Build AI models that know your enterprise.

Transform institutional knowledge into frontier-grade LLMs—without infrastructure burden or cloud lock-in.

Why Forge?

Domain alignment.

Structured customization pipelines that integrate proprietary datasets, ontologies, and decision frameworks.

End-to-end training.

Train models across the full lifecycle, from pre-training and synthetic data generation to post-training with reinforcement learning.

Production-grade evaluation.

Rigorous evaluation frameworks tailored to enterprise KPIs, not generic benchmarks.

Infrastructure flexibility.

Deploy in the environment that matches your risk profile without surrendering control to a single cloud vendor.

Security and governance.

Strict data isolation, controlled training pipelines, and auditable customization workflows aligned to your compliance policies.

From your data to your model.

The system for your frontier AI.

Pre-training

Domain learning at scale: Train on large volumes of unstructured enterprise data to internalize your domain language and concepts.
Model architectures: Support for advanced architectures including Dense and Mixture-of-Experts (MoE) to balance performance, specialization, and efficiency for enterprise-scale workloads.
Multi-modal ready foundations: Text, image and audio support where relevant.

Reinforcement learning

Reinforcement learning: Align model behavior using Reinforcement Learning from Human Feedback (RLHF), with model distillation to maintain consistency and efficiency.
Efficient adaptation: Low-Rank Adaptation (LoRA) to specialize models without heavyweight retraining.
Enterprise alignment: Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) to encode standards and preferences.

Synthetic data generation

Domain examples on demand: Generate high-signal training samples tailored to your workflows and terminology.
Edge-case coverage: Create long-tail scenarios that don’t appear frequently in real data but matter in production.
Policy-bound scenarios: Produce compliance-aware examples to reinforce governance and reduce unsafe outputs.

Evaluation and regression testing

KPI-aligned evaluation: Measure model quality against enterprise outcomes—not generic benchmarks.
Regression suites: Detect performance drops when data, prompts, or model versions change.
Drift detection: Monitor behavioral drift over time as domains, policies, and usage evolve.

Model lifecycle management

Version everything: Models, datasets, training runs, and configs—tracked as first-class assets.
Traceability and auditability: Reproduce decisions and outputs with a clear lineage of what changed and why.
Rollback with confidence: Revert to known-good versions when regressions or policy changes occur.

Inference

Optimized runtime performance: Serve customized models with low-latency, high-throughput inference optimized for enterprise-scale workloads.
Policy-aware responses: Enforce governance and safety constraints at inference so outputs consistently respect internal standards and controls.
Flexible deployment: Run inference across private cloud, on-prem, or Mistral compute with control over data residency and infrastructure.

Pre-training

Domain learning at scale: Train on large volumes of unstructured enterprise data to internalize your domain language and concepts.
Model architectures: Support for advanced architectures including Dense and Mixture-of-Experts (MoE) to balance performance, specialization, and efficiency for enterprise-scale workloads.
Multi-modal ready foundations: Text, image and audio support where relevant.

Reinforcement learning

Reinforcement learning: Align model behavior using Reinforcement Learning from Human Feedback (RLHF), with model distillation to maintain consistency and efficiency.
Efficient adaptation: Low-Rank Adaptation (LoRA) to specialize models without heavyweight retraining.
Enterprise alignment: Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) to encode standards and preferences.

Synthetic data generation

Domain examples on demand: Generate high-signal training samples tailored to your workflows and terminology.
Edge-case coverage: Create long-tail scenarios that don’t appear frequently in real data but matter in production.
Policy-bound scenarios: Produce compliance-aware examples to reinforce governance and reduce unsafe outputs.

Evaluation and regression testing

KPI-aligned evaluation: Measure model quality against enterprise outcomes—not generic benchmarks.
Regression suites: Detect performance drops when data, prompts, or model versions change.
Drift detection: Monitor behavioral drift over time as domains, policies, and usage evolve.

Model lifecycle management

Version everything: Models, datasets, training runs, and configs—tracked as first-class assets.
Traceability and auditability: Reproduce decisions and outputs with a clear lineage of what changed and why.
Rollback with confidence: Revert to known-good versions when regressions or policy changes occur.

Inference

Optimized runtime performance: Serve customized models with low-latency, high-throughput inference optimized for enterprise-scale workloads.
Policy-aware responses: Enforce governance and safety constraints at inference so outputs consistently respect internal standards and controls.
Flexible deployment: Run inference across private cloud, on-prem, or Mistral compute with control over data residency and infrastructure.

Intelligence for high-consequence environments.

Code modernization.

Train models on proprietary codebases and engineering standards to refactor legacy systems, migrate frameworks, and generate reviewable code that follows your architecture and development practices.

Industrial domain adaptation.

Train models on your engineering documentation, standards, vocabularies, and decision frameworks so they understand domain terminology, constraints, and workflows as a foundation, not as an afterthought.

Cybersecurity.

Detect and prioritize real attacks by training on your environment’s telemetry, including alerts, identity events, endpoint and network logs, and past incident timelines. Generate investigation paths and response recommendations that follow your security policies.

Quant research.

Train models on proprietary signals, research archives, and execution data to generate new hypotheses, signal variations, and structured experiment plans for systematic strategy research.

Build AI models that know your enterprise.

Why Forge?

Domain alignment.

End-to-end training.

Production-grade evaluation.

Infrastructure flexibility.

Security and governance.

From your data to your model.

Pre-training

Reinforcement learning

Synthetic data generation

Evaluation and regression testing

Model lifecycle management

Inference

Intelligence for high-consequence environments.

Code modernization.

Industrial domain adaptation.

Cybersecurity.

Quant research.

Build your differentiator.