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Custom AI starts with a hypothesis worth proving

Building custom models is an investment. We start every engagement by validating the business case and proving the concept with your real data - so you invest in production only when the results justify it.

Validate. Prove. Then build.

The most expensive mistake in AI isn’t building the wrong model – it’s building any model before proving it’s worth building. We de-risk your investment with a structured approach: business case first, proof of concept second, production only when the hypothesis is proven.

01

Business Case & Feasibility

Before any model is built, we assess whether custom AI is the right solution. We evaluate data readiness, define measurable success criteria, and estimate realistic ROI – giving you a clear go/no-go recommendation.

  • Problem-AI fit assessment
  • Data availability & quality audit
  • Success criteria & KPI definition
  • ROI estimation & investment recommendation

We’ll tell you honestly if custom AI isn’t the right path – before you invest.

02

Proof of Concept

We build a working prototype using your real data to test the hypothesis. You see concrete results – accuracy, speed, cost impact – and get the evidence you need to make the production investment decision.

  • Working prototype with your data
  • Performance benchmarks & metrics
  • Technical feasibility validation
  • Production roadmap & cost estimate

Real results with real data – the evidence your CFO needs to greenlight production.

03

Production Development

With proven feasibility and clear business case, we follow CRISP-ML(Q) to build production-grade systems. Every deployment ships with monitoring, retraining pipelines, and infrastructure that scales.

  • Production-grade ML system
  • Integration with existing workflows
  • Monitoring & drift detection
  • Retraining pipelines & SLA-backed support

Build with confidence – every system is backed by proven PoC results.

What we build

quality

Quality Inspection & Defect Detection

Catch defects your inspectors miss, at production-line speed. Computer vision systems trained on your specific products, deployed at your edge - reducing rework costs and customer complaints.

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Document Processing & Knowledge Extraction

Turn unstructured documents into actionable data. Automate invoice processing, contract analysis, compliance checks - eliminating hours of manual work per day.

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Resource & Schedule Optimisation

Cut planning cycles from weeks to days. Optimise logistics, scheduling, and resource allocation with constraint solvers and ML - reducing idle time, overtime, and missed deadlines.

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Predictive Maintenance & Digital Twins

Stop equipment failures before they stop your production. Multi-layer digital twins with real-time anomaly detection reduce unplanned downtime and extend asset life.

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Process Automation & Orchestration

Automate end-to-end business workflows that span multiple systems. From data extraction pipelines to decision-support systems - eliminating manual handoffs and human error.

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Production Deployment & Monitoring

We don't build demos. Every system ships with monitoring, retraining pipelines, drift detection, and the infrastructure to scale - so your ROI compounds, not decays.

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Case study - ArcelorMittal

AI in harsh industrial environments.

 

Multiple Computer Vision systems deployed in steelmaking and logistics operations, designed for extreme conditions where precision and speed are non-negotiable.

Steel Ladle Pickup Safety
Safety-critical hook positioning over steel ladles
Computer Vision system using YOLO for element localization and U-Net for infrastructure segmentation.
Enables real-time position determination and operator guidance in extreme industrial conditions.
99%
Detection accuracy for hooks approaching the ladle

500ms
End-to-end response time for real-time hazard detection
Wagon UID Recognition
Automated data reading from passing railcars
Computer Vision and ML system (YOLO, ViT, LSTM) for automatic railcar recognition.
Real-time processing eliminating manual logistics data entry.

82%
Reduction in processing time (8.5h → 1.5h)

98%+
Licence plate recognition accuracy (dual-camera)

5x
Faster licence plate number recording

Frequently asked questions.

Because the most expensive AI project is one that shouldn’t have been built. A PoC validates the core hypothesis with your real data in 4-8 weeks – proving that the approach works, the data is sufficient, and the expected business impact is realistic. This gives you concrete evidence to justify the production investment, rather than committing six-figure budgets based on assumptions.

That’s one of the most valuable outcomes a PoC can deliver – it saves you from an investment that wouldn’t have paid off. We’ll be transparent about what we found: whether the data quality is insufficient, the accuracy targets aren’t achievable, or the ROI doesn’t justify custom development. We’ll also present alternatives – sometimes a different approach, a simpler solution, or better data collection strategy can unlock the value you’re after.

It depends on the use case. For computer vision, we typically need a representative sample of images or video from your environment – even a few hundred labelled examples can be enough for a proof of concept. For NLP and document intelligence, we need sample documents representative of the variety you handle. We always start with a data feasibility assessment to understand what’s available, what’s missing, and what quality remediation might be needed.

Most PoCs take 4-8 weeks from kick-off to a working demonstration. This includes data preparation, model training, initial validation, and a live demo with your data. We’re transparent about what a PoC can and can’t prove – it validates feasibility and gives you a concrete basis for the production investment decision.

Off-the-shelf AI tools (like ChatGPT or general CV APIs) are great for generic tasks. Custom development is necessary when your problem requires domain-specific accuracy, integration with proprietary data, compliance with industry regulations, or performance guarantees that generic tools can’t provide. Part of our business case assessment is helping you determine which approach fits – we won’t recommend custom development if an existing solution does the job.

Yes. We offer flexible support models: from full managed services (we operate and maintain the model) to knowledge transfer (we train your team to take over). Most clients choose a hybrid – we handle model monitoring and retraining while your team manages the business-side integration. Support terms are defined during project scoping.