What We Build
NeoBram builds production-grade AI systems: not prototypes, not proof-of-concepts that never ship, and not off-the-shelf tools rebranded as custom AI. Every system we deliver is built on your data, integrated with your existing infrastructure, and deployed with the monitoring and maintenance infrastructure needed to keep it working accurately over time.
Why most AI projects never reach production
According to Gartner, only 53% of AI projects make it from prototype to production. The most common failure points are: data that is not ready for production use, systems that cannot integrate with existing infrastructure, and no MLOps framework to maintain model accuracy over time. NeoBram addresses all three from day one.
8-12 Weeks
Scoping to Production
4 Weeks
Working MVP Delivered
100%
Production-Ready with MLOps
3 Options
Post-Deployment Support Models
What We Build and Deploy
Six AI system types. Each one built for production, not for demos.
Large Language Model Applications
Document intelligence, chatbots, and knowledge systems that actually work
We build production-grade LLM applications using RAG (Retrieval-Augmented Generation) architectures that ground AI responses in your actual company data. This includes document Q&A systems, internal knowledge bases, contract analysis tools, and customer-facing chatbots. Every system is built with hallucination controls, source citation, and confidence scoring.
RAG-based systems reduce hallucination rates by 60-80% vs. base LLM deployments
Agentic AI Workflows
AI that completes multi-step business processes autonomously
Agentic AI systems go beyond answering questions. They take actions: researching, deciding, executing, and reporting across multiple systems. We build agentic workflows for procurement automation, customer onboarding, compliance monitoring, and complex data processing pipelines that previously required human coordination.
Agentic workflows reduce process cycle times by 40-70% for complex multi-step operations
Computer Vision Systems
Visual inspection, detection, and classification at machine speed
We develop computer vision systems for manufacturing quality control, document processing, security monitoring, and medical imaging. Our systems are trained on your specific data, calibrated to your quality standards, and integrated with your existing production or operational infrastructure.
Computer vision quality control achieves 99.5%+ defect detection rates vs. 95% for manual inspection
Predictive Analytics and ML Models
Forecasting and scoring models that improve decisions at scale
We build and deploy predictive models for demand forecasting, credit risk scoring, churn prediction, preventive maintenance, and fraud detection. Every model comes with explainability features for regulated use cases, confidence intervals, and a monitoring framework to detect when predictions start degrading.
Predictive maintenance models reduce unplanned downtime by 30-50% in manufacturing environments
MLOps and AI Infrastructure
The plumbing that keeps AI working reliably in production
Most AI projects fail not at build time but at production time. We design and implement MLOps infrastructure: automated model training pipelines, performance monitoring dashboards, data drift detection, A/B testing frameworks, and rollback capabilities. Your AI systems stay accurate, observable, and maintainable over time.
Organizations with mature MLOps practices deploy AI 3x faster and have 60% fewer production incidents
Secure and Compliant AI Deployment
Production AI that satisfies your legal, security, and audit teams
We build security and compliance into every system from the first line of code. This includes data encryption, role-based access controls, audit logging, model explainability for regulated decisions, and privacy-preserving techniques for sensitive data. We design for GDPR, India DPDP Act, RBI, SEBI, and CDSCO requirements.
Compliance-by-design reduces post-deployment remediation costs by 80% vs. compliance-as-afterthought
Technology Stack
We work with the tools that are right for your use case, not the ones we are most comfortable with.
Have a specific AI system in mind?
Tell us what you want to build. We'll tell you if it's feasible, what it will take, and what it will cost.
Most scoping conversations take 30 minutes. You'll leave with a clear sense of whether the project is viable, what the key technical risks are, and what a realistic timeline and budget looks like.
Book a Free Technical Scoping CallAI Systems We Build by Industry
The right architecture depends on your industry's data, compliance requirements, and operational context.
Manufacturing
- Computer vision quality inspection on production lines
- Predictive maintenance from sensor data streams
- AI-powered demand forecasting for inventory optimization
- Energy consumption optimization using ML
BFSI
- Real-time fraud detection with sub-100ms latency
- Explainable credit risk scoring for RBI compliance
- Document intelligence for KYC and loan processing
- Automated regulatory reporting and compliance monitoring
Pharma and Healthcare
- Clinical document processing and adverse event detection
- Drug interaction analysis using LLM pipelines
- Patient data integration and clinical trial matching
- Diagnostic assistance for imaging and pathology
Oil, Gas and EPC
- Remote asset anomaly detection from IoT sensor data
- Safety incident prediction and prevention systems
- Project document intelligence and contract analysis
- Maintenance schedule optimization using historical data
Our 8-12 Week Build Process
Every engagement follows the same disciplined process. No surprises, no scope creep, no missed deadlines.
Requirements and Architecture Design
We run deep-dive sessions with your business and technical teams to define exact requirements, success metrics, and constraints. We design the system architecture, data pipeline, and integration points before writing a single line of code. This prevents expensive rework later.
Data Preparation and Model Development
We prepare your data, select or fine-tune the right model for your use case, and build the core AI system. At week 4, we deliver a working MVP for your team to test and validate. Feedback from the MVP shapes the final system.
Integration and Testing
We integrate the AI system with your existing infrastructure, run comprehensive testing (unit, integration, performance, and adversarial), and conduct user acceptance testing with your team. Security and compliance reviews happen in this phase.
Production Deployment and MLOps Setup
We deploy to production with zero-downtime deployment procedures, set up monitoring dashboards, configure alerting, and implement the MLOps pipeline for ongoing model maintenance. We run a 2-week parallel operation period before full cutover.
Handover, Documentation, and Support
We deliver complete technical documentation, runbooks, and training for your team. We agree on a post-deployment support model: full handover, managed service, or hybrid. You have everything you need to own and operate the system independently.
Frequently Asked Questions
Technical and commercial questions CTOs and CIOs ask before engaging NeoBram for AI development.