AI Infrastructure Engineering
Proprietary AI. Engineered for Lean Scale.
We guide enterprises through the complexities of building and fine-tuning custom models without the bloated infrastructure costs. Optimal paths to owned intelligence.
The Challenge
The Model Ownership Trap.
Why most enterprises fail at building proprietary AI—and how the cycle perpetuates dependency on third-party APIs.
Sunk Cost Fallacy
Over-provisioning GPUs and inefficient training runs burning runway. Teams default to brute-force compute when surgical approaches yield better ROI.
Fine-Tuning Fragility
Catastrophic forgetting and poor data curation leading to model degradation. Without proper methodology, fine-tuning destroys more value than it creates.
Deployment Bottleneck
Great prototype models that are too expensive to run at scale. The path from notebook to production remains the graveyard of AI initiatives.
Our Methodology
Surgical Optimisation.
A three-phase approach to building proprietary AI that scales without burning through your infrastructure budget.
Low-Rank Adaptation
LoRA / QLoRA
We prioritize efficient fine-tuning techniques over full-parameter training. Achieve comparable performance at 10-100x lower compute cost by training only the critical weight matrices.
Synthetic Data Engineering
High-Signal Generation
Creating high-value training data cost-effectively. We engineer synthetic datasets that target specific capability gaps, eliminating the need for expensive manual annotation at scale.
Quantization & Distillation
Inference Optimization
Shrinking models for cheaper inference without losing quality. Deploy 4-bit quantized models or distill knowledge into smaller architectures purpose-built for your use case.
Services
Technical Competencies.
Deep expertise across the full spectrum of proprietary AI development—from initial architecture through production deployment.
Proprietary LLM Fine-Tuning
Customizing open-weight models (Llama 3, Mistral, Qwen) on your enterprise data. We handle the full pipeline from data preparation through evaluation, ensuring your model learns exactly what your business needs.
RAG Pipeline Optimization
Reducing latency and token costs in Retrieval-Augmented Generation systems. We architect hybrid search, optimize chunk strategies, and implement intelligent caching to cut costs while improving relevance.
SLM Deployment
Edge-ready Small Language Models for specific tasks. When you don't need 70B parameters, we distill and optimize compact models that run on modest hardware while maintaining task-specific performance.
Cost-Per-Token Audits
Forensic analysis of your current AI spend and optimization roadmaps. We map every API call, identify waste, and provide concrete recommendations with projected savings timelines.
NOT SURE WHERE TO START?
Every engagement begins with a feasibility assessment.
We evaluate your data, infrastructure, and objectives to determine the optimal path—whether that's building proprietary models or optimizing your current stack.
Proven Results
Case Studies.
Real engagements. Measurable outcomes. See how enterprises transitioned from API dependency to owned AI infrastructure.
Financial Services
Fortune 500 Bank
Challenge
Internal document processing with 40M+ pages annually using GPT-4 at $2.1M/year
Solution
Fine-tuned Llama 3 70B with LoRA on proprietary financial documents, deployed with 4-bit quantization
Results
E-Commerce
Global Retail Platform
Challenge
Product recommendation system generating $8K/day in API costs with inconsistent results
Solution
Distilled GPT-4 knowledge into custom 7B parameter model with RAG integration
Results
Healthcare Tech
Clinical AI Startup
Challenge
Medical summarization requiring HIPAA compliance impossible with cloud APIs
Solution
On-premise Mistral 7B fine-tuned on clinical notes with custom tokenizer for medical terminology
Results
* Client details anonymized. Results representative of typical engagements.
Get Started
Initiate Feasibility Study.
Not ready for a project? Let's determine if building your own model is even the right fiscal move.