GenAI & agentic systems that survive real users, real cost ceilings.
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.
GenAI is moving from chat demos to workflow-embedded systems.
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.
- Yesterday's GenAI deployment
- 01Single LLM behind a chat box, answering from training data - hallucinations and confident-but-wrong responses.
- 02Customer-support GenAI billed at frontier-model prices for every query - economics broke at scale.
- 03Edtech "AI tutoring" without curriculum grounding - answers contradict the textbook the student is reading.
- 04Knowledge-work AI that can't take any action - produces summaries, not outcomes.
- 05No fallback when the model is uncertain - confident hallucination over honest refusal.
- Today's expectation
- 01RAG-grounded responses citing the source document - refusal when the answer isn't in the corpus.
- 02Cost-tiered routing - small model for easy queries, frontier model only when needed. 80% cost reduction without quality loss.
- 03Curriculum-grounded edtech assistants - answers tied to the syllabus, the textbook, the course context.
- 04Agentic systems - plan, retrieve, call tools, execute. Outcomes, not summaries.
- 05Honest uncertainty - escalate, refuse, or surface the gap. Never hallucinate.
Where AiSPRY plugs in across GenAI & agentic systems.
RAG, agentic frameworks, fine-tuned LLMs, and cost-tiered routing - built for consumer scale and cross-industry knowledge work.
Curriculum-grounded AI tutors
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.
Cost-tiered support GenAI
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.
Personalisation & demand
SKU-level demand forecasting, prescription forecasting, customer-segment models, and personalisation pipelines - built on the same forecasting stack used in healthcare and industrial.
Agentic workflow automation
Plan → retrieve → call tool → execute → verify. Built on AutoGen, CrewAI, Flowise. Outcomes, not summaries. Used for procurement workflows, research, and cross-system reconciliation.
Multi-format extraction & reasoning
PDF, scanned forms, contracts, statutes - extraction with OCR + layout-aware models, then reasoning over the structured output. Audit-trail logged.
Fine-tuning & eval pipelines
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.
Cross-industry engagements that survive real users.
Real catalogs, real cohorts, real procurement teams.
Explore every Consumer & Cross-Industry case study - challenge, solution, impact.
Programme-aware learning assistants, procurement copilots, and cost-tiered GenAI stacks - grounded in client data, not in vibes.
We don't ship GenAI that can't survive a CFO review.
- ✓
Cost-tiered by default
Frontier models for the queries that need them. Small models for the 70% that don't. Per-conversation cost ceilings enforced - not aspirational.
- ✓
RAG-grounded, refusal-capable
Source-cited answers. Honest refusal when the corpus doesn't have it. No hallucinated entitlements, no fabricated stats.
- ✓
Agentic when warranted
AutoGen / CrewAI / Flowise - used where plan-and-execute is genuinely needed, not because it's the trend.
- ✓
Eval-gated deploys
Ragas / TruLens in CI. Drift caught before it ships. The eval suite is part of the deployable, not a one-time exercise.
- ✓
One stack across industries
The same forecasting / RAG / agentic spine that runs in healthcare, industrial, and public sector - adapted to consumer cost / latency realities.
If it can survive a CFO review at quarter-end, it ships.
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.