AI & Machine Learning Company in Calgary

How clean is your historian? That answer, plus the depth of OT crossover, drives a Calgary AI number more than model choice does: a scoped AI proof of concept at Calgary energy rates over six to ten weeks, covering a data audit on your PI System or SCADA historian, a baseline model, and a hosted demo behind your VPN, a production ML model (pump failure prediction, leak detection, drilling optimisation, ESG anomaly scoring) including MLOps, drift monitoring, and an AIDA-aligned model card the AER and your internal audit team can defend, or a full production AI system with RAG over AER directives, multiple operational models, fine-tuning on field data, and integration into Maximo, SAP S/4HANA, or AVEVA PI System. Calgary rates align with Edmonton but sit slightly below Toronto, with a premium attached to OT/IT crossover work because finding engineers who understand both Kubernetes and SCADA is not cheap. We quote fixed fee.

The Artificial Intelligence and Data Act (AIDA) under Bill C-27 classifies systems as high-impact when they materially affect health, safety, or economic interests. Models that influence real-time operational control, well integrity decisions, worker safety alarming, or environmental compliance reporting almost always classify as high-impact, even though energy is rarely discussed in AIDA commentary. Our discovery phase runs the classification explicitly, and high-impact builds ship with a model card, a bias and fairness review where worker exposure or community impact is in scope, an incident reporting plan, and human oversight controls calibrated to API RP-755 fatigue and RP-754 process safety standards. Even pre-enactment, Imperial Oil, Cenovus, Enbridge, and Suncor model risk committees already demand AIDA-style governance because OSFI-equivalent scrutiny is coming and the Alberta Energy Regulator has publicly signalled interest in algorithmic accountability for safety-critical applications. We document everything so that when the rules harden you do not retrofit.

Yes. We have shipped models that operate inside Alberta Energy Regulator Directive 056 (Energy Development Applications), Directive 088 (Pipeline Safety), and Canadian Energy Regulator filing workflows. Patterns we routinely build include automated parsing of historical AER applications and decisions for precedent search, anomaly detection on pipeline pressure and flow data with alarm rationalisation under API RP-1167, leak detection tied into existing SCADA polling, document intelligence for CER Section 58 and 52 applications, and ESG carbon accounting aligned with the Canadian Net-Zero Emissions Accountability Act. Every safety-adjacent model ships with SHAP explainability so an AER inspector or your VP of Operations can see why the model flagged a specific event. We do not build closed black boxes for safety-critical use. Output logs feed Splunk, AVEVA PI System, or whichever historian your operations team already trusts.

When a project requires research that exceeds standard applied ML, we scope collaborations with Amii fellows or University of Calgary graduate labs through Hunter Hub rather than oversell in-house capacity. Amii's mandate explicitly covers Calgary and Edmonton jointly, so Calgary clients get the same access to fellows as the Edmonton headquarters. Schulich School of Engineering and the Department of Computer Science at UCalgary feed strong reservoir engineering and geoscience hybrid talent, particularly for subsurface ML. Our core team handles applied engineering, MLOps, and productionisation, which is where most Calgary AI projects actually stall after a promising PoC. For standard work (predictive maintenance, document RAG, fine-tuning, classical regression) no academic partner is required. We will tell you which bucket your problem fits and have introduced clients to Amii's industry sponsorship program when the roadmap and budget justified it.

There is no AWS, Azure, or GCP region inside Alberta yet, so we default to AWS ca-central-1 in Montreal, Azure Canada Central in Toronto, and GCP northamerica-northeast1 in Montreal for storage, training, and inference. Private connectivity from Calgary corporate networks runs over AWS Direct Connect, Azure ExpressRoute, or carrier MPLS, with separate air-gapped staging for OT workloads bridging to Fort McMurray and Cold Lake. For LLMs that must stay inside Canada under Alberta PIPA, CCCS guidance, or operator IP policy, we use Cohere's Canadian endpoints or self-host Llama 3 and Mistral on Canadian GPU instances. Cross-border use is acceptable only when a Privacy Impact Assessment and your IP counsel sign off, typically only for non-sensitive corporate tooling. Residency is documented in the model card and DPA so PIPA and the Alberta Privacy Commissioner have a clear answer.

A typical Calgary energy model (pump failure prediction, leak detection, well integrity scoring, drilling optimisation, refining demand forecast) takes sixteen to twenty-four weeks from kickoff to production, assuming clean historian data and AER documentation as part of scope. Week 1 to 4 is data audit against PI System or SCADA history. Week 5 to 12 is modelling, iteration, and challenger testing. Week 13 to 18 is MLOps, drift monitoring, shadow mode deployment behind your existing alarms, and internal model risk review. Week 19 onward is staged rollout, typically pad-by-pad or facility-by-facility, with operator training built into the schedule because control room adoption is what kills most energy AI projects, not modelling. If field data needs labelling or new sensors, add six to ten weeks. We have shipped on this cadence inside Imperial, Cenovus, and midstream stacks.

Most custom AI work qualifies for the federal Scientific Research and Experimental Development (SR&ED) program, which returns 35 percent refundable credit on eligible R&D spend for Canadian-controlled private corporations up to the $3M expenditure limit, and 15 percent non-refundable above. Eligible activity generally includes novel modelling on subsurface, midstream, or downstream data, architecture experimentation, algorithmic uncertainty, and systematic investigation, not routine integration. Alberta operators can stack the Alberta Innovation Employment Grant (IEG) at up to 8 percent for incremental qualifying R&D spend in Alberta, and the structure is designed to layer cleanly with SR&ED. We deliver time-tracked logs, technical narratives, experiment histories, and failed-hypothesis documentation aligned with the CRA T661 form and IEG schedule. Final eligibility sits with your SR&ED consultant and the CRA.

Remote oil sands and northern operations are the hardest AI deployment problem in Canada. Connectivity to Fort McMurray, Cold Lake, Kearl Lake, and Horizon is intermittent, OT networks are deliberately segmented under IEC 62443 zones and conduits, and any model that influences control loops must satisfy plant safety committees. Our pattern is to train models in ca-central-1 against historised data, package inference as containerised services that run on Honeywell Forge, AVEVA Edge, or dedicated edge GPUs at the facility, and sync results back to corporate through a unidirectional gateway or store-and-forward queue. Splunk and PI System act as the audit trail. Alarms are rationalised through your existing DCS rather than competing with it, and we never expose the control network to internet-facing endpoints. Calgary corporate gets the dashboard, the site gets the inference, and your OT security architect signs off in writing before anything goes live.