ForgeAI
Enterprise AI/ML Operations Platform
End-to-end platform for data ingestion, labeling, LLM fine-tuning, RLHF alignment, evaluation, deployment, and production monitoring — 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 ingestion to production monitoring. Connect to 10+ data sources (S3, Snowflake, BigQuery, Databricks, and more), automatically detect and mask PII, label data with multi-role annotation workflows, fine-tune leading open-source LLMs with LoRA, and align models using RLHF with DPO, PPO, and GRPO.
Before anything reaches production, validate models with human evaluation campaigns, red teaming across six attack categories, and side-by-side output comparison. Then deploy to staging, production, or canary environments with drift detection, cost tracking, and real-time performance metrics.
Designed for self-hosted and on-premise deployments, ForgeAI ensures your data never leaves your infrastructure. With RBAC, SSO/SAML, audit trails, and webhook notifications, it's built for organizations in finance, healthcare, legal, and government that require strict data sovereignty and governance.
Supported Technologies
Built on industry-leading frameworks and models
Data Ingestion & Preparation
10+ Data Connectors
Connect to S3, Snowflake, BigQuery, Databricks, Redshift, Azure Blob, GCS, PostgreSQL, MySQL, and Hugging Face Hub with unified import workflows.
PII Detection & Masking
Automatically scan and redact sensitive data — SSN, credit cards, emails, phone numbers, IP addresses — with configurable regex patterns before training.
Read-Only Query Safety
Built-in SQL injection prevention for all database connectors with multi-statement blocking and comment stripping.
Dataset Versioning & Lineage
Track every transformation, merge, and export with full lineage history and audit trails across dataset versions.
Data Labeling & Annotation
Multi-Role Annotation Workflows
Structured workflows with annotators, reviewers, and managers for data labeling at scale with role-based permissions.
Active Learning
Uncertainty-based sample prioritization using least confidence, margin sampling, and entropy strategies to label the most impactful data first.
Quality Control Pipelines
Approval/rejection pipelines with inter-annotator agreement tracking to ensure label quality and consistency.
Training-Ready Export
Export datasets in instruction-tuning, classification, and preference formats ready for SFT, RLHF, and DPO training.
Model Training
LLM Fine-Tuning
Fine-tune Llama 3.1 (8B/70B), Mistral 7B, Gemma 2, Phi-3, and arbitrary HuggingFace models using LoRA/PEFT with 4-bit quantization for memory-efficient training.
Encoder Model Training
Full fine-tuning support for BERT, RoBERTa, and DistilBERT for text classification and NER tasks.
Experiment Tracking
Log hyperparameters, step-level metrics, and loss curves across training runs with side-by-side comparison and best-experiment discovery.
Real-Time Training Dashboard
Live training progress with WebSocket streaming, loss curves, and metrics for full visibility into every run.
RLHF & Alignment
DPO, PPO & GRPO
Three reinforcement learning algorithms for aligning models with human preferences, powered by TRL.
Constitutional AI Filters
User-defined content constraints with keyword and regex rules to enforce safety boundaries during generation.
Reward Hacking Detection
Automated monitoring of reward distributions using z-score analysis to catch reward explosions, collapses, and distribution shifts during training.
KL Divergence Guardrails
Automatic training pause when policy divergence exceeds configurable thresholds, preventing catastrophic forgetting.
Evaluation & Testing
Human Evaluation Campaigns
Pairwise comparison, Likert scale, ranking, and binary evaluation with per-evaluator tracking and DPO preference export.
Red Teaming
Track adversarial testing across six attack categories: jailbreaking, hallucination, bias, privacy extraction, prompt injection, and over-refusal.
Side-by-Side Comparison
Compare model metrics, run the same inputs through multiple models, and view output differences before promoting to production.
Deployment & Monitoring
Multi-Environment Deployment
Deploy models to staging, production, or canary environments with health checks, rollback support, and text generation and classification APIs.
Production Drift Detection
Population Stability Index (PSI) monitoring with automatic severity classification and actionable recommendations for retraining.
GPU Cost Tracking
Per-job compute cost tracking across GPU, CPU, memory, and storage with configurable pricing for H100, A100, L4, V100, and T4 instances.
Real-Time Performance Metrics
Health monitoring, latency tracking, CPU/memory metrics, and confidence calibration dashboards for deployed models.
Enterprise & Security
Role-Based Access Control
Fine-grained RBAC with admin, manager, annotator, and reviewer roles across all platform features.
SSO / SAML Integration
SAML 2.0 support with auto-provisioning, role mapping from IdP assertions, and standard SP metadata endpoints.
Self-Hosted Deployment
Deploy on-premise with containerized infrastructure. Your data never leaves your infrastructure.
Webhook Notifications
HMAC-signed webhook events with retry logic for training completion, model registration, 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 with containerized infrastructure. 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. You can also load arbitrary HuggingFace model IDs.
QDo I need expensive GPUs to fine-tune models?
Not necessarily. ForgeAI supports 4-bit quantization (QLoRA via BitsAndBytes) which enables fine-tuning of large language models on consumer-grade GPUs with significantly reduced memory requirements. The platform also includes a GPU memory estimator that recommends the right hardware for your model and dataset.
QWhat data sources can I connect to?
ForgeAI supports 10 data connectors out of the box: Amazon S3, Snowflake, Google BigQuery, Databricks, Amazon Redshift, Azure Blob Storage, Google Cloud Storage, PostgreSQL, MySQL, and Hugging Face Hub. All SQL connectors enforce read-only query safety to prevent accidental writes.
QHow does RLHF alignment work in ForgeAI?
ForgeAI supports three reinforcement learning algorithms: DPO (Direct Preference Optimization), PPO (Proximal Policy Optimization), and GRPO (Group Relative Policy Optimization) — all powered by TRL. Built-in guardrails include KL divergence auto-pause and reward hacking detection to keep training stable.
QHow does ForgeAI handle sensitive data?
ForgeAI includes built-in PII detection and masking that automatically scans for SSNs, credit card numbers, emails, phone numbers, IP addresses, and more. You can scan datasets before training and redact sensitive information with configurable patterns. Combined with self-hosted deployment, your data never leaves your infrastructure.
QCan I evaluate models before deploying to production?
Absolutely. ForgeAI includes human evaluation campaigns (pairwise comparison, Likert scale, ranking), red teaming across six attack categories, and side-by-side model comparison — all before anything reaches production.
QWhat deployment options are available?
ForgeAI supports staging, production, and canary deployments with health monitoring, drift detection (PSI-based), and real-time performance metrics. Deploy on Kubernetes or use the built-in model serving API with text generation and classification endpoints.
Ready to Get Started with ForgeAI?
Schedule a demo or talk to our team about your requirements.
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