What the client needed
Automotive retailers needed an AI layer that could answer inventory and policy questions accurately - not generic chatbot replies that erode trust on high-consideration purchases.
Data lived across disconnected feeds, CRM exports, and operational spreadsheets. Customer conversations had to hand off cleanly between digital channels and showroom staff.
How SparkScribe approached it
Before build, SparkScribe worked with Patti to translate their AI Platform goals into an actionable plan - not an off-the-shelf template.
Discovery & planning
- Audited dealer inventory feeds, CRM exports, and policy documents to ground AI answers in real data.
- Designed human review hooks for high-stakes automotive questions before responses reach customers.
- Planned handoff paths between digital chat, phone, and showroom staff around existing DMS constraints.
Our engagement covered AI integration, API design, Workflow automation, Admin tooling - scoped in phases with staging parity and admin self-service so Patti could run day-to-day operations without waiting on engineering.
How we solved it
We built a Python services layer combining FastAPI and Flask endpoints with Airtable-backed operational data and LLM orchestration grounded in dealer-specific catalog and policy context.
Human review hooks flag high-stakes answers before they reach customers. Integration patterns accommodate API, export, or middleware connections depending on what each dealer's DMS exposes.
How we helped the client
Patti shipped as a production AI engagement layer tuned for automotive retail workflows rather than a generic demo bot.
- Grounded answers: Responses reference live inventory and dealer policy where data is available.
- Operational fit: Workflows align with how showroom and digital teams already collaborate.
- Review safety: Escalation paths protect brand tone on sensitive customer questions.
Technologies we used
Technologies we used
- Python
- FastAPI
- Flask
- Airtable
- OpenAI
How we applied the stack
FastAPI and Flask services orchestrate LLM calls with Airtable-backed operational data. OpenAI generates responses within dealer-specific context windows. Integration patterns adapt to each dealer's DMS - API, export, or middleware - without forcing a single vendor stack.
