Computer Vision Engineered to Survive Past the Demo.
From identifying where AI can create value to building production applications and computer vision systems. The focus is not AI for its own sake — it is reliable software that improves an actual workflow.
Three Ways to Engage
Video pipeline engagements structured to eliminate technical uncertainty before scale. Fixed scope milestones, absolute code ownership.
Pipeline Audit & Feasibility
Systematic analysis of video ingestion and hardware bottlenecks. We benchmark stream stability, evaluate frame rates, and define the exact deployment architecture before writing production code.
- 01RTSP / camera stream stability audit
- 02Latency bottleneck & frame-drop analysis
- 03Edge vs. cloud compute sizing & specs
- 04Architecture blueprint & fixed estimates
Model Inference & Edge Acceleration
High-performance model engineering for live video feeds. We convert heavy algorithms into low-latency runtimes optimized for GPUs, embedded systems, and continuous multi-camera feeds.
- 01TensorRT compilation & FP16/INT8 quantization
- 02Sub-30ms detection & multi-object tracking
- 03Zero-copy GPU memory optimization
- 04Thermal & hardware resource balancing
Full-Scale Computer Vision Systems
End-to-end visual system engineering. From physical camera stream decoders to production gRPC backends, continuous telemetry, and automated business event triggers running 24/7.
- 01Automated video & sensor analytics pipelines
- 02Industrial inspection & real-time monitoring
- 03High-throughput gRPC / streaming APIs
- 04Production observability & failover handling
Five-Stage Methodology
"Reduce uncertainty early. Invest once it is earned." Each phase produces verifiable telemetry before subsequent architectural investment.
Understand
Analyze domain logic, map human checkpoints, quantify error thresholds and establish hard latency targets.
Validate
Prototype baseline models against real messy inputs. Determine feasibility score before system build.
Build
Engineer resilient pipelines with fallback branches, deterministic tools, schemas, and verification harnesses.
Productionize
Deploy containerized services to edge or cloud with telemetry logs, error alerting, and active security gates.
Improve
Continuous eval suites, drift detection, active learning loops, and team engineering handover.
Where Gstream Fits
Operations Teams
High manual toil in repetitive document workflows, invoice processing, order reconciliation, and multi-source ERP verification.
Startups & Software Companies
Product teams requiring hardened AI capability baked directly into their SaaS runtime without incurring brittle hallucinations.
Industrial & Visual-Data Teams
Physical operations handling massive feeds of images, video streams, or high-resolution architectural scans requiring real-time answers.
Engineering Principles
AI where uncertainty is useful
Deploy probabilistic inference exclusively for synthesis, semantic categorization, and fuzzy pattern parsing.
Deterministic software where rules should be reliable
Financial calculations, data transformation schemas, and state updates must remain strictly deterministic.
Human approval where mistakes matter
Design confidence thresholds that automatically route edge cases to human operators with clean decision diffs.
Evaluation before claiming improvement
Quantitative baseline eval harnesses run against real production datasets before claiming any automated victory.
Production observability
Structured telemetry across latency, token efficiency, confidence drift, and tool error rates embedded by default.
Maintainable architecture & clear handover
Clean modular code repositories, reproducible Docker configurations, and comprehensive technical documentation.
Initiate an Architecture Review
Gstream operates on low-volume, high-density technical engagements led directly by Kaspars Polis. We evaluate production viability within 48 business hours.
Applied AI and computer vision software engineer specializing in deterministic pipelines and high-performance video media runtimes.