Vocational resume coaching and mock interviews: helping students land offers
Client Profile
Pain Points
Coaching is labor-heavy
One-on-one resume editing strains teacher time and schedules.
Few mock interviews
Limited mock-interview sessions mean insufficient practice.
Slow feedback
Students get little timely feedback on applications.
Live Demo
How We Built It
Smart resume optimization
Optimize resume wording against the job JD with matching advice.
AI mock interviews
An AI interviewer asks role-based questions and follows up anytime.
Review reports
Auto-generate review reports and improvement lists after each session.
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
Role library
Cataloged target-role JDs and interview focus.
Resume tool
Built resume optimization and matching.
Interview assistant
Built AI mock interviews.
Student trial
Small-scale trial and refinement.
Rollout
Integrated into the standard employment-coaching flow.
Before vs After
“Students can practice interviews anytime and get review reports — noticeably more of them are getting offers.”
Where It Fits
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