Generative AI
Conversational applications, business copilots, specialized agents, Anthropic Claude, Mistral, Gemini, OpenAI models.
Loading...
Loading...
Access International designs production-grade AI applications: conversational agents, business automations, RAG pipelines, data platforms, and decision dashboards. Every delivery is backed by a quality audit, a bias audit, and a confidentiality audit.
Conversational applications, business copilots, specialized agents, Anthropic Claude, Mistral, Gemini, OpenAI models.
Retrieval-augmented generation pipelines, vector indexing, LlamaIndex and LangChain orchestration, response-quality monitoring.
Multi-agent orchestration, Model Context Protocol (MCP), n8n workflows, end-to-end process automation.
Industrializing AI data pipelines: Databricks architectures, lakehouses, ETL/ELT pipelines (Pentaho, Airflow, dbt), semantic modeling, enterprise data cataloging.
Power BI, executive dashboards, tabular models, re-architecture of legacy reporting systems.
ElevenLabs voice synthesis, HeyGen avatars, transcription, image and video processing, multilingual ASR.
AI is not delivered on the strength of a prototype. Every system goes live with signed audits, measured quality thresholds, and a risk register.
Response benchmark, supervised evaluation, continuous scoring. Quality is not a feeling, it's a measurement.
Detection of language drift, cultural and gender bias. Auditable public report.
Flow mapping, personal data filtering, pseudonymization, Quebec Law 25 and GDPR compliance.
Logging of queries and responses, reproducibility, versioning of prompts and models.
Decision-support AI agent for a North American administration. Natural-language access to the regulatory documentation base.
Semantic qualification platform for editorial content, NLP rules and continuous learning.
Product recommendation engine and marketing-agent orchestration, integrated with the client CDP.
Document-agent automation: clause extraction, compliance control, pre-validation.
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.
We qualify the use case, target architecture, and quality bench. A thirty-minute first conversation rules out the classic pitfalls.