved service tasks.

Manufacturing

Assembly and maintenance procedures checked in real time.

Regulated work

Future applications subject to domain validation, risk assessment, and appropriate partners.

Have a procedure that needs real-time guidance?

Project Parallax is being shaped toward pilot-ready Android XR deployment. The strongest first conversations are with instrument manufacturers, technical-training teams, and labs with high-cost procedural errors or scarce expert availability.

Discuss a pilot