The automotive data layer for AI agents
Levam provides verified, structured OEM and automotive data through an API designed for AI assistants, agents and enterprise applications.
Connect vehicles, configurations, components, diagrams, parts and compatibility into a single machine-readable context your AI can understand and use.
Built for marketplaces, automotive platforms, manufacturers and enterprise AI teams.
AI cannot reliably answer automotive questions without automotive context
General-purpose models understand language, but they do not know the configuration of a specific vehicle, its factory relationships, part applicability, replacement chains or the structure of OEM catalogs. Without a data layer, a model guesses from an incomplete VIN, mixes generations and markets, confuses engines and transmissions, suggests incompatible parts, relies on outdated sources — and returns a confident answer that is wrong.
Levam replaces assumptions with verified vehicle context.
A context engine built for automotive intelligence
Levam is not a flat database of part numbers. It connects vehicle identification, market and region, model and modification, engine, transmission, trim, catalog groups and assemblies, OEM diagrams, positions, OEM numbers, descriptions, installation quantities, applicability, supersessions and related aftermarket objects into one coherent structure.
Every object is delivered to the model together with its place in the vehicle's structure — so the answer can be traced back to the exact configuration it applies to.
Designed for models, agents and machine reasoning
Unlike a catalog API built for human browsing, Levam returns normalized entities with stable identifiers, explicit relationships, component hierarchy, configuration context and applicability models — structured JSON ready for tool calling, RAG and agent workflows, with localized names and references back to the source catalog.
Agent-ready context
Responses are structured so an agent can reason over them directly: entities, relations and constraints instead of prose.
Structured relationships
Vehicles, systems, assemblies, parts and replacements are linked explicitly — no guessing about what belongs to what.
Deterministic tools
Stable identifiers and predictable methods make Levam safe to expose as function-calling tools in agent frameworks.
Explainable answers
Every part comes with its position in the OEM structure, so answers can cite the configuration they are based on.
Multilingual by design
Normalized, localized component and part names let one integration serve users across markets and languages.
Built for scale
A REST API designed for production traffic of marketplaces, platforms and enterprise assistants.
From user question to verified answer
Scenario: "Find the correct front brake pads for this VIN and explain the difference between available replacements."
Identify the vehicle
The VIN resolves to an exact model, market, engine, transmission and production period.
Resolve the relevant system
The agent navigates from the vehicle to the braking system and the front brake assembly.
Retrieve verified parts
Levam returns the OEM positions, numbers, quantities and current supersessions for this configuration.
Provide model-ready context
The structured response is passed to the LLM as grounded context for the final answer.
Keep the answer auditable
Each statement can be traced back to a catalog position — not to a model's assumption.
{
"vehicle": {
"vin": "WBA…",
"model": "3 Series (G20)",
"engine": "B48B20",
"market": "EUR"
},
"context": {
"system": "Brakes",
"assembly": "Front brake pads"
},
"parts": [{
"oem_number": "34 11 6 850 886",
"name": "Repair kit, brake pads",
"quantity": 1,
"applicability": "exact match",
"superseded_by": "34 10 6 888 777"
}]
}
Build automotive AI that can do real work
AI parts assistant
Help customers find the right part for their exact vehicle — fewer wrong orders and returns.
Workshop copilot
Give mechanics instant, configuration-aware answers — fewer manual catalog lookups.
Marketplace agent
Ground search and recommendations in verified applicability — higher search quality and conversion.
Customer support agent
Resolve compatibility questions automatically — faster responses and better customer experience.
Claims and inspection automation
Verify components and replacements against factory data — fewer manual checks in the loop.
Catalog and data enrichment
Normalize and link your product data to OEM structure — faster launch of new AI features.
One automotive data layer. Multiple sources of truth connected.
Levam is a single data model — not separate "OEM" and "aftermarket" products. Each category below is part of the same connected context.
Vehicle identity
VIN and frame decoding, model and modification, market, production period, engine, transmission and vehicle parameters.
OEM structure
Catalog groups, assemblies, diagrams, callout positions, OEM numbers, installation quantities, supersessions and applicability.
Parts intelligence
Normalized part names, cross-references, alternatives, related aftermarket products and manufacturers.
Semantic automotive layer
Universal component classification, multilingual normalization and machine-readable relationships between all of the above.
Why enterprise AI teams choose Levam
Comprehensive coverage
One of the most comprehensive automotive datasets available for AI products, spanning dozens of brands and markets.
Verified automotive context
Structured data derived from OEM catalog structure — designed to reduce ambiguity and unsupported AI answers.
Faster time to market
Skip years of sourcing, cleaning and linking data from dozens of providers — integrate one API instead.
Lower AI error rate
Grounding answers in verified vehicle context measurably reduces hallucinated part numbers and wrong fitment claims.
Enterprise integration
Predictable REST API, stable identifiers and support from a team that understands automotive data.
Continuous improvement
Catalogs, supersessions and mappings are continuously updated as manufacturers revise their data.
More coverage means fewer dead ends for your agent
Built to fit modern AI stacks
User → AI application → LLM / agent framework → Levam tools → verified automotive context → final response.
Integrate through REST with structured JSON: function calling, tool-based agents, RAG pipelines, workflow automation, internal copilots, customer-facing assistants and data enrichment jobs.
Automotive data infrastructure for enterprise scale
Custom volumes, dedicated infrastructure options, SLA, priority support, integration consulting, custom data workflows, account management, custom commercial terms, licensing and regional or brand-specific packages.
Discuss your use caseSimple plans, built around your volume
Billing is based on vehicle identifications — the moment a vehicle is resolved to its exact configuration. Everything after that (navigating systems, parts, applicability) is included.
Professional API
- 25,000 vehicle identifications per month included
- Full OEM structure and parts data
- Aftermarket cross-references included
- Structured JSON for agents and RAG
- Production access and support
Enterprise
- Custom identification volume
- Dedicated infrastructure options
- SLA and priority support
- Custom integrations and licensing
- Account management
Your AI is only as good as the context behind it
Build automotive agents that identify vehicles, understand components, verify parts and return answers your users can trust.
Tell us what you are building, your markets, expected volume and required vehicle coverage.