Ship AI software that actually works in production
Most machine learning projects never leave the notebook. Precision AI Core closes the gap between prototype and production with software infrastructure that deploys, monitors, and scales intelligent systems for real businesses in Quebec and beyond.
Six engines, one platform
Each module solves a specific production challenge. Combine them or deploy individually depending on where your pipeline bottlenecks.
Predictive analytics engine
Time-series forecasting and anomaly detection that learns from your operational data. Supports streaming ingestion from Kafka, RabbitMQ, or direct database polling with sub-second latency windows.
Computer vision pipeline
Object detection, segmentation, and classification models packaged as containerized microservices. Pre-trained on industrial datasets with fine-tuning APIs for your domain-specific imagery.
Natural language understanding
Multilingual text analysis supporting English and French out of the box. Entity extraction, sentiment scoring, document summarization, and semantic search powered by transformer architectures.
Automated model retraining
Drift detection triggers retraining pipelines automatically. Version every model artifact, compare performance against baselines, and promote winners without manual intervention or downtime.
Edge deployment toolkit
Compile models for ARM, NVIDIA Jetson, or x86 edge devices. Our quantization engine reduces model size by up to 80% while preserving over 97% of original accuracy on validated benchmarks.
Compliance and audit layer
Every prediction logged with full lineage. Meets SOC 2 Type II requirements and supports PIPEDA-aligned data residency. Export audit trails as structured reports for regulatory review.
The production gap is expensive
Research estimates that 87% of machine learning projects stall before reaching end users. The reasons are almost always infrastructure, not algorithms. Here is what our AI software eliminates from your workflow.
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Environment fragmentation
Data scientists use one stack, engineers use another. Our platform standardizes the runtime so models run identically from a laptop to a Kubernetes cluster without rewriting a single line.
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Silent model degradation
Models decay as real-world data shifts. Built-in statistical monitors detect concept drift within hours, not months, and trigger retraining before business metrics suffer.
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Scaling bottlenecks
A single-instance Flask server cannot handle enterprise traffic. Our serving layer auto-scales inference pods horizontally and batches requests to maximize GPU utilization at any load.
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Regulatory uncertainty
Canadian privacy law requires clear data governance. Every data transformation, model input, and prediction output is versioned and traceable through our compliance layer.
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Vendor lock-in
We deploy on your cloud, your on-premise servers, or a hybrid topology. No proprietary formats — export models as standard ONNX or TorchScript artifacts at any time.
Connects with the tools you already run
Native connectors and webhook-based adapters let you wire Precision AI Core into existing data pipelines, monitoring stacks, and business applications without custom middleware.
From first call to live inference in four phases
We follow a structured deployment methodology that de-risks each stage and keeps stakeholders aligned on measurable outcomes.
Discovery and data audit
We map your data landscape, identify high-value prediction targets, and assess infrastructure readiness. This phase typically runs two weeks and produces a prioritized roadmap with expected ROI ranges for each use case. No commitment beyond this point.
Proof of concept build
A working prototype on real data, deployed in a sandboxed environment. We validate model accuracy against your domain benchmarks and surface any data quality issues before investing in production hardening. Duration: three to five weeks.
Production hardening
Containerization, load testing, failover configuration, and integration with your CI/CD pipeline. We set up monitoring dashboards, alerting rules, and the automated retraining loop. Security review and penetration testing happen here.
Ongoing optimization
Post-launch, our platform continuously measures model performance, retrains on fresh data, and surfaces optimization opportunities. Monthly review sessions keep your team informed and in control of the AI lifecycle.
Things teams ask before signing
Tell us what you are trying to automate
Describe your use case and we will respond within one business day with an honest assessment of feasibility, estimated timeline, and whether our platform is the right fit.