ForgeAI
Enterprise AI Data Platform
End-to-end platform for data labeling, LLM fine-tuning, model evaluation, and deployment — built for teams that need full control over their AI pipeline.
ForgeAI is a comprehensive AI/ML operations platform that covers the entire model lifecycle — from data preparation to production deployment. Unlike API-dependent AI services, ForgeAI gives your team complete ownership of the pipeline: label your data, fine-tune leading open-source models, evaluate outputs with human feedback, and deploy with one click.
Every step uses real data and real computation — no simulations, no random numbers, no fake endpoints. Whether you're building custom NLP models on proprietary data or fine-tuning large language models for domain-specific tasks, ForgeAI provides the infrastructure, workflows, and governance your enterprise needs.
Designed for self-hosted and on-premise deployments, ForgeAI ensures your data never leaves your infrastructure — making it ideal for organizations in finance, healthcare, legal, and government that require strict data sovereignty.
Supported Technologies
Built on industry-leading frameworks and models
Data Labeling & Annotation
Multi-Role Annotation Workflows
Built-in workflows with annotators, reviewers, and managers for structured data labeling at scale.
Quality Control Pipelines
Approval/rejection pipelines with inter-annotator agreement tracking to ensure label quality.
Multi-Format Data Support
Support for text, image, audio, and tabular data types with task-specific annotation interfaces.
Training-Ready Export
Export datasets in instruction-tuning, classification, and preference formats ready for SFT and RLHF/DPO.
LLM Fine-Tuning
Leading Open-Source Models
Fine-tune Llama 3.1, Mistral 7B, Gemma 2, Phi-3, and more using LoRA/PEFT techniques.
Memory-Efficient Training
4-bit quantization with QLoRA for GPU-constrained environments — no expensive hardware required.
Encoder Model Support
Full fine-tuning support for BERT, RoBERTa, and DistilBERT for classification and NER tasks.
Real-Time Training Dashboard
Live training progress, loss curves, and metrics dashboard for full visibility into every training run.
Model Inference & Serving
One-Click Deployment
Deploy fine-tuned models with auto-scaling to staging, production, or canary environments.
Text Generation API
Full-featured generation API for LLMs with configurable temperature, top-p, and top-k sampling.
Classification Inference
Real-time classification with confidence scores for encoder models.
Deployment Monitoring
Health monitoring, latency tracking, CPU/memory metrics, and deployment dashboards.
Human Evaluation & Testing
Evaluation Campaigns
Pairwise comparison, Likert scale, ranking, and binary evaluation with per-evaluator tracking.
Automated Benchmarking
Accuracy, bias, safety, and regression test suites with configurable pass/fail thresholds.
Side-by-Side Comparison
Compare model outputs head-to-head before promoting to production.
Enterprise-Ready
Role-Based Access Control
Fine-grained RBAC with admin, manager, annotator, and reviewer roles across all platform features.
Self-Hosted Deployment
On-premise deployment — your data never leaves your infrastructure. Full data sovereignty guaranteed.
Model Registry & Versioning
Full model lifecycle management with versioning, tagging, lineage tracking, and audit trail.
Webhook Notifications
HMAC-signed webhook events for training completion, deployment status, and pipeline events.
Built for Teams That Demand More
AI/ML teams building custom models on proprietary data
Enterprises in finance, healthcare, legal, and government that need data sovereignty
Organizations moving from API-dependent AI (OpenAI, etc.) to self-hosted models
Teams that need end-to-end control over the model lifecycle
Frequently Asked Questions
Get answers to common questions about ForgeAI.
QCan ForgeAI be deployed on-premise?
Yes. ForgeAI is designed for self-hosted and on-premise deployments. Your data, models, and training runs stay entirely within your infrastructure — no external API calls required.
QWhat models can I fine-tune with ForgeAI?
ForgeAI supports fine-tuning of leading open-source LLMs including Llama 3.1 (8B and 70B), Mistral 7B, Gemma 2 9B, and Phi-3 Mini using LoRA/PEFT. It also supports full fine-tuning of encoder models like BERT, RoBERTa, and DistilBERT.
QDo I need expensive GPUs to fine-tune models?
Not necessarily. ForgeAI supports 4-bit quantization (QLoRA) which enables fine-tuning of large language models on consumer-grade GPUs with significantly reduced memory requirements.
QHow does the data labeling workflow work?
ForgeAI provides a multi-role annotation pipeline: annotators label data, reviewers approve or reject labels, and managers oversee the process. It supports text, image, audio, and tabular data with inter-annotator agreement tracking.
QCan I evaluate models before deploying to production?
Absolutely. ForgeAI includes human evaluation campaigns (pairwise comparison, Likert scale, ranking) and automated testing suites (accuracy, bias, safety, regression) so you can thoroughly validate models before production deployment.
QWhat deployment options are available?
ForgeAI supports staging, production, and canary deployments with traffic percentage routing. Models are served with auto-scaling, health monitoring, and real-time performance metrics.
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Ready to Get Started with ForgeAI?
Schedule a demo or talk to our team about your requirements.
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