AI Engineering
Production AI, Engineered into Instruments
Tismo engineers production AI into instruments and connected systems across software, firmware, electronics, cloud, and edge platforms. Work spans on-device inference on the instrument's own microcontroller, machine learning on images, spectral data, waveforms, and time-series signals, predictive maintenance, and AI-based support and automation. The model layer is swappable, hosted or locally hosted open-weights, and every deliverable carries validation, explainability, and regulatory traceability for medical, life-sciences, and industrial instruments. All project IP belongs to the client.
Any team can train a model. Shipping one inside an instrument, through firmware, electronics, regulatory review and a decade of service life, is product engineering. We work with OEM engineering teams to put AI where it earns its place: on the instrument, at the edge, in the cloud, and in the workflows around it.
Where a classical algorithm solves the problem, we use it. An LLM is one tool in the box, not the default answer. AI is integrated as part of the core product architecture, across software, firmware, electronics, cloud and edge, so that intelligence stays predictable, testable and maintainable over the entire lifecycle of the instrument.
Where AI Earns Its Place
More than a dozen delivered projects are AI/ML systems, engineered with the validation and traceability production demands.
In your products
- On-instrument ML: analysis running on the device itself
- IoT and edge AI agents
- MCP servers that expose instrument capability to AI clients
In your operations
- Predictive maintenance and remaining useful life
- Anomaly detection across instrument fleets
- Workflow automation: measurement, analysis, reporting
In your customer support
- Support automation over product knowledge, logs and case history
- AI-guided troubleshooting for field engineers
- Ticketing and CRM integration
Your Data. Your Models. Your IP.
The first question OEMs ask is the right one: where does the data go, and who owns what comes out?
NDA first
Signed before we hear the idea, before any project discussion, any data, any documents.
You own everything
All project IP (models, training pipelines, source code and documentation) belongs to you. Always.
Runs where you need it
Deployment follows your data policy: hosted models under enterprise terms, your own cloud tenancy, or locally hosted open-weights models where the data cannot leave your site.
Scoped and audited
Engineers work under access agreements scoped to the engagement, inside the ISO 9001 / ISO 13485 quality system.
AI in the Architecture, Not Bolted On
Intelligence is designed in at system level, in three stages: Your Data to AI Model & Agents to AI-Powered Apps. The model layer is deliberately swappable: LLM-agnostic, hosted or self-hosted to suit your data policy.
Advanced Analysis of Images, Spectra, Waveforms
Analytical, medical and industrial instruments generate high-value data that classical processing leaves on the table. We apply ML grounded in domain knowledge and signal behaviour.
Our analysis capabilities include:
Images
Detection, segmentation and classification: from microscopy and machine-vision inspection to defect detection on production lines.
Spectra
Spectral interpretation using multivariate and statistical techniques: chromatography, spectroscopy, mass spectrometry.
Waveforms and time series
Pattern recognition and anomaly detection in vibration, ECG, acoustic and process signals.
Feature engineering
Feature extraction and dimensionality reduction, so results hold up across units, lots and operating conditions.
Where appropriate, we design human-in-the-loop systems to support assisted interpretation and controlled decision-making.
Embedded AI / ML on the Instrument
Embedded ML splits into two phases: the model is designed on the PC, then deployed onto the instrument's own microcontroller.
Design phase - on the PC
Prepare the data, develop the model (typically a 1D CNN), then train, validate and test it against real instrument data.
Deployment phase - on the device
Port the trained model to C/C++, optimise it for the target MCU, then calibrate and deploy it on the instrument.
Inference runs on the instrument itself: no cloud, no latency, and no data leaves the device.
Support, Agents & Automation
Beyond the instrument, AI improves the workflows around it. We build these systems for clients, and run them ourselves first.
SalesHub
A generative-AI agent for sales teams that answers from product documentation and history, instead of tribal knowledge.
TADA
A GenAI diagnostic agent for service engineers, guiding instrument troubleshooting from manuals, logs and case history.
InstaReply
RAG-based auto-replies for service emails, grounded in the product knowledge base and reviewed before they go out.
These capabilities enable scalable support models and more responsive field operations, while maintaining system security and reliability.
Predictive Maintenance with ML
Instruments can continuously assess their own health, combining sensor data, logs and operational history to catch issues early and accurately.
Key capabilities include:
Drift and degradation
Detection of abnormal operating behaviour before it becomes a failure.
Failure modes
Classification of failure modes and root-cause indicators.
Remaining useful life
RUL estimation from observed degradation patterns.
Recommendations
Maintenance actions driven by what the data shows, not the calendar.
These solutions reduce unplanned downtime, improve service efficiency, and extend the operational life of instruments deployed in the field.
How We Engineer AI
Grounded in established engineering principles and product realities.
Architecture first: model boundaries, data flows and failure modes are engineering decisions made up front, not an afterthought add-on
Domain-grounded models: models are informed by measurement physics and process behaviour, not trained blind on whatever data exists
Validation and traceability built in: test sets, acceptance criteria and explainability are part of development, with an engineer in the loop and an audit trail
Engineered for the field: built for a 10-plus-year service life, with deployment, monitoring and long-term evolution designed in
Model Lifecycle and Governance
A model is a liability the day after it ships if nobody is watching it. Every AI feature we deliver comes with a plan for what happens to it in production.
Versioned, not overwritten
Models, training data and pipelines are version-controlled, so any result traces back to the exact model that produced it.
Monitored in production
Performance and data drift are tracked against baseline, with alerting before accuracy degrades into a field issue.
Retrained on a plan
Retraining triggers, cadence and approval steps are defined up front, not improvised after something breaks.
Reversible by design
Every deployment rolls back to the last validated version, cleanly and without touching the rest of the product.
Regulatory-grade AI
For medical devices, analytical instruments and life-sciences products, AI is engineered inside the same quality framework as the rest of the device, with compliance, validation and traceability as first-class requirements from day one.
Our approach considers:
- Alignment with quality management systems such as ISO 13485 and ISO 9001
- Design controls, documentation and traceability across data, models and software
- Verification and validation strategies appropriate to risk class and intended use
- Human-in-the-loop designs where AI supports, rather than replaces, expert decisions
- Clear separation between analytical assistance and clinical decision-making where required
- Explainability by design: model outputs your reviewers can trace back to the inputs that drove them, not a black box you have to defend to an auditor
- Bias and performance testing across operating conditions, sample types and edge cases, documented as part of the validation record
Services
- oAI feasibility studies and use-case definition for instruments and systems
- oData strategy, acquisition and pipeline design
- oModel development for analysis, diagnostics and predictive applications
- oLLM-agnostic system design: the model layer stays swappable, with no lock-in to a single vendor
- oEdge and cloud AI deployment architecture
- oIntegration of AI with existing software, firmware and electronics
- oVerification, validation and performance benchmarking
- oOngoing support and SLA-backed maintenance post-deployment
Technologies
- oPython, C/C++, C#, scientific computing and numerical analysis libraries
- oClassical ML, statistical modelling, deep-learning frameworks, time-series and anomaly-detection toolkits
- oLibraries for spectral analysis, waveform processing, filtering and feature extraction
- oImage processing and vision frameworks; NVIDIA Jetson and DeepStream for video analytics
- oOptimised inference runtimes for ARM and embedded platforms, model compression and quantisation
- oLLM-agnostic integration: OpenAI, Claude, Gemini and locally hosted open-weights models; RAG pipelines; MCP servers
- oSecure cloud deployment: AWS, Azure, Google Cloud, on-premise; model monitoring and drift detection
Case Studies
Curating our case studies for you...