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AI Foundry: Sovereign Enterprise Foundation Models & Domain Fine-Tuning

Generic public LLMs lack your company’s institutional knowledge and risk leaking sensitive IP. Idynic AI Foundry builds custom, domain-adapted foundation models that you own 100%.

40%

Higher Domain Accuracy vs Generic LLMs

100%

Customer Intellectual Property Ownership

65%

Inference Token Cost Reduction

Zero

Telemetry or Weights Shared Externally

Spotlight: Idynic AI Foundry with Hugging Face & PyTorch

Accelerate proprietary enterprise model adaptation with certified open-weights optimization pipelines on state-of-the-art silicon.

Explore the framework

The Foundation Model Lifecycle Forge

Transform raw enterprise data into highly performant, specialized reasoning engines through disciplined four-stage model engineering.

01

Synthetic Data Curation

Automated data cleaning, deduplication, and synthetic domain instruction synthesis using frontier teacher models with strict privacy filtering.

02

LoRA & QLoRA Tuning

Parameter-efficient fine-tuning with rank-stabilized scaling, gradient checkpointing, and distributed FlashAttention-2 acceleration.

03

RLHF & DPO Alignment

Direct Preference Optimization aligning models with corporate compliance rules, safety taxonomies, and precise tone guidelines.

04

Quantization & Packaging

FP8 and AWQ INT4 post-training quantization reducing VRAM footprint by 65% with zero measurable perplexity loss.

Proven Empirical Accuracy Gains

Generic frontier models fail on esoteric corporate dialects. Our specialized foundry adaptations consistently outperform models 10x their parameter size on domain-specific reasoning benchmarks.

View technical whitepaper
Model Architecture Baseline Score Post-Foundry Accuracy Gain
Llama 3.3 70B (Financial QA) 71.4% 94.2% +22.8%
DeepSeek-R1 32B (Legal Code) 64.8% 89.6% +24.8%
Mistral Large (Biomedical) 78.2% 96.1% +17.9%
Qwen 2.5 14B (SQL Synthesis) 68.0% 92.5% +24.5%

Our Offerings

We provide full-stack model engineering capabilities to ensure your enterprise owns its weights, protects its IP, and scales inferencing without vendor lock-in.

Consult model engineers

Domain-Specific Model Fine-Tuning

Custom weights adapted to proprietary vocabularies, schemas, and enterprise documentation.

  • Multi-GPU distributed training with DeepSpeed ZeRO-3 and FSDP
  • Automated hyperparameter grid sweeps and loss convergence tracking
  • LoRA rank stabilization preventing catastrophic forgetting

Synthetic Data Generation & Filtering

Produce high-density training corpuses without exposing confidential production PII.

  • De-identification pipelines compliant with GDPR, HIPAA, and CCPA
  • Semantic diversity clustering avoiding model mode collapse
  • Automated adversarial hallucination injection and critique loops

Case Study

Custom Model Engineering

Specialized 32B Model Outperforms 405B Frontier LLM for Tier-1 Private Bank

A Swiss private wealth manager required strict on-premise execution for portfolio compliance audits. Idynic fine-tuned an open-weights 32B parameter model using QLoRA and DPO, achieving 96.4% factual accuracy while cutting inference costs by 84%.

Learn more

Case study

Model Engineering Operations

Frequently Asked Questions

Technical insights into model weights adaptation and quantization.

Your enterprise retains 100% ownership of all adapter weights, training artifacts, and synthetic datasets. Nothing is ever shared or fed back into public model providers.

Activation-aware Weight Quantization (AWQ) protects salient weight channels, reducing GPU memory by over 60% with less than 0.3% degradation in benchmark perplexity.

Forge Your Proprietary Enterprise AI Models

Schedule a technical feasibility session with our distributed training and model alignment researchers.

Architect Custom Models