Skip Setup Headaches and Start Your Project Fast - Download Free Boilerplates
Design, Training, and Deployment of AI Models, Led by Senior AIOps Engineers
We extensively conduct applied research and use case-specific experimentation. Over the past five years, Radixweb’s dedicated R&D unit has executed 60+ controlled experiments in AI engineering like unstructured text analysis and video-based anomaly detection.
Findings have been directly implemented in the AI systems we’ve built for predictive maintenance, medical imaging, and supply chain optimization.
Our specialization in AI application development services is transitioning trained models into stable, maintainable software assets. Across 20+ client projects, our teams have integrated model outputs with existing ERPs and CRMs and established frameworks to manage drift.
Our typical engagement cycles achieve production-readiness within 8-12 weeks, even in regulated domains such as banking and healthcare.
AI systems we release undergo a three-tier validation approach - synthetic unit testing at the data pipeline level, functional validation against business logic, and model reliability testing using edge-case scenarios.
In the last audit cycle, 96% of our AI deployments needed zero emergency intervention post-launch. Our clients deployed AI modules without any unplanned downtime for over 18 months.
AI engineers at Radixweb hold formal certifications in major AI infrastructure platforms, including AWS Machine Learning Specialty and Microsoft Azure AI Engineer Associate. We adhere to provider-specific architectural standards, cost controls, and security benchmarks.
We have deployed AI models using native services like SageMaker, as well as hybrid pipelines combining Kubernetes, Kafka, and third-party stores.
Our consultants provide model suitability analysis, cost-benefit scenarios, and data maturity assessment for tech leaders to evaluate where and how AI can generate measurable value.
Build intelligent apps designed around your domain logic and operational data. Our AI app solutions cover data engineering, model training, infrastructure setup, and app integration.
Integrate AI components into your existing products. Create intelligent features like personalized recommendations, user intent recognition, fraud alerts, and predictive automation.
We create low-risk AI pilots to validate the viability of models and quantify performance. Before we proceed to full-scale engineering, we deploy prototypes within tightly scoped sprints.
We deploy trained models into mobile, web, or enterprise apps using RESTful APIs and streaming endpoints. Teams can trace our secure AI models within defined latency thresholds.
We design, train, and optimize ML models with modular architecture, automated pipelines, and advanced algorithms to deliver adaptive intelligence for enterprise apps.
We deploy and customize gen AI models for various enterprise use cases. Solutions mostly involve LLMs adapted to business-specific vocabulary, workflows, and privacy considerations.
Radixweb's testing and QA teams conduct structured audits of AI models for accuracy, bias, stability, and compliance. We run stress testing, bias assessments, and explainability reporting.
Post-deployment, we provide performance tracking, user feedback ingestion, and model refinement cycles as per changing data conditions, business rules, and technical environments.
We build large-scale generative models for chatbots, content generation, and language reasoning. We also integrate GPT, LLaMA, and Claude into your CRM and service platforms.
We’ve delivered NLP solutions for legal document parsing (93%+ accuracy), email triage, conversational routing, sentiment scoring, and regulatory compliance scanning.
Some of our projects in AI computer vision include defect detection on manufacturing lines, medical image segmentation, smart surveillance, and OCR for document digitization.
Develop AI software systems that generate predictions from time-series data. A forecasting model we built for a retail client reduced stockouts by 37% and improved sales prediction.
We build AI-powered recommendation engines like retail product ranking and content personalization that adapt to changing user behavior without requiring manual rule updates.
Our conversational systems, such as customer support agents or product configuration bots, are optimized for intent accuracy, context persistence, and latency under production load.
We develop unsupervised and semi-supervised anomaly detection frameworks. Across critical deployments, clients have reported a 60–75% improvement in incident flagging speed.
We apply RL methodologies to optimize long-term decision policies in environments with sparse feedback. Agents are trained using custom reward functions and real-world constraints.
Our AI models accurately extract structured data from loose content in record time. In production, our automated pipelines have reduced document processing times from days to hours.
We design deep learning models, vision-based detection, and sensor-driven inference for environments with intermittent connectivity and real-time decision requirements.
These figures represent the scope and integrity of our tech engineering portfolio.
Years of Engineering Maturity and Tech Expertise
Solutions Delivered Across 25+ Countries
Client Retention Rate in Global Engagements
Avg. Dev Team Score on Clutch and G2 Review Platform
We’ll take you from scope to live AI in 8 weeks. That’s our baseline.
Our focus is on applying fairness-aware learning and synthetic balancing to reduce demographic bias in loan approval models, fraud detection, and hiring platforms.
As a longstanding product engineering company, we’ve consistently advanced alongside emerging technologies. With a strong foundation in intelligent system design, AI has become one of our core focus areas.
This partnership has been excellent and Radixweb has shown expertise in all of the key technologies we needed. Our own fluid requirements have made it challenging at times, but Radixweb has excelled.
From the initial discussions to ongoing support, the way they look after customers, how to work across organizations, a combination of all of these makes Radixweb an amazing partner.
It gives us this sense of pride when our customers say that they are flattered by the product. We know we chose the right people in the way they have made our solution unique.
Our AI developers follow a disciplined, 10-step engineering process aligned with your technical standards, domain logic, and deployment conditions.
Get expert AI input, a scoped plan, budget clarity, and a launch timeline.
