Sologenx delivers enterprise software, cloud solutions, and digital transformation services to businesses worldwide.
Consultation- Indore, Madhya Pradesh, India
- +91-731-2024242
- support@sologenx.com
- Mon - Sat: 09:00 to 18:00
Artificial Intelligence & Machine Learning
AI & Machine Learning Development Services
Sologenx builds AI systems that solve specific business problems — not science projects. From predictive models that forecast demand to NLP engines that automate document processing, every solution we deliver is production-ready, explainable, and designed for measurable ROI.
We help enterprises move from AI curiosity to AI production. Our team handles the full pipeline: data preparation, model training, validation, deployment, and ongoing retraining — so your AI actually improves over time instead of degrading.
AI Solutions We Deliver
Deep Learning & Neural Networks
Natural Language Processing
Computer Vision
Predictive Analytics
Intelligent Automation
MLOps & Model Management
Why Businesses Choose Sologenx for AI
Business Problem First
We start with ROI analysis, not technology demos. If AI is not the right fit, we tell you before you spend anything.
Proof of Concept in 4-6 Weeks
Working prototype with your real data, not a slide deck. You see whether the approach works before committing to production.
Production-Grade Engineering
Models deployed with monitoring, retraining pipelines, and version control — not abandoned in a Jupyter notebook.
You Own Everything
Full source code, trained models, data pipelines, and documentation. No vendor lock-in or recurring licence fees.
Have a business problem AI could solve?
Describe your challenge. We will assess whether AI is the right approach and outline a realistic plan — in a free 30-minute call.
Book a Free AI ConsultationAI Technologies & Frameworks
Frequently Asked Questions
A proof of concept typically costs USD 10,000–30,000. Production AI systems range from USD 40,000 to USD 150,000+ depending on data complexity, model requirements, and integration scope.
It depends on the problem. Some tasks need thousands of labelled examples, others work with hundreds. We assess your data during discovery and recommend strategies like data augmentation, transfer learning, or synthetic data generation if volume is limited.
We build MLOps pipelines with automated drift detection, scheduled retraining, A/B testing for new model versions, and monitoring dashboards so accuracy does not degrade as data patterns shift.
AI is the broad goal of building intelligent systems. Machine learning is a subset — algorithms that learn patterns from data. Deep learning is a further subset using neural networks. We use whichever approach fits your problem best.
Yes. We deploy models as APIs or microservices that integrate with your existing applications, databases, and workflows without requiring a full system overhaul.
Proof of concept: 4–6 weeks. Production system: 3–9 months depending on complexity, data preparation needs, and integration requirements.
Absolutely. We sign NDAs, use encrypted environments, and can work entirely within your infrastructure. Data never leaves your control unless you explicitly authorise it.
Healthcare (medical imaging, clinical NLP), finance (fraud detection, risk scoring), manufacturing (quality inspection, predictive maintenance), retail (recommendation engines, demand forecasting), and logistics (route optimisation).
Not initially. We handle the full pipeline. Once the system is stable, we provide documentation and training so your team can manage it, or we offer ongoing managed AI services.
We validate with your data during the proof of concept before committing to production. If results are not satisfactory, we recommend alternative approaches or honestly advise against proceeding — before significant investment.
Yes. We build explainable AI when required. SHAP values, attention maps, feature importance scores, and plain-language explanations help stakeholders understand and trust model decisions.
AWS SageMaker, Azure ML, Google Vertex AI, or on-premise GPU servers. We recommend based on your existing infrastructure, compliance needs, and budget.