CARMA AI: a parts selection agent for Dubai
For carma.team we built an AI agent for the first layer of parts requests. It accepts a VIN, photo or car parameters, asks clarifying questions, checks catalog access, finds applicable OEM numbers and prepares a structured answer for the customer or manager.
Not a generic chatbot, a checked parts workflow
The agent does not guess the part. It follows a controlled process: user status, catalog access, vehicle identification, part clarification, OEM lookup and handoff to a human specialist when the request becomes complex.
VIN and AUTO flows
VIN/Frame lookup, OCR from photos and parameter-based vehicle selection by make, model, year and engine.
OEM lookup
After the vehicle is identified, the agent moves from part name to applicable OEM numbers and diagrams.
n8n orchestration
n8n controls the scenario branches, webhook triggers and calls between the chat, backend and external APIs.
Django + Redis
The backend handles service logic, tokens, logs and responses, while Redis stores session state and cached catalog data.
Primary parts requests take time from managers
A customer rarely sends a perfect part number. They may describe a part in their own words, send a VIN, upload a photo or ask for a cheaper analog. A manager then has to identify the vehicle, check access, find OEM numbers, review compatibility and prepare the next step.
Free-form request
The customer can send text, VIN, photo or incomplete vehicle data instead of a clean catalog query.
Clarifying questions
The agent asks for missing details such as front/rear axle, side, model, year or engine.
Access checks
Before catalog work, the scenario can check user status, balance, tariff and remaining catalog requests.
Manager handoff
If the case is complex, the manager receives the vehicle, request, OEM result and dialog context.
The agent follows a parts-selection sequence
CARMA AI is designed as a process, not a free-form answering bot. Each step narrows the request and makes the next tool call more reliable.
1. User and access
The scenario checks authentication, verification status and catalog request limits before spending external catalog calls.
2. Vehicle identification
VIN, photo OCR or make/model/year/engine parameters are used to identify the right vehicle modification.
3. Part clarification
The agent interprets plain language, handles slang and asks up to several clarifying questions when needed.
4. OEM result
The backend uses catalog endpoints to find OEM numbers, applicability and links to diagrams or groups.
5. Cache and logs
Repeated data can be stored for 24-30 days, reducing repeated API calls and making support easier.
6. Answer or handoff
The user receives a structured answer, or the manager receives a prepared request with context.
A typical dialog
The public case does not expose private catalog data, so the scenario below shows the logic rather than a production transcript.
Customer: “VIN: WAUZZZ4M5HD000000. I need front brake discs.”
Agent: identifies the vehicle, checks whether the user has catalog access and asks whether the request is for front axle, rear axle or a full kit if needed.
Backend: passes the request through vehicle lookup, part clarification, OEM search and cached catalog data where available.
Result: the customer receives applicable OEM numbers and the manager receives the vehicle, request, selected modification, found part data and dialog context.
What AlphaDog worked on
This case should be read precisely: we are not claiming that AlphaDog built the whole CARMA business platform. The focus of the case is the AI-agent layer and the workflow around primary parts requests.
Conversation design
We designed how the agent starts the dialog, collects missing vehicle data, asks clarifying questions and avoids unsupported answers.
Scenario logic
We mapped VIN-flow, AUTO-flow, image/OCR input, free-text requests, catalog access checks and manager handoff.
API orchestration
The workflow connects the chat layer with backend services and catalog endpoints for vehicle, OEM and user access data.
Reliability boundaries
The agent follows a process with defined states and fallbacks, so complex or uncertain requests can be moved to a human specialist.
Workflow logic plus AI clarification
The technical stack is practical: n8n for scenario orchestration, Django for backend services, Redis for state and cache, and external catalog APIs for vehicle and part data.
n8n workflow layer
Webhook triggers, scenario branches, calls to backend services and controlled transitions between user flows.
Django backend
Bot response handling, API endpoints, token work, logging and service code that supports the AI flow.
Redis and Postgres
Session state, cached catalog data, logs and operational records for support and debugging.
Catalog API integration
Vehicle lookup by VIN, parameter-based vehicle lists, OEM search, catalog access checks and user profile data.
The value is in controlled tool use
A regular FAQ bot can answer from a knowledge base. CARMA AI has to interact with account state, vehicle data, catalog availability, OEM search and dialog memory. That requires workflow control and backend logic.
Regular FAQ bot
Good for static answers, company information and simple support questions. It usually cannot reliably operate catalog states or user access rules.
Workflow AI agent
Combines AI clarification with deterministic steps: access check, vehicle identification, part search, caching, logging and handoff.
Human specialist
Still needed for complex edge cases, final commercial decisions and unusual requests. The agent prepares context before the specialist joins.
AI handles the first layer before the human specialist
CARMA received an AI layer for primary parts consultation. It helps customers formulate the request, moves through catalog logic and gives the team a more structured handoff instead of a raw message.
VIN or photo input
The agent can start from a VIN string, uploaded photo or car details.
OEM and compatibility context
The result is connected to the identified vehicle, part request and catalog path.
Less manual qualification
Managers join later in the process, when the request already contains useful context.
Chat or voice potential
The same logic can be adapted for website chat, messenger flow or voice intake when needed.
Important notes
These details make the case clearer for companies considering their own AI agent.
Did AlphaDog build the entire CARMA platform?
Can the same logic work for voice?
What makes this harder than a simple chatbot?
Can this be adapted to other industries?
Tell us what you want to build
Send a short message. We will review your task and suggest the right website, CMS, SEO, AI agent or automation path.