Generative & conversational AI built for teams who still sign the emails
Mind Nexus AI delivers applied-AI studio engagements across discovery, conversational AI, RAG knowledge systems, workflow automation, custom LLM apps and evaluation design. Every discipline includes human-in-the-loop architecture and honest scoping—we sell professional services, not courses or subscriptions.
Before you fine-tune a model nobody asked for, we map where generative AI and conversational AI actually reduce friction in your organization. Discovery workshops with product, support and ops leaders produce a prioritized backlog: which workflows deserve a copilot, which need RAG over internal docs, and which should stay human-only. We assess data readiness, PIPEDA implications and integration constraints with your existing stack. Deliverables include a phased roadmap, CAD budget ranges, success metrics you can report to the board, and explicit kill criteria when a use case fails evaluation. This is consultancy and studio scoping—not accredited training or a SaaS trial.
We design and build assistants that match how your team actually speaks to customers and each other—not generic chat widgets. Work covers conversation design, tone and voice guardrails, integration with CRM or ticketing APIs, and escalation paths when confidence scores drop. Whether the stack uses ChatGPT, Claude, Copilot or an open-weight model on your infrastructure, we implement prompt libraries your internal owners can maintain. Every deployment includes logging, feedback capture and a human review queue. Adoption is never guaranteed; we optimize for trust and source citations, not vanity conversation counts.
No. 03 — C$40,000–110,000 project
Knowledge Systems & Retrieval (RAG)
Grounded answers require more than plugging a PDF into a vector database. We engineer retrieval pipelines: chunking strategy, metadata filters, hybrid search, re-ranking and hallucination checks against your knowledge base. Source citations appear in the UX so users can verify before acting. For Canadian clients we document cross-border processing and PIPEDA-aligned retention. Retrieval quality is measured with an evaluation suite tuned to your domain—legal clauses, product SKUs, internal policies. Results depend on document hygiene and ongoing curation your team maintains.
No. 04 — C$28,000–75,000 project
Generative Content & Workflow Automation
Some teams need drafts, summaries and routing—not open-ended chat. We build copilot workflows that generate structured outputs, push them through approval gates, and write back to systems you already use. Automation includes exception handling: when the model flags uncertainty, the task returns to a human with full context. We avoid black-box pipelines that break silently on the first edge case. Scope covers prompt engineering, API integration, and monitoring hooks so ops can see throughput and error rates without reading raw logs.
No. 05 — C$50,000–140,000 project
Custom LLM Apps & Fine-tuning
When general models miss your vocabulary—clinical codes, regulatory language, product nomenclature—we build custom LLM applications and selective fine-tuning on data you control. Work includes data preparation, model evaluation against holdout sets, inference cost modelling and deployment on cloud or client infrastructure. Fine-tuning is not magic: we set expectations on drift, retraining cadence and human review for high-stakes outputs. IP on custom weights and prompts stays with you under contract terms we define during discovery.
Shipping an assistant without an evaluation suite is how confident wrong answers reach customers. We design red-team scenarios, automated regression tests, retrieval quality benchmarks and production monitoring aligned to responsible-AI practice. Human-in-the-loop design specifies who reviews what, within what SLA, and how disagreements get logged for model improvement. Governance documentation supports enterprise procurement and PIPEDA audits. We do not certify perfection—we make failure visible early.
Discuss scope and CAD ranges
Tell us about your use case. We will respond with an honest view of feasibility and a suggested discovery path.
