AI Development Services | Bonami Software

The Challenge: Physicians spend two hours on EHR documentation for every hour of patient care. Manual coding and billing introduced weeks of delay between encounters and claim submissions.
The Solution: Built an AI-first EHR with ambient clinical scribe, smart ICD-10/CPT code suggestions, and automated claim pipeline — so clinicians focus on patients, not paperwork.
The Impact: Reduction in Charting Time – 70%

Code & Claim Accuracy – 85%

Patient Throughput Increase – 3x

The Challenge: Legal teams manually adapted contracts across jurisdictions, with fragmented case documentation and time-intensive compliance checks slowing down workflows.
The Solution: Delivered an AI-powered legal platform with jurisdiction-aware contract drafting, OCR intelligence, and automated compliance scoring across U.S. and Mexican frameworks.
The Impact: Faster Drafting & Review – 60%

Compliance Improvement – 70%

Admin Workload Reduction – 40%

The Challenge: Generic recommendation engines and reactive moderation allowed toxic content to spread while failing to match content with user interests.
The Solution: Built an AI-first social platform with hybrid recommendation engine, real-time toxicity detection, and BERT/GPT sentiment analysis for safer, more relevant communities.
The Impact: Engagement Improvement – 70%

Harmful Content Detection – 95%+ accuracy

Content Relevance Increase – 35%

The Challenge: Traditional trading bots relied only on technical indicators, ignoring social sentiment signals that drive rapid crypto price movements.
The Solution: Developed an autonomous trading system combining LSTM price prediction, TensorFlow sentiment analysis of Reddit/Twitter, and XGBoost signal enhancement with automated risk management.
The Impact: MAPE for Predictions – <5%

Monthly ROI Improvement – 15%

Manual Intervention Reduction – 90%

The Challenge: Manual MRI segmentation was time-consuming, inconsistent, and lacked real-time clinical integration for emergency environments.
The Solution: Built a 3D U-Net segmentation engine with hybrid Dice + Focal loss, FastAPI real-time inference, and MLflow monitoring for continuous clinical performance.
The Impact: Dice Segmentation Accuracy – 90%+

Radiologist Review Time Reduction – 60%

Real-Time Stroke Triage – Enabled

The Challenge: Manual scheduling created conflicts and uneven workloads across locations. HR teams lacked visibility into productivity and engagement across distributed teams.
The Solution: Delivered an AI-driven workforce platform with predictive conflict resolution, GPS-verified attendance, multi-view scheduling, and AI-generated onboarding content.
The Impact: Scheduling Efficiency – 45% improvement

Manual HR Documentation – 90% reduction

Employee Engagement – 30% increase

The Challenge: Traditional segmentation models struggled with overlapping accessories and complex avatar geometries, with latency constraints in interactive environments.
The Solution: Built a hybrid YOLO + U-Net architecture with dynamic scaling algorithms and GPU-accelerated PyTorch inference for real-time avatar segmentation and virtual try-ons.
The Impact: Segmentation Accuracy – 97%

User Engagement Increase – 40%

Reduced Return Rates – 30% via try-ons

AI Development Built for Real-World Impact