SYSTEM_DIAGNOSTICS
------------------
CPU_LOAD: 34%
MEM_ALLOC: 12GB
NEURAL_NET: ACTIVE
## Smart Manufacturing
### 85% of AI pilots fail.
### We build the 15% that scale.
Pioneering the use of advanced AI technology in real-world projects. If your AI dies when it touches real systems, you don’t have a model problem — you have an integration problem.
[Book a Strategic Consultation
](#form-ready)
PROVEN IMPACT IN: [DEFENCE](https://digica.com/ai-solutions-tailored-for-the-defence-industry.html) • [MEDICAL](https://digica.com/digica-delivering-value-and-transforming-performance-in-healthcare-through-ai.html) • [IOT](https://digica.com/software-development/the-internet-of-things-iot.html) • [MANUFACTURING](https://digica.com/manufacturing.html)
SYSTEM_STATUS
LIVE PRODUCTION
UNPLANNED DOWNTIME ID: #Err-09
-21.9% / Month
PRODUCTION YIELD ID: #Yld-A1
+14.0% / Year
DETECTION LEAD TIME ID: #Lat-X2
+26.9% Faster
LATENCY: 12ms DATA\_STREAM: ACTIVE
- [Who are Digica ](#who)
- [The Pilot Graveyard ](#pilot)
- [How We Work ](#how)
- [Case Studies](#case-studies)
- [FAQ](#faq)
[Book a Strategic Consultation
](#form-ready)
### Who are Digica
Digica is the partner that brings AI from demo to production. Based in Europe, our team of 80+ data scientists and software engineers specialises in AI and machine learning for industry leaders. Our clients—including Meta, AMD, Roche, and Teledyne—prove our commitment to real operational impact. The logos you see are companies that trust Digica to deliver production-grade AI.
## THE PROBLEM
### The Pilot Graveyard: Where demos succeed, but production kills them.
Mismatched PLCs. Fragmented data. "Green dashboards" that lie. Vendors show you a pristine demo on clean data, but real factories are messy.
> The reason 85% of industrial AI projects fail isn't the algorithm. It's that they can't survive "Thursday"—a busy, messy day on a live production line.
[Book a Strategic Consultation
](#form-ready)
### Signals You’re in the Pilot Graveyard
#### Isolation Failure
Pilots run fine in an isolated sandbox but crash immediately when connected to legacy OT networks or real-time SCADA feeds.
#### The "Data Cleaning" Trap
Your team spends 90% of their time cleaning CSVs manually because the solution lacks automated data harmonisation pipelines.
#### The Phantom Green Light
Dashboards show all systems "Green" and optimised, yet operators on the floor are reporting jams, stops, and quality defects.
### How We Work
Digica’s cooperation model is flexible. Consistency and transparency are key to our delivery process.
### NDA
Signing NDA if required to protect your IP.
Legal framework setup.
### Review Data
We analyse your sample data for 2–3 days free of charge.
Overview of potential project & indicative budgets.
### PoC
Proof of Concept phase to demonstrate value.
Validate feasibility before full investment.
### Full Solution
Delivery of robust, scalable operational solution.
Deployment, testing, and support.
### Engineering Out The Risk
We don't just deliver code. We deliver resilient industrial systems that respect the reality of your factory floor.
#### Integration That Scales
AI that adapts to your legacy systems—not the other way around. We build bridges to existing PLCs and historians without forcing a rip-and-replace.
#### Production-Grade AI
Built for real machines, mixed vendors, and strict OT constraints. Our systems are designed to fail safely and recover automatically.
#### Predictive > Reactive
Shift from putting out fires to preventing them. Detect subtle vibration or temperature anomalies hours before a bearing fails.
#### Proof, Not Promise
Measurable results in weeks, not quarters. We define success by OEE impact and yield improvement, not by successful model training.
[Book a Strategic Consultation
](#form-ready)
### Built for Decision-Makers Who've Seen Pilots Fail
CTOs, plant directors, and transformation leads who need AI that actually works
### CTO / Head of Engineering
INTEGRATION WITHOUT DISRUPTION
You need AI that doesn't break your stack. We build on top of legacy systems—PLCs, SCADA, ERP—without rip-and-replace. Scalable architecture. Zero production risk.
### VP Operations / Plant Director
UPTIME, YIELD, THROUGHPUT
You're judged on uptime and output. We deliver predictive alerts that prevent unplanned downtime, improve yield, and stabilise throughput—without disrupting operations.
### Head of Digital Transformation
ROADMAP & ROI
You're building the future—but pilots keep dying. We deliver cross-system orchestration, unified data layers, and 90-day ROI so your transformation roadmap survives executive scrutiny.
### Case Studies
TOY FACTORY
### Detection of printing errors
A Toy Factory needs to identify printing errors and assess whether they render the whole brick unsellable. We trained a CNN for detecting three kinds of printing errors with per-pixel probability heatmaps.
Results:Automated QA threshold established for accept/reject decisions.
PyTorch, OpenCV, Docker, FastAPI
BAE Systems
### Process Optimisation
Developed a machine learning model to recommend optimisations to the process as the factory is operating to reduce energy and resource consumption.
Results:Desktop app visualising optimal component arrangement and scheduling.
Python, PyQT6, ORTools
Anybotics
### Robotic console analysis
Intelligent computer vision inspection solution for robots operating in hostile environments. Inspects gauges, liquid levels, and thermal anomalies.
Results:Works offline in hazardous situations with synthetic data training.
TensorFlow 2, C++, OpenCV, ROS
Ayla Networks
### Predicting device reboots
Predict router poor performance and prevent it using Machine Learning. Indicate reboot probability and detailed reasons for failure.
Results:Model predicts reboots 12-hours in advance with >85% accuracy.
PySpark, XGBoost, Amazon SageMaker
Medical Imaging Silicon
### Structural defects in silicon
Reduce the amount of faulty silicon assembled into equipment. Identify defective regions (slices) in silicon wafers from scans and test data.
Results:Accurately identified all slices in test data provided.
XGBoost, SHAP, Scikit-image
## FAQ
### Frequently Asked Questions
[ Show all FAQs ](https://digica.com/index.php?Itemid=997)
How soon can I see results?Many clients begin seeing measurable improvements (defect reduction, fewer breakdowns, etc.) within **3-6 months** from solution deployment.
Do I need lots of labelled data to get started?No. We combine what data you have with synthetic data generation and hybrid approaches. Even rare defects can be modeled without thousands of real-world examples.
How will this integrate with my existing OT / factory floor systems?Our edge-first approach is designed to work with SCADA, DCS, PLCs, and most industrial control systems. We aim for minimal disruption in operations.
What if I already tried an AI pilot and it failed?That’s exactly where Digica shines. We help you move past the “pilot graveyard”-identifying what held the pilot back, fixing the bottlenecks, and delivering production-grade solutions.
What is the cost?It depends on the scope, but we offer **Factory Health Audits** and **Proof-of-Concept sprints** at fixed cost so you can estimate value before committing large scale investment.
How does this help with sustainability or energy goals?Our digital twin / smart factory approaches can drive **5–7%** operational cost savings and **~30%** energy reductions. Plus, fewer defects & less waste support environmental goals.
### Ready to escape the Pilot Graveyard?
Stop building demos. Start building systems that survive the factory floor.