case studies
See how our AI solutions drive success for our clients—tailored, secure, and impactful.
Case studies
Case 1
Database Intelligence Chatbot
Challenge:
The company needed a secure and scalable solution to interact with their internal database containing massive datasets (5,000+ rows, 10,000+ columns). Manual data analysis and retrieval was time-consuming and required technical expertise, creating bottlenecks in decision-making processes.
What We Built:
Natural Language Querying (AI-powered): We developed an intelligent chatbot that translates natural language questions into complex database queries, allowing non-technical users to extract insights instantly without SQL knowledge.
Advanced Analytics Engine: The system performs real-time counting, filtering, aggregation, and trend analysis across massive datasets, delivering results in seconds rather than hours.
Secure Local Architecture: Built with enterprise-grade security protocols running entirely on local infrastructure, ensuring complete data privacy with no external data transmission. The system maintains role-based permissions and audit trails while keeping all sensitive information within the company's network.
Thanks to AI-powered natural language processing with robust database querying, we dramatically reduced analysis time and eliminated manual data processing. The system empowers teams to make data-driven decisions faster, reduces dependency on technical staff, and ensures instant access to critical insights at all times.
Case 2
Warehouse optimization
Challenge:
A fully automated robotic warehouse needed a smarter backend system to significantly speed up product dispensing and improve how items are placed throughout the storage system. The new logic had to follow strict operational rules while also learning from real demand patterns, predicting seasonality, and adapting product placement to ensure maximum efficiency using machine learning.
What We Built:
Stock-In Optimization (ML-powered): We developed a system that uses machine learning scoring to predict the demand for each product. Based on this prediction, the system calculates optimal storage locations to ensure high-demand products are stored in the fastest-access spots, ready for fast dispensing.
Fast Dispensing: The system ensures FEFO (First Expiry First Out) compliance, handles multi-product orders, and balances robot tasks efficiently to avoid collisions and maximize throughput.
Nightly Reorganization: Based on demand predictions, the system repositions products dynamically to keep high-demand items easily accessible.
By integrating machine learning–based demand prediction with automated stock-in logic, fast dispensing workflows, and nightly warehouse optimization, we significantly increased the speed and efficiency of product dispensing. The system continuously adapts storage placement, minimizes handling time, and ensures high-demand items are always positioned for the fastest possible output.
Case 3
Churn Predictor Model
A fast-growing subscription app struggled with rising monthly churn and had no clear way to identify users who were about to cancel. We designed and delivered an AI-powered churn prediction system that helped them take action before users left.
What We Did:
Analyzed user behavioral data (logins, usage, features, tickets, payments) to uncover hidden churn patterns.
Identified which subscribers were most likely to churn, including risk scores for every user segment.
Added explainability tools to show key churn drivers
Delivered a simple web app for single or batch predictions with retention recommendations.
Outcome
Within the first month, the company started identifying most high-risk users early and reduced churn by 20–25%, protecting a significant portion of recurring revenue. This gave them a clear retention strategy and a strong competitive advantage.
Case 4
Recode open question
We continuously refine AI performance to drive long-term growth and adaptability.Challenge:
A survey and analytics provider struggled with large volumes of open-ended responses that had to be manually categorized, taking up to 8 hours per week per analyst and slowing down the process.
Solution:
We developed an automated open-ended response analysis system using machine learning and text analysis to quickly extract and categorize responses. The tool analyzes thousands of texts in real time and assigns them to relevant categories.
Outcome:
The company saved 3-8 hours per analysis, significantly speeding up the process and enabling faster decision-making and better client offerings. The service will be soon available on www.recodeq.com
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