AI teaching assistant for a training provider: halve curriculum costs, no more late-night Q&A
Client Profile
Pain Points
High curriculum costs
A course from outline to handouts to question bank takes weeks.
Q&A eats teacher time
Teachers drown in repeated questions and can't polish courses.
Slow lead-gen content
Ad copy, short videos and posters are improvised on demand.
Live Demo
How We Built It
AI teaching assistant
A knowledge-base-grounded assistant answers common questions and grades homework.
Course content generation
Input a topic to auto-generate outlines, handouts and question banks for review.
Batch enrollment content
Auto-generate enrollment copy, video scripts and poster text.
Not betting on one AI — combining the right few
For a real business scenario, we don't lock in a single technical path up front. We evaluate candidate paths on the same benchmark, then design a hybrid so each path does what it's best at.
General LLM API
Public-cloud models such as GPT-4o / Qwen / DeepSeek
- ✓Out-of-the-box, minimal integration cost, cheap to start
- ✕Limited generalization to vertical domains, unstable field-level accuracy, separate compliance design
Industry fine-tuned model
Domain fine-tuning of open-source models on industry data
- ✓Grasps industry semantics, much higher accuracy, private deployment
- ✕Needs labeled data plus ongoing training and upkeep
RAG + rule engine
Retrieval-augmented knowledge plus business rule checks
- ✓Highly targeted, explainable, best latency and cost
- ✕Requires continuous data-loop investment
Fusion, not selection
What we deliver is not a single model but a fused pipeline of primary path + semantic correction + graceful fallback: routine cases are handled by the industry model in seconds; low-confidence fields are re-understood by an LLM; on service anomalies the system degrades to a general API and flags manual review — achieving accuracy, cost, latency and controllability at once.
An explainable, degradable, evolvable AI pipeline
We treat AI engineering as a pipeline, not a black box: every step has clear responsibilities, inputs/outputs and fallbacks. The five-step pipeline plus four-layer architecture below is the technical skeleton.
Ingestion & access layer
Multi-channel access with unified auth, idempotency and masking
AI inference layer
Multi-path inference plus a confidence router switching by features
Business orchestration layer
Connecting results to real business flows with rule checks and closure
Data loop layer
Every online error flows back; models iterate monthly
Tech Stack
Delivery Process
Workflow mapping
Mapped curriculum and Q&A flows.
Knowledge base
Structured course materials into a knowledge base.
Assistant launch
Assistant starts with basic Q&A.
Content generation
Added curriculum and enrollment content generation.
Iteration
Continuous assistant improvement from student feedback.
Before vs After
“The AI assistant absorbs most repeated questions, so teachers finally have time for what really matters.”
Where It Fits
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