Generative.AI
Custom Foundation Models for Agentic Intelligence
Power autonomous agents with custom generative models tailored to specific domains and optimized for particular tasks. Leverage open-source foundations with proprietary intelligence for superior performance and competitive advantage.
Comprehensive Generative Capabilities
Natural Language
Text generation and reasoning
Multimodal
Image, audio, video processing
Analytical
Mathematical and scientific
Creative
Innovation and content creation
Structured
Code and data transformation
Multilingual
Cross-language understanding
| Technique | Purpose | Benefits |
|---|---|---|
| Instruction Tuning | Follow complex multi-step instructions | Improved task completion accuracy |
| RLHF Integration | Align with human preferences | Better value alignment |
| Domain Adaptation | Specialize for industry terminology | Higher domain expertise |
| Few-Shot Learning | Rapid adaptation to new tasks | Faster deployment cycles |
| Continual Learning | Update without forgetting | Sustainable model evolution |
Advanced Model Architecture Stack
Foundation Layer
Pre-trained open-source models (Llama, Mistral, Falcon) providing base capabilities
Adaptation Layer
Domain-specific fine-tuning using proprietary data and specialized objectives
Optimization Layer
Performance tuning, quantization, and efficiency improvements
Alignment Layer
Value alignment, safety constraints, and behavioral guardrails
| Component | Technology | Purpose |
|---|---|---|
| Foundation Models | Llama 3, Mistral, Claude | Base language capabilities |
| Fine-tuning Pipeline | LoRA, QLoRA, Full Fine-tuning | Domain specialization |
| Optimization Engine | Quantization, Pruning, Distillation | Efficiency improvement |
| Inference Runtime | vLLM, TensorRT, ONNX | Fast deployment |
| Safety Framework | Constitutional AI, RLHF | Responsible behavior |
Model Development Spectrum
Performance & Efficiency Optimization
| Optimization | Technique | Result |
|---|---|---|
| Model Compression | Quantization, Pruning | 90% size reduction |
| Inference Speed | TensorRT, ONNX | Sub-100ms response |
| Distributed Training | Multi-GPU clusters | Billions of parameters |
| Edge Deployment | Mobile optimization | On-device inference |
| Dynamic Batching | Request optimization | High throughput |
Quality Assurance & Benchmarking
| Benchmark | Score | Domain |
|---|---|---|
| MMLU | 92.8% | Multitask Language Understanding |
| HellaSwag | 96.1% | Commonsense Reasoning |
| HumanEval | 85.3% | Code Generation |
| GSM8K | 89.7% | Mathematical Reasoning |
Enterprise Benefits
| Benefit | Impact | Timeframe |
|---|---|---|
| Competitive Advantage | Proprietary models tailored to domain | 3-6 months |
| Cost Reduction | 10x savings vs frontier model APIs | Immediate |
| Data Privacy | Keep sensitive data within infrastructure | Immediate |
| Offline Operation | Mission-critical applications | 1-3 months |
| Business Context | Deep understanding of terminology | 2-4 months |
Data Engineering & Curation
| Process | Description | Output |
|---|---|---|
| Data Cleaning | Remove noise and inconsistencies | High-quality datasets |
| Data Augmentation | Expand training data diversity | Robust model performance |
| Synthetic Generation | Create domain-specific examples | Expanded training corpus |
| Privacy Transformation | Anonymize sensitive information | Compliant training data |
Model Governance & Versioning
| Feature | Purpose | Benefits |
|---|---|---|
| Version Control | Track model iterations | Complete lineage tracking |
| A/B Testing | Compare performance | Data-driven decisions |
| Rollback Capability | Revert to previous versions | Risk mitigation |
| Access Control | Manage permissions | Security and compliance |