The integration of AI in software development is transforming the technology industry at an unprecedented rate. At Crombie, we have adopted a true AI—driven approach where AI is the backbone of our architecture and engineering processes, not just an add-on. This model enables us to maximize efficiency, quality, and scalability.
AI-driven architecture involves on-device AI and intelligent pipelines
We implement continuous monitoring software based on machine learning models that operate locally on developers' devices. This on-device solution offers:
- Intelligent and proactive monitoring: We use custom rules in SonarQube, filtered key detection, and AI—maintained anti-pattern lists to analyze code in real time.
- Advanced filtering and early detection: AI identifies false positives and anticipates areas for improvement, providing immediate feedback.
- Agile and secure iteration: Teams can quickly deploy new functionality, confident that critical issues will be detected before reaching production.
Additionally, our CI/CD pipelines incorporate intelligent validations, such as dependency analysis, vulnerability detection, and refactoring suggestions, all of which are powered by AI.
Advanced integration of LLMs and specialized models
At Crombie, we don't limit ourselves to generalist models when using this technology. Our team uses LLMs, such as ChatGPT, o4, Sonnet, and Gemini 2.5 Pro, for text processing and code generation, as well as specialized models for specific tasks.
What sets our integrations apart?
- Pre—trained and customized models: We use open-source Hugging Face models and develop our own for image, audio, and text processing according to project needs.
- We create connected agents that interact with enterprise databases and external systems using Retrieval-Augmented Generation (RAG) architectures to obtain, synthesize, and act on information in real time.
- We also optimize GitHub Copilot configurations: Each project has a customized, weekly-updated instance fed with technical documentation and insights from previous projects, boosting the team's productivity and quality.
Use Cases of AI in Software Development
The application of AI in software development enables us to solve complex problems across multiple industries:
Retail:
- Prediction of product demand based on historical data and seasonality.
- Detection of cart abandonment patterns in e-commerce
- Automatic recognition of products in gondolas from photos
- Classification of customer comments by type of complaint or satisfaction.
- Automatic generation of product descriptions.
- Product recommendations based on user interests.
- Fraud detection in purchase processes.
Agro and food:
- Identification of plant diseases from images.
- Classification of crops by type or stage of maturity.
- Detection of animals or pests in fields using satellite images or drones.
- Crop yield estimation with visual and meteorological data.
Automotive and logistics:
- Estimating vehicle use and wear from photos.
- Automatic reading and determination of the country of origin of patents.
- Optimizing routes using historical data and real-time conditions.
- Claims or damage analysis using photos submitted by users.
Financial Services and Insurance:
- Automatic detection of false or altered documents.
- Claims classification and analysis using text models.
- Detection of fraud patterns in transactions.
- Generation of automatic customer service responses.
Human resources and education:
- Automatic classification of CVs according to searched profile.
- Employee performance analysis based on feedback or metrics.
- Generation of interview summaries or synthesis.
- Implementation of personalized training or course recommendation systems.
Public sector and NGOs:
- Identification of critical areas in cities through satellite photos.
- Sentiment analysis in social networks about public policies.
- Automatic processing of legal or administrative documents.
- Sign language recognition and accessibility improvement.
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Technical and business advantages of using AI in software development
- Speed and Agile Iteration: Artificial intelligence applied to software development enables the quick launch of digital products without sacrificing security or quality.
- Guaranteed control and quality: Intelligent monitoring and early error detection ensure robust and reliable products.
- Customization and scalability: We adapt AI to each stack and domain, integrating our own and third-party models according to customer needs.
- Continuous updating and experimentation: Our culture of experimentation and weekly tool updates keep our teams at the forefront of technology.
Conclusion:
At Crombie, AI in software development is the engine that drives innovation and efficiency. Our AI—driven approach, which is based on advanced technical architecture and the deep integration of intelligent models, enables us to transform ideas into high-impact digital products that are ready to face the challenges of the future.
Take your business to the next level with AI!
At Crombie we design and integrate Artificial Intelligence solutions that maximize your efficiency and growth.
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