Custom AI Development
& Engineering
We build, fine-tune, and deploy custom artificial intelligence systems that run reliably at enterprise scale. From proprietary model fine-tuning to distributed inference pipelines with sub-100ms latency, we turn complex AI research into production revenue drivers.
What We Delivered:
High-Throughput Autonomous Document Intelligence Platform
Client: Global Logistics & Supply Chain Enterprise · Logistics & International Freight · 8-Week Production Delivery
The Core Bottleneck
Manual customs document extraction required 400 human operators with a 4.2% error rate, resulting in severe port customs fines and cargo clearance delays.
The ModelScale Architecture
We engineered an intelligent document processing pipeline using fine-tuned vision-language models, spatial OCR post-processing, and automated anomaly detection.
Key Production Components
- Custom fine-tuned Qwen2-VL model deployed on AWS Inferentia2 clusters
- Distributed Celery / Redis task queue scaling to 1,200 concurrent documents
- Automated human-in-the-loop review queue for low-confidence edge cases
Production Metrics
From 45 minutes down to 1.1 seconds per customs packet
Reallocated 350 staff members to higher-value operations
Zero customs compliance penalties across 6 months
Scope of Delivery & Architecture
Custom Model Fine-Tuning & Distillation
Adapting open-weight models (Llama 3, DeepSeek, Mistral) to your industry terminology, internal datasets, and exact output formats using LoRA and QLoRA.
High-Throughput Inference Architecture
Deploying low-latency model clusters using vLLM, TensorRT-LLM, and Triton Inference Server with dynamic batching and continuous token streaming.
Multi-Modal AI Pipeline Engineering
Developing systems that fuse text, vision, audio, and structured tabular data into unified intelligence workflows.
Automated Evaluation & Continuous Learning
Building automated evaluation loops (Evals) that test every release against gold-standard datasets to prevent hallucination regressions.
Modern, Scalable Technology Foundation
- vLLM
- TensorRT-LLM
- Triton Inference Server
- Ollama
- Unsloth
- Axolotl
- Hugging Face Transformers
- DeepSpeed
- Ray Serve
- Kubernetes / KServe
- FastAPI
- Celery
- NVIDIA H100 / A100
- AWS Inferentia2 / Trainium
- RunPod
- CoreWeave
How We Mobilize and Deliver
Data Synthesis & Baseline Evals
Data cleaning, synthetic data generation, and establishing automated ground-truth benchmarks.
Model Training & Pipeline Build
Fine-tuning, quantization (FP8/AWQ), and integrating business logic into APIs.
Infrastructure & Production Rollout
Kubernetes deployment, auto-scaling, latency optimization, and monitoring.
Transparent Engagement Tiers
Clear milestones, guaranteed IP ownership, and zero lock-in.
Production MVP
Custom AI microservice fine-tuned on your data and integrated into your current backend.
- Custom model adaptation & LoRA fine-tuning
- Sub-150ms inference API endpoints
- Integration with existing database & auth
- Complete source code and model weights
Enterprise AI System
PopularFull-scale multi-modal AI platform with auto-scaling GPU infrastructure, admin portal, and eval pipeline.
- Distributed GPU deployment on Kubernetes
- Real-time telemetry and automated eval loops
- Full CI/CD model retraining pipeline
- Dedicated Principal Engineer & QA team
- SOC 2 compliance documentation
Dedicated AI Pod
Full engineering team continuously shipping new AI features, optimizations, and integrations.
- 1 Lead AI Architect, 2 AI Engineers, 1 DevOps Engineer
- Weekly model fine-tuning and performance boosts
- Guaranteed response times and priority roadmap
Frequently Asked Questions
Who owns the code and trained model weights?
You do. 100% of the intellectual property, model weights, training scripts, datasets, and infrastructure code are owned exclusively by your company from day one.
Can our system be deployed on-premise or in our private AWS/Azure cloud?
Yes. We build cloud-agnostic architectures that deploy via Docker and Kubernetes into your existing AWS, Azure, GCP, or private bare-metal environments.
How do you prevent hallucinations in critical production workflows?
We implement multi-layered defenses: structured JSON schema validation, multi-agent verification, grounded vector RAG, and automated regression testing before outputs touch end-users.
Your next release runs on our stack.
Book a 30-minute strategy call. We'll audit one workflow, benchmark it against our best-of, and hand you a written roadmap — free.