AI models trained on your data, deployed and monitored in production
Foundation models (GPT, Claude, Llama) are powerful but generic. For optimal performance on your specific domain · ticket classification, entity extraction, business content generation · you need a model fine-tuned on your data. Wikolabs handles the full cycle: data preparation, fine-tuning, evaluation, deployment and monitoring.
Generic models make errors on vocabulary and nuances specific to your sector. Without an MLOps pipeline, models degrade in production undetected. Inference on unoptimized models is expensive. And without deployment infrastructure, models remain POCs that never reach production.
We build a complete MLOps pipeline: data preparation and annotation, fine-tuning on your cloud infrastructure (GCP Vertex AI, AWS SageMaker or Azure ML), comparative evaluation against the base model, API endpoint deployment and continuous monitoring (drift, performance, inference cost). The model is automatically retrained when performance degrades.
Training dataset assembly, annotation (manual or semi-automatic), quality validation and train/eval/test split.
Training on Vertex AI, SageMaker or Azure ML. Business metric evaluation. Base model vs. fine-tuned comparison.
Deployment on scalable API endpoint. Inference optimization (quantization, batching). Integration into your existing systems.
Performance metric monitoring, concept drift detection and automatic retraining trigger when needed.
A fine-tuned domain model averages 40% better performance on your specific tasks.
A small specialized model costs much less to run than a large generic model for the same task.
The model trained on your data belongs to you. It's a strategic asset that grows in value over time.
Free 30-minute audit. We analyze your context and deliver a concrete roadmap.