Custom AI & ML Development
Proprietary machine learning models, custom LLM fine-tuning, and predictive intelligence engineered for scale.
Custom AI development is the process of architecting, training, fine-tuning, and deploying bespoke machine learning and deep learning models tailored specifically to a business's proprietary data, operational logic, and proprietary product features.
Generic off-the-shelf AI APIs lack your domain-specific business context.
Public APIs like stock GPT-4 are expensive at scale, share generalized weights, and frequently fail at specialized domain tasks like complex medical coding, proprietary financial scoring, or specialized Canadian legal compliance.
Exorbitant per-token API costs when processing high-volume enterprise transactions
Inability to maintain proprietary IP and custom model weights within your private infrastructure
Sub-optimal accuracy on niche industry terminologies, jargon, and proprietary taxonomies
Vendor lock-in and unpredictable API deprecation or rate-limiting schedules
Private, high-performance models tailored to your competitive advantage.
We design data curation pipelines, fine-tune open-weights models (Llama 3, Mistral, Qwen) using parameter-efficient techniques (QLoRA, DPO), and deploy optimized inference runtimes (vLLM, TensorRT-LLM) on dedicated cloud infrastructure.
Curated Dataset Engineering
We clean, deduplicate, synthetically expand, and label your proprietary datasets for high-density training signal.
Fine-Tuning & Alignment
Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) to teach models your exact tone, format, and reasoning paths.
Inference Optimization
Model quantization (FP8, INT4, AWQ) and continuous batching using vLLM to cut inference latency by 4x and cloud compute costs by 70%.
Predictive Analytics & Vision
Custom XGBoost, Random Forest, and YOLO/ResNet computer vision architectures for tabular forecasting and visual inspection.
What Is Included in Every Engagement
Zero vague promises. Everything we build and deploy is documented, measurable, and owned 100% by your team.
Model Artifacts & Weights
- Domain-fine-tuned model checkpoints (GGUF, Safetensors, AWQ)
- Automated data cleaning and synthetic augmentation scripts
- Evaluation harness with benchmark scoring and hallucination metrics
- Full intellectual property (IP) assignment and private repository ownership
Production Inference & MLOps
- vLLM / TensorRT-LLM containerized microservice deployed on AWS/GCP/RunPod
- Autoscaling GPU cluster configurations with zero cold-start fallbacks
- Prometheus & Grafana telemetry tracking tokens/sec and GPU utilization
- Continuous training CI/CD pipeline for periodic model retraining
API & Software Integration
- OpenAI-compatible REST API wrapper for drop-in backend integration
- FastAPI/Python microservices with asynchronous request queuing
- SDK clients for TypeScript, Python, and Go
- Comprehensive technical documentation and architectural runbooks
Our 4-Phase Delivery Process
A predictable, transparent engineering sprint pipeline that ensures on-time deployment.
Feasibility & Data Pipeline Audit
We evaluate your training datasets, benchmark baseline off-the-shelf models, and define target accuracy, latency, and cost thresholds.
Data Preparation & Synthetic Augmentation
We normalize data schemas, filter noisy records, generate high-quality instruction pairs, and split cross-validation cohorts.
Model Training, LoRA & Evaluation
We execute multi-GPU training runs, perform hyperparameter tuning, and evaluate checkpoints against our domain benchmark test suite.
Quantization & Production Deployment
We quantize model weights, deploy high-throughput inference endpoints with vLLM, and integrate with your production applications.
Fine-Tuning Llama 3 for Automated Legal Document Analysis
Built and deployed a quantized 70B parameter model locally on private Canadian cloud infrastructure, guaranteeing zero data leakage.
Read Full Architecture & ResultsQuestions About Custom AI & ML Development
Direct, technical answers regarding our custom ai & ml development methodology, Canadian data compliance, and timelines.
When should we fine-tune a model instead of using prompt engineering or RAG?
Fine-tuning is recommended when you need the model to consistently adhere to a complex output syntax, understand deep domain terminology, mimic a specific brand style, or when public API token costs become unsustainable at high request volumes.
Do we own the proprietary model weights and training datasets?
Yes, 100%. All training scripts, curated datasets, LoRA adapter weights, and containerized deployment images are fully transferred to your organization with complete intellectual property ownership.
What hardware or cloud infrastructure is required to host private models?
Depending on model size and latency needs, we deploy models on AWS EC2 (G5/P4 instances), GCP, or cost-effective dedicated GPU providers (Lambda Labs, RunPod). With INT4/AWQ quantization, powerful 8B models can run efficiently on single consumer-grade GPUs.
How do you prevent bias and maintain factual accuracy in custom models?
We utilize strict data deduplication, synthetic validation sets, automated red-teaming harnesses, and DPO (Direct Preference Optimization) to penalize inaccurate or biased model responses.
Can you build predictive machine learning models for tabular and time-series data?
Yes. Beyond LLMs, we build classical machine learning models (LightGBM, XGBoost, LSTM networks) for customer churn prediction, dynamic pricing, demand forecasting, and predictive lead scoring.
Let's Engineer Your Unfair Advantage
Fill out the form below or message us directly on WhatsApp. We typically review technical requirements and respond within 4 hours.