Kitchen design visualisations
AI phone call analysis
We used AI to convert initial kitchen CAD data into high-fidelity, lived-in design images.
Impact
The designer saves days of work in a professional rendering software.
He skips the hassle of organising professional photography sessions.
His clients have immediate feedback loop on how their home will look like.
CLIENT: Interior design studio
CLIENT REVENUE: 300k Euro
INDUSTRY: Construction
DURATION: 1 Week
Source image exported from drawing studio software
AI visualisation
Automatic generation of views from multiple angles
Or a short video clip
The Challenge
Contextual Gaps
AI needed to accurately infer and generate realistic environmental context, including lighting, textures, and domestic clutter, absent in the original file.
Maintaining Design Fidelity
The final photorealistic output had to strictly preserve the exact technical dimensions and core design elements from the initial draft.
The Solution
We used Gemini 3 Pro Image to understand the design file and then automatically create a realistic picture by adding details, textures, and context.
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Developed a proprietary Gemini implementation, engineered for repeatable high-fidelity visualization and rapid, consistent output generation.
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Reduced visualization time from days (manual) to minutes, achieving near real-time rendering of complex interior spaces.
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Attained a high image realism score while maintaining 1:1 dimensional accuracy against the source CAD model.
Phone call summaries by AI
AI phone call analysis
An AI phone call analysis system that transforms conversations into summaries, next steps, and recommendations for the operators.
Impact
No need for the company owner to listen to full length recorded phone calls. A quick look at the summaries is enough.
Actions overview and tracking for the phone call operators like to send an email to the customer, do a follow up call etc.
Sentiment analysis - was it a positive call? Is the customer happy?
CLIENT: Environmental consulting agency
CLIENT REVENUE: 0.5M Euro
INDUSTRY: Consulting
DURATION: 6 Weeks
The Challenge
Speech-to-Text Accuracy
Incorrect text input corrupts downstream AI tools (intent, solutions), leading to misunderstandings and inaccurate resolutions for the operator.
Peak call volumes cause the AI databases to time out
Operators lose real-time assistance.
Tone of Operator Recommendations
Technical, or demanding language causes operator confusion and decreased confidence.
The Solution
We built an automated n8n pipeline that leverages Google Speech‑to‑Text for accurate transcription and Google Gemini for real‑time call summaries, action items, and operator‑friendly recommendations.
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Google outperformed ElevenLabs in our tests.
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In order to avoid timeouts of the AI tools, we had to separate the tasks into multiple steps. One-by-one execution helped to mitigate the problem.
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The tone of languages of the AI recommendations to the phone call operators proved to be delicate and it required extra attention.
Supply-Demand Tool by AI
It all begins with an idea.
A predictive supply-demand tool that aligns procurement with real-world sales, inventory, and lead times.
CLIENT: Electronics manufacturer
INDUSTRY: EV Mobility
DURATION: 3 Weeks
The Challenge
Variant-Driven Complexity
The client produces multiple EV charger models, each selling at different proportions. Forecasting demand wasn’t just about total volume—it required understanding how variant mix affects component needs.
Fragmented Inventory Visibility
Inventory was spread across several categories: finished goods in stock, products ordered but not yet delivered, and components already ordered but still in transit. This made it difficult to assess true availability and plan ahead.
Lead Time and Procurement Uncertainty
Each component came with its own supplier lead time, and delays could ripple through the production schedule. The key question was: which components should be ordered, when, and in what quantities to meet future demand without overstocking or stalling production?
The Solution
We built a custom supply-demand planning engine that integrates real-time sales data, inventory status, and supplier lead times to generate precise procurement recommendations.
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Product variant sales proportions
Existing stock of finished goods and components
Ordered but undelivered products and parts
Component-specific lead times and supplier variability
Minimum order quantities and batch sizes
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Probabilistic demand modeling to simulate variant-level sales
Constraint-based optimization for procurement timing and quantities
Rule-based logic for deterministic checks (e.g., safety stock thresholds)
Scenario simulation to test different sales and supply conditions
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A modular interface for inputting assumptions and constraints
Visual outputs showing component needs over time
Editable forecasts and override options for planners
Exportable recommendations for procurement systems
Operational Impact
90% less time in Excel
100% accuracy of the calculation
Business Impact
750K Euro annual cost savings due to inventory optimisation
24K Euro annual cost savings due to time saved in Excel
Tech Support Powered by AI
It all begins with an idea.
A support assistant that reduced response times by 87% using only verified documentation.
CLIENT: Smart home system manufacturer
INDUSTRY: IoT & Home Automation
DURATION: 8 Weeks
The Challenge
Documentation Gaps
Documentation was fragmented across multiple product generations and sources. Support teams couldn't find accurate information quickly.
Hallucination Risk
AI models generate incorrect answers when lacking information. In technical support, wrong instructions could damage devices or create safety hazards.
The Solution
We implemented a RAG system that grounds every response in verified documentation.
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Vectorised all documentation and manuals into a searchable semantic database.
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Vector similarity search retrieves relevant documentation for each query.
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AI answers only from provided context, with clear handling when information is unavailable.
Operational Impact
24/7 availability with instant responses
92% Customer Satisfaction
Business Impact
234K Euro annual cost savings due to reduced support staff overhead