EADPAG provides bespoke LLM and Gen AI models matching industry-specific needs. We optimize custom AI models to deliver actionable, data-driven results.
Every engagement starts from your model's baseline and is calibrated against your production data, not a generic benchmark.
Adapting foundation models to your industry's vocabulary, edge cases, and compliance constraints.
Full and parameter-efficient tuning of large language models for task-specific accuracy.
Reinforcement Learning with Human Feedback to align model behavior with real user preference.
Joint tuning across text, image, and structured data for unified model reasoning.
Quantization, distillation, and pruning for lower latency and inference cost.
Getting production-grade output from limited or no labeled examples.
Auditing and correcting model outputs for fairness across sensitive attributes.
Structured prompt and context design to raise reliability without retraining.
Fine-tuning is a measurement problem before it's a training problem. Here's how we hold the line on both.
Reusing learned representations from adjacent domains to minimize computational overhead and training time.
Cleaning, normalization, and augmentation pipelines built around your data's actual failure modes.
Search algorithms that tune learning rate, batch size, and regularization intelligently rather than by grid search alone.
Integrating text, image, and contextual signals into a single coherent model behavior.
Parallel processing across multiple GPUs to compress training cycles without sacrificing stability.
Real-time tracking, drift detection, data anonymization, and encrypted training throughout the model's lifecycle.
Each stage closes with a measurable checkpoint before the next begins.
Analyzing existing models and mapping a detailed fine-tuning plan against your target metrics.
Improving data quality, normalization, and feature extraction ahead of training.
Adjusting parameters and hyperparameters to handle complex, real-world tasks.
Seamless system integration with regular updates and monitoring after go-live.
It starts with defining the target task and success metric, then preparing and labeling a representative dataset, selecting a base model, running supervised or parameter-efficient tuning, and validating against held-out data before deployment. Each stage produces a checkpoint you can review.
We treat fine-tuning as a measurement discipline: every model is evaluated against your production data and business metric, not a generic leaderboard, and monitored continuously after deployment for drift and quality regressions.
Finance teams gain models tuned to regulatory language and risk terminology, reducing false positives in compliance and fraud workflows. eCommerce teams gain models tuned to product catalogs and customer intent, improving search relevance and recommendation accuracy.
Larger, well-labeled datasets generally produce more stable and generalizable models, but parameter-efficient techniques and few-shot methods let us deliver strong results even with limited data by transferring knowledge from the base model.
Your unique concepts will be crafted into a remarkable end result. Tell us what you're building and an AI specialist will follow up within one business day.