Generative AI Company in Calgary

Corpus size and evidence burden are what move a Calgary GenAI budget. A scoped generative AI proof of concept at Calgary rates over six to ten weeks covers a RAG pipeline against your operator-specific corpus, an evaluation harness, and a hosted demo; a production RAG or document-extraction system adds operator-grade ingestion, evaluation, prompt injection defences, and OSFI E-23 or AER evidence packs, scaled by corpus size and integration scope; a full enterprise generative AI platform (Pathways Alliance ESG automation at fleet scale, WestJet MRO log copilot, Neo Financial banking back-office) includes fine-tuning, evaluation infrastructure, and human-in-the-loop UX. Calgary rates sit at par with Edmonton and ten to twenty percent below Toronto. We are fixed-fee in CAD, with Alberta Innovates and Emissions Reduction Alberta program funding credited against eligible portions where applicable.

Alberta PIPA (Personal Information Protection Act) and federal PIPEDA both require explicit consent or a legitimate business purpose for using personal information in AI training, plus a Privacy Impact Assessment for high-risk processing. Our standard pattern uses operator-controlled corpora (AER filings, well logs, internal reports, customer correspondence with consent) and explicitly excludes any data where consent is ambiguous. Training and fine-tuning run in AWS ca-west-1, ca-central-1, or Azure Canada Central with no cross-border data movement except where a documented PIA signs off. We coordinate with the operator's Privacy Officer (named function inside Suncor, Cenovus, ATB Financial, and WestJet) and produce a PIA aligned with the Office of the Information and Privacy Commissioner of Alberta's guidance plus the OPC's Principles for responsible AI. Personal data is never logged in prompts or completions; redaction runs at the gateway.

Yes. OSFI Guideline E-23 (Model Risk Management) applies to all models used by federally regulated financial institutions, including LLMs. Our generative AI systems for Calgary banking clients produce the artefacts E-23 explicitly demands: a model inventory entry, a model risk tier (high or medium typically for customer-facing LLM applications), an independent model validation report (we coordinate with internal Validation or an external validator like Deloitte or KPMG), a model card with intended use, limitations, training data lineage, and known biases, a challenger model where appropriate, ongoing performance monitoring against drift, and a documented incident response plan. FCAC fair treatment of consumer rules apply to retail-facing models, with explicit human-in-the-loop gates on adverse action. We have patterned implementations after public work from RBC's Borealis AI and TD's Layer 6 AI, adapted for Calgary scale. Bill C-27 AIDA classification is produced alongside.

Domain-specific document parsing is one of our deepest Calgary generative AI capabilities. AER filings (Directive 056, 060, 084) ship as structured PDF and submission portal exports; we parse with AWS Textract or Azure Document Intelligence augmented by a custom AER schema mapper that pulls licence number, BA code, surface UWI, bottomhole location, and operational parameters into Pinecone-equivalent vector storage. Well logs in LAS 2.0, LAS 3.0, and DLIS format need specialised parsers we have built in-house against the Society of Petrophysicists and Well Log Analysts (SPWLA) standards, normalising curves like GR, NPHI, RHOB, and ILD into both vector and structured representations. WITSML real-time feeds chunk by depth interval. Pipeline ILI reports from operators like Pembina, Enbridge, and TC Energy include in-line inspection vendor outputs (Rosen, NDT Global, GE PII) with axial location, defect classification, and depth-of-wall data we extract into both narrative chunks and structured anomaly tables. Operator-specific abbreviations (e.g., Suncor versus Cenovus naming) get a controlled-vocabulary normaliser in the ingestion pipeline.

There is no universal answer, and we tell operators that explicitly. Cohere Command R+ wins on Canadian sovereignty (the deepest Canadian-only inference option), retrieval-tuned grounding, and procurement simplicity (Cohere is a Toronto-headquartered Canadian company). Anthropic Claude through AWS Bedrock in ca-central-1 wins on reasoning depth, long-context handling for well logs and ILI reports, and constitutional AI safety properties. Azure OpenAI in Canada Central wins on Microsoft-shop integration (Suncor and many midstream operators standardise on M365) and tool calling. Self-hosted Llama 3.3 70B, Mistral Large, or Qwen 2.5 on AWS ca-west-1 H100 instances wins when AIDA explainability, AER audit requirements, or operator IP sensitivity rule out hosted frontier models. We run a one-week selection workshop weighing your residency requirements, integration footprint, latency budget, cost per million tokens, and roadmap, then pick deliberately. We do not default to GPT-4 because the marketing said so.

Production prompt injection is the single most common Calgary generative AI failure mode, and it kills enterprise deployments faster than poor model quality. Our defence-in-depth pattern: input filtering at the gateway against known injection patterns (Lakera Guard, Microsoft Prompt Shield, custom regex); document chunking with provenance tags so retrieved content cannot impersonate system instructions; structured prompts that cleanly separate retrieved content from user input through delimiters and roles; output filtering against PII leakage, internal URL exfiltration, and policy violation; tool call allowlisting where the agent can only call named safe functions, not arbitrary code; human-in-the-loop gates on any state-changing or AER-bound action. We run red-team exercises pre-launch covering OWASP LLM Top 10, including LLM01 (Prompt Injection), LLM02 (Insecure Output), LLM06 (Sensitive Info Disclosure), and LLM09 (Overreliance). Every production prompt is versioned, evaluated, and rolled out behind a feature flag with kill switches the operator's incident response team can pull.

A typical Calgary generative AI project (RAG over AER filings and well logs, ESG report drafting, banking KYC extraction, MRO log summarisation) takes twelve to twenty weeks from kickoff to production, assuming clean source data and an internal champion engaged from week one. Week 1 to 3 is regulatory scoping, AIDA classification, residency map, and corpus inventory. Week 4 to 9 is ingestion pipeline build, RAG architecture, evaluation harness, and prompt engineering against a hold-out set graded by operator subject-matter experts. Week 10 to 14 is red team, prompt injection defence, human-in-the-loop UX, and OSFI E-23 or AER evidence pack drafting. Week 15 to 18 is staging deployment with shadow-mode evaluation against analyst baseline, and stakeholder sign-off. Week 19 onward is production rollout, monitoring, and drift detection. Fine-tuning extends timelines by four to eight weeks. We hit this cadence on MST hours with synchronous standups, not overnight handoffs.

Most novel generative AI work qualifies for the Scientific Research and Experimental Development (SR&ED) program, which returns 35 percent refundable credit on eligible R&D spend for Canadian-controlled private corporations (CCPCs) up to the $3M expenditure limit, and 15 percent above. Eligible activity typically includes novel domain-specific parsing under uncertainty (e.g., LAS or DLIS embeddings for upstream RAG), fine-tuning experimentation, evaluation methodology design where standard benchmarks fail, prompt injection defence research, and systematic investigation around AIDA explainability. Routine RAG over a generic corpus is not eligible. Alberta Innovates AICE (Applied Innovation Catalyst), Emissions Reduction Alberta BEST Challenge for methane or emissions-related AI, and the federal Strategic Innovation Fund can stack on eligible portions. We deliver time-tracked logs, technical narratives, evaluation result histories, and failed-hypothesis documentation aligned with CRA T661. Final eligibility sits with your SR&ED consultant, Alberta Innovates program officer, and the CRA.