Their generative AI is in production. Audited, measured, governed.

We produce a use-case-specific test bench, run continuously on every model or prompt change. Quality metrics are published in a dedicated dashboard with alerts on degradations. No change is deployed without passing the bench.

We select the model case by case: Anthropic Claude for complex reasoning and writing, Mistral and open models for sovereign or on-premise deployments, Gemini for multimodal, OpenAI when the client ecosystem requires it. The choice is justified by a comparative bench.

We map flows upstream, filter personal data before sending to the model, pseudonymize when necessary, and host regionally. Quebec Law 25 (Canada) and GDPR (EU) environments are isolated. Prompts and responses are logged in a compliant way.

Cloud (Azure OpenAI, AWS Bedrock): fast deployment, managed models, pay-per-use. On-premise (vLLM, Ollama, open-source Llama/Qwen/Mistral): full sovereignty, maximum confidentiality, fixed costs. Recommendation case by case based on regulatory constraints and data sensitivity.

6 to 12 weeks for a POC validated in production on a restricted corpus (a few thousand documents). 3 to 6 months for a scaled deployment (large corpus, multi-tenant, SLA). Quality bench in place from the POC phase to measure continuous improvement.

Depends on volume. Below ~1M tokens/day: cloud (Claude, GPT) is usually more economical. Beyond that: self-hosted with dedicated GPUs becomes competitive. We compute the precise tipping point in the pricing document, with 1/3/5-year projections.

Three mandatory audits: quality, bias, confidentiality. Observability dashboards (LangSmith, LangFuse). Prompt + model versioning. Clear retention policies. Regional hosting with tenant isolation. Full audit trail documentation for inspections. Quebec Law 25 / GDPR / sector compliance.

Our developers use Claude Code, Copilot, Cursor daily to generate boilerplate, tests, documentation, refactoring. Productivity x2-3. Structured practice: mandatory human review, versioning of strategic prompts, monthly quality audit on AI-generated vs manual code. Capacity building transferred to the client.

Yes, systematically. Skills transfer is planned from Discovery: pair-programming on RAG patterns, monthly workshops, documentation of prompts and models. On delivery, the client team is autonomous on the evolution and operation of the AI system.

Five criteria we apply on every engagement, in this order. (1) Use case fit — reasoning depth, instruction following, multilingual quality vary across models; benchmark on the actual client task before deciding. (2) Data residency and confidentiality — Quebec Law 25 and GDPR constrain where data can be processed; some models offer EU-only or Canada-only inference, others don't. (3) Latency and throughput — interactive use cases (customer-facing chat) need sub-2-second responses, batch use cases tolerate seconds-to-minutes. (4) Total cost of ownership — token pricing, fine-tuning cost, self-hosting feasibility on Mistral or Llama if confidentiality requires it. (5) Vendor risk — pricing changes, deprecation policies, model lock-in. Our position is vendor-agnostic: model selection is a use-case decision, not an organizational allegiance. We benchmark before committing. See the AI / Data / Automation expertise.

It depends on volume, confidentiality, and team capacity. Managed API (Anthropic, OpenAI, Mistral, Azure OpenAI) is the right default below ~10M tokens per month: zero infrastructure to manage, latest models, billed per use, reliable. Self-hosted (vLLM, Ollama, TGI on H100/A100 GPUs with open-source models like Llama, Qwen, DeepSeek, Mistral self-hosted) becomes economic above ~10-50M tokens per month, OR when confidentiality blocks public cloud LLMs. The hidden cost of self-hosting is rarely the GPU bill — it's the 1-2 FTE needed to operate it reliably. The frequent third option is Azure OpenAI in a private tenant with VNet integration and customer-managed keys: best of both worlds for regulated organizations that want managed but compliant. We benchmark on the client's actual workload before committing. See the AI / Data / Automation expertise.