AI-Driven Engineering
We build AI systems that run in production — not proof-of-concept demos. From intelligent pipelines and LLM integrations to on-premises inference engines, our AI work is grounded in engineering rigour and real-world constraints.
AI Capabilities, Production-Ready
Every AI engagement we take on is engineered to run at scale, within real infrastructure and operational constraints.
LLM & Generative AI Integration
Integrate large language models into your product workflows — RAG pipelines, domain-specific fine-tuning, structured output generation, and enterprise-safe deployment patterns.
ML Model Development & Training
Custom model development for classification, detection, regression, and recommendation use cases — from dataset preparation and training pipelines to evaluation and deployment.
AI Pipeline Architecture
Design and build high-throughput AI inference pipelines — frame pre-filtering, multi-model orchestration, result collation, and event dispatch — optimised for latency and compute efficiency.
Intelligent Automation
Replace manual, repetitive workflows with AI-driven automation — document processing, decision routing, anomaly detection, and event-triggered action pipelines across your operations.
On-Premises AI Inference
Deploy AI where data privacy demands it — on-site inference engines that eliminate cloud video streaming, reduce bandwidth costs, and satisfy data-sovereignty requirements for regulated industries.
AI Analytics & Dashboards
Surface AI-generated insights through real-time dashboards and reporting layers — making model outputs actionable for operators, managers, and decision-makers without technical overhead.
AI Built by Engineers, Not Researchers
We approach every AI engagement as a systems engineering problem — with real constraints, real data, and real production requirements.
Production-First Mindset
We don't build models that only work in notebooks. Every AI system we ship is designed for production: fault-tolerant, observable, and optimised for real operating conditions.
Compute Efficiency as a Core Discipline
We've engineered AI systems that run 7 concurrent capabilities on commodity on-premises hardware by eliminating over 90% of redundant computation before any model is invoked.
Full-Stack Delivery
From data pipelines to inference engines to the API layers and dashboards that surface results — we own the full AI stack, not just the model component.
Privacy & Data Sovereignty
We design AI deployments that keep sensitive data on-premises when required — no mandatory cloud upload, no per-frame API cost, no data-residency compromise.
AI in Production
Case studies from live AI systems we've designed, built, and deployed.
SajagAI Platform
An end-to-end intelligent video surveillance ecosystem with on-premises AI inference — intrusion detection, vehicle recognition, and litter detection running on commodity hardware across multiple sites.
Compute-Efficient Video AI
A pre-filter-first architecture that eliminates over 90% of video frames before any deep learning model is invoked — enabling 7 concurrent AI capabilities on commodity hardware.
Ready to build production AI?
Whether it's a greenfield AI product or integrating intelligence into an existing system — we're ready to engineer it.