Applied AI & Machine Learning Systems
Production-grade LLMs, vector search, on-device vision models, and autonomous AI workflows.
Engineering precision from architectural conception to production scaling.
We design, build, and deploy production AI systems engineered for real-world reliability, sub-second latency, and strict data privacy. From domain-specific RAG pipelines to optimized on-device computer vision models, our engineering team takes AI from experimental notebooks to resilient production infrastructure.
Domain-Specific RAG & Vector Intelligence
Connect proprietary internal knowledge bases, ERP systems, and documents with hybrid semantic and keyword search, rerankers, and strict permission-aware retrieval.
On-Device & Edge Computer Vision
Lightweight, quantized vision models executing client-side on standard CPUs and mobile hardware (80–90ms inference) with 100% data privacy and zero cloud transit.
Autonomous Agentic Workflows
Deterministic, tool-calling multi-agent loops that automate complex multi-step workflows, reconciliations, and data validations with rigorous guardrails.
Production LLM Observability & Scoring
Automated test suites scoring accuracy, latency, hallucination rates, and cost per token before any model output touches end-users.
What We Engineer & Deliver
- Enterprise Retrieval-Augmented Generation (RAG) Architecture
- Sub-100ms On-Device Edge Computer Vision Systems
- Autonomous Multi-Agent Task Orchestration
- Fine-Tuned Open Source Models (Llama, Mistral, DeepSeek)
- High-Throughput Vector Search & Embedding Storage (Qdrant, Pinecone)
- Continuous Model Evaluation, Guardrails & Drift Monitoring
Architectural principles built into every deployment.
Zero-Data-Leakage Isolation
Deploy models within your VPC or on-premise infrastructure ensuring zero third-party training data exposure or unauthorized telemetry.
Hybrid Async Stream Pipelines
High-concurrency streaming responses utilizing Server-Sent Events (SSE) and WebSocket grids with dynamic fallback routing.
Cost & Token Optimization Layer
Semantic prompt caching and intelligent routing between frontier models and lightweight quantized local models, cutting operational API spend by up to 60%.
Core Technologies & Frameworks
See how we applied these engineering principles in production.
Review the technical architecture, problem statement, and measured outcomes in our detailed case study.
Frequently Asked Technical Questions
How do you ensure our corporate data isn't leaked or used to train public models?
All enterprise AI deployments are isolated within private VPCs (AWS, GCP, or on-premise). We use enterprise agreements with zero-retention guarantees or self-hosted open-source models (like Llama and Mistral) that never send data to external third parties.
What is the typical timeline for an enterprise RAG or custom AI system?
A production-grade Proof of Concept (PoC) is typically delivered within 3 to 4 weeks, with full production integration, evaluation suite, and security hardening completed within 8 to 12 weeks.
Can your AI models run on-device without high cloud server costs?
Yes. As demonstrated in our Noor Browser case study, we optimize and quantize computer vision and classification models to run directly on client CPUs and edge devices with 80–90ms latency and zero cloud compute cost.
Ready to engineer your applied ai & machine learning systems?
Speak directly with an engineering lead. We provide clear technical roadmaps, honest architectural tradeoffs, and fixed estimates before starting.