Education platforms, retail experiences, customer-support functions, and cross-industry knowledge work - all share the same problem: GenAI demos are easy, GenAI in production at consumer scale is not. AiSPRY's RAG, agentic, and fine-tuned LLM stacks are built for the cost / latency / accuracy triangle that actually matters.
A chatbot impresses for two weeks. A workflow-embedded GenAI system runs for years. The bar is no longer "can it answer" - it's whether it can route, retrieve, refuse, escalate, and stay within a per-conversation cost ceiling. That's the bar AiSPRY's cross-industry deployments are built to.
RAG, agentic frameworks, fine-tuned LLMs, and cost-tiered routing - built for consumer scale and cross-industry knowledge work.
RAG-grounded learning assistants citing the syllabus, the textbook, and the lecture transcript. Trained on AiSPRY's own LMS for analytics-first delivery - capstone-led, university-credited programs.
Small model handles 70%+ of queries; frontier model called only when complexity demands. RAG over product docs, ticket history, policy. Fallback to human for ambiguity. Per-conversation cost ceilings enforced.
SKU-level demand forecasting, prescription forecasting, customer-segment models, and personalisation pipelines - built on the same forecasting stack used in healthcare and industrial.
Plan → retrieve → call tool → execute → verify. Built on AutoGen, CrewAI, Flowise. Outcomes, not summaries. Used for procurement workflows, research, and cross-system reconciliation.
PDF, scanned forms, contracts, statutes - extraction with OCR + layout-aware models, then reasoning over the structured output. Audit-trail logged.
LoRA / QLoRA fine-tuning where domain language matters; Ragas / TruLens evaluation harnesses; prompt-engineering systematised. We don't deploy a GenAI feature without an eval suite that catches drift.
Real catalogs, real cohorts, real procurement teams.
Programme-aware learning assistants, procurement copilots, and cost-tiered GenAI stacks - grounded in client data, not in vibes.
Frontier models for the queries that need them. Small models for the 70% that don't. Per-conversation cost ceilings enforced - not aspirational.
Source-cited answers. Honest refusal when the corpus doesn't have it. No hallucinated entitlements, no fabricated stats.
AutoGen / CrewAI / Flowise - used where plan-and-execute is genuinely needed, not because it's the trend.
Ragas / TruLens in CI. Drift caught before it ships. The eval suite is part of the deployable, not a one-time exercise.
The same forecasting / RAG / agentic spine that runs in healthcare, industrial, and public sector - adapted to consumer cost / latency realities.
Talk to AiSPRY about a RAG-grounded support assistant, an agentic workflow rollout, a curriculum-grounded edtech tutor, or a cost-tiered GenAI re-architecture for a deployment that's already live but bleeding budget.