Proprietary Custom AI & ML Models
When generic off-the-shelf models fail, we build custom deep learning and machine learning architectures engineered specifically for your proprietary datasets, edge devices, and business KPIs.
Purpose-Built Neural Networks for High-Stakes Business Problems
Commercial APIs provide generic outputs with high recurring token costs. BigIntend designs, trains, and optimizes bespoke machine learning architectures from scratch or fine-tunes open-source foundational backbones, giving you complete IP ownership and superior domain precision.
100% IP Ownership
All trained weights, checkpoints, training datasets, and code belong to you.
90% Lower Serving Cost
Optimized specialized models run on significantly smaller, cheaper GPU hardware.
Unmatched Accuracy
Trained strictly on your domain vocabulary, sensory signals, and business rules.
Our Custom Model Development Capabilities
Supervised & Unsupervised Deep Learning
Custom convolutional, recurrent, and transformer architectures designed for complex predictive tasks.
- Custom PyTorch & TensorFlow Architectures
- Multi-Task & Transfer Learning Pipelines
Tabular Machine Learning & XGBoost
State-of-the-art gradient boosted trees and neural tabular models for high-stakes risk, credit, and pricing algorithms.
- Automated Feature Engineering & Selection
- SHAP / LIME Model Explainability
Time-Series Forecasting & Anomaly Detection
Deep learning models (N-BEATS, PatchTST, Temporal Fusion Transformers) for high-frequency demand forecasting.
- Multi-Horizon Inventory & Energy Forecasting
- Real-Time Sensor Anomaly Alerts
Domain-Specific Foundation Model Adaptation
Parameter-efficient fine-tuning (LoRA, QLoRA, Prefix Tuning) on proprietary text, financial, or biomedical datasets.
- Custom Tokenizer Training
- Synthetic Data Augmentation Pipelines
Model Quantization & Edge Optimization
Compressing models with INT8/FP4 quantization, pruning, and ONNX/TensorRT compilation for edge hardware.
- Sub-10ms Inference Latencies
- NVIDIA Jetson, iOS & Android Deployment
Automated Continuous Training Pipelines
Robust MLOps pipelines that monitor feature drift and trigger automated re-training on new data.
- Airflow & Kubeflow Pipeline Orchestration
- Automated Champion/Challenger Model Validation
Frameworks, Libraries & Training Infrastructure
Our 5-Step Model Engineering Lifecycle
Data Feasibility & Baseline Benchmarking
We audit training data distribution, check class imbalances, and establish baseline performance.
Feature Engineering & Architecture Design
We engineer domain features and select optimal neural or ensemble network backbones.
High-Performance GPU Model Training
We train models using mixed-precision acceleration with hyperparameter optimization.
Validation, Red-Teaming & Explainability
We evaluate ROC-AUC, F1-scores, latency benchmarks, and generate SHAP explainability reports.
Containerized Serving & MLOps Monitoring
We deploy containerized REST/gRPC endpoints with real-time drift detection and SLA logging.
Frequently Asked Questions
Build a Custom AI Model
Schedule a session with our Machine Learning Engineers to discuss your data and model architecture.