3C exporter quoting and RFQ response: inquiry responses from hours to minutes
Client Profile
Pain Points
High RFQ volume
Dozens of RFQs a day; slow replies lose to competitors.
Complex quoting
Many specs and complex price tables make manual quotes error-prone.
Late follow-ups
Inquiries lack systematic follow-up, leaking potential orders.
Live Demo
How We Built It
RFQ parsing
Auto-parse RFQ requirements into specs and quantities.
Auto quoting
Calculate and generate quotes from price tables and rules.
Follow-up reminders
Auto routing, follow-up reminders and script suggestions.
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
Price system
Organized price tables and quoting rules.
RFQ parsing
Trained RFQ requirement parsing.
Quoting system
Built auto quoting.
Trial
Sales staff trial.
Full use
Integrated into daily inquiry handling.
Before vs After
“RFQs are answered almost instantly with accurate quotes, and our win rate is clearly up.”
Where It Fits
Related Cases
AI Translation & Lead-gen Assistant
Built an AI translation and lead-gen assistant for a trading company to break language and labor bottlenecks.
GuangzhouAI Sample Images & Catalog Generation
Built a sample-image and catalog generation pipeline for a garment exporter to speed up sample communication.
JiangmenAI Multilingual Website & Inquiry Follow-up
Built a multilingual website and inquiry follow-up system for a hardware exporter to lower the overseas acquisition barrier.