AI layer · agents in the workflows
Artificial intelligence that works inside your tools.
AI that moves customers to purchase: it prioritises the right customers, activates stock and guides the team — while your data stays under your control. Nebula AI moves work from static data views to AI-assisted prioritisation: background assistants turn CRM, inventory and service data into proactive sales actions, and decision support is embedded directly into the workflow tools. Agents can be guided — with human oversight at every step — or find the path to a solution on their own.
4
prototypes in development
Who it is for
Importers, dealers and finance institutions on the VEGA and GAIA platforms that want more sales, after-sales and better team utilisation from existing data — without switching tools.
Integrations
VEGA · GAIA · configurators and price lists · call centre · DMS and appointment planner (MPO) · external and internal LLMs · RAG and knowledge graphs · human oversight
Core capabilities
Golden Moment Proactive Sales (VEGA AI) — the right customer at the right time, with an AI-ranked opportunity queue (Hot/Warm/Cold) and a recommended next step.
Stock Management (VEGA AI) — natural-language inventory search, best-fit matching, aging-stock and pricing signals.
Virtual Assistant + interactive learning (VEGA AI) — one agent for sourced answers, guided help, quizzes for easy learning, news and built-in user-support requests — without leaving the CRM.
New Vehicle Sales Targeting (GAIA AI) — ranking customers by purchase probability with recommended timing and offer.
Spare Parts Inventory Activation (GAIA AI) — dead parts stock becomes a targeted after-sales campaign. Planned.
New Vehicle Fleet Sales Agent (GAIA AI) — an agent supporting new-vehicle sales to fleets and key accounts. Planned.
AI Service Booking Assistant — service appointment booking with smart customer identification and slot reservation. Planned.
External or internal LLMs — you choose where the models run: a public provider or your own infrastructure, driven by data sovereignty and security.
Regulated flows and autonomous agents — from strictly governed processes to agents that find their own path to solving a given task, always with human oversight.
Knowledge graphs — advanced knowledge bases built on knowledge graphs for verifiable, grounded answers from data, documents and processes.
Status · August 2026
Four of the seven use cases already run as prototypes in development.
Four of the seven use cases in the Nebula AI portfolio already run as working prototypes in active development, exercised on real automotive data — an assistant that answers from the official manuals with citations and screenshots, natural-language search over new-vehicle stock, and two multi-step pipelines that turn dealer data into ranked, GDPR-gated worklists. Three use cases are planned.
VEGA Asistent — one in-app agent: grounded answers from the manuals with citations and images, quizzes for easy learning, guided troubleshooting, dealer-hub news and built-in requests.
Natural-language stock search — a sentence as a query over the entire current new-vehicle stock.
Golden Moment proactive sales (GM) — customer-first: internal signals → customer ranking (Hot / Warm / Cold) → matching against own stock → offer brief.
New-vehicle sales targeting (NV) — vehicle-first: vehicle ranking (aging, margin, demand) → selecting the right customers → channel by consent.
Planned: an agent for new-vehicle sales to fleets and key accounts, an AI service-booking assistant, and spare-parts inventory activation (AFS).
Prototype in development · VEGA AI
One assistant inside the CRM: answers, learning and support.
VEGA Asistent is a single agent embedded in the corner of the VEGA screen, accompanying the user through everyday work. A question asked in everyday words — »zakaj mi ne pusti shranit ponudbe?« (»why won't it let me save the offer?«) — is first rewritten into the manual's register and answered exclusively from the official VEGA and mVEGA manuals: every sentence cites the page it came from, and the answer is completed by the original screenshots of the cited pages. When the manuals don't cover something, it says so — and takes the user forward another way.
Advanced grounded answers with images — from the full library of official manuals in Slovenian and Croatian.
Guided troubleshooting: steps from the manuals that walk the user out of a concrete blocker.
Interactive learning: short quizzes and tips from the manuals, tailored to the user's role — the agent infers the role in the background from how the application is used; nobody fills in a profile.
Staying informed: dealer-hub news, ranked by relevance to the user's work.
Built-in requests: when an answer is not enough, the agent prepares a request for the Nebula team straight from the conversation.
Embedded with a single script tag; the widget is fully isolated and does not interfere with the application.
Prototype in development · VEGA AI
»A family SUV under €30,000 with an automatic« is a valid query.
Salespeople search the entire current stock of Renault, Dacia and Alpine new vehicles by describing what the customer needs, in Slovenian, Croatian or English. An LLM compiles the sentence into a precise, auditable filter (body type, powertrain, price, equipment) over the real factory equipment of every version, taken directly from the vehicles system. A second, semantic channel catches what no equipment code can express — »avto za kampiranje« (»a car for camping«) finds the right trims through roof rails and washable interiors. The two channels blend, and a concierge model orders the final shortlist and explains each pick in the user's language.
Vehicle equipment is never estimated: the filter reads the actual factory equipment lists of each version — for practically the entire stock.
The query compiler picks only from a closed equipment vocabulary — it never guesses.
When a filter matches nothing, the tool clearly says so — it cannot invent stock.
Prototypes in development · GAIA AI and VEGA AI
Pipelines for stock vehicles and people.
Two back-office use cases run as multi-step pipelines over the dealer's own data — every step predefined, no external feeds, no black-box scoring. Each pipeline is packaged as an MCP server, so any approved AI agent can drive it, and each ends at a human: the manager confirms before anything is sent.
Golden Moment (GM): finds the customers ready for their next vehicle — Hot / Warm / Cold on internal signals — and matches each hot customer against the dealer's own stock. When nothing fits, the salesperson gets a configuration brief for a new-build offer instead: call this customer, offer this car.
New-vehicle push (NV): vehicle-first — ranks the new cars that most need to move (aging, margin, demand), then finds the right customers for each; a hard ownership-tenure rule and routing to call centre, e-mail or SMS by score and consent.
Shared guardrails: a GDPR consent gate in the data layer, a shared suppression store (no customer is contacted twice across pipelines), every threshold in a business-owned playbook file rather than in code, and outcome logging that feeds the next run.
Planned: a third pipeline — spare-parts inventory activation (AFS), from stock aging and car-parc fitment to routing into campaign, transfer or write-off.
Planned
Three use cases in the plan: fleets, service and parts.
The portfolio continues with three use cases at the planning stage — under the same rules as the prototypes: grounded answers, GDPR guardrails and a person who decides.
An agent for new-vehicle sales to fleets (key accounts) — from signals across the network to an offer brief for the business customer; built on the Key Accounts view in GAIA.
An AI service-booking assistant — the customer identifies themselves through several parameters (for example part of the chassis number plus the model, the registration number or an after-sales invoice number), and where the assistant has access to a scheduling planner — through the DMS or the MPO tool by our partner CPL, which we are jointly rolling out at Summit Motors Ljubljana — it also books the appointment.
Spare-parts inventory activation (AFS, GAIA AI) — a multi-step pipeline: stock aging → fitment against the car parc → scoring → routing to campaign, transfer or write-off.
Reference
Prototypes in development for GA
7 use cases in portfolio · 4 prototypes in development · 3 planned · prototype trials August 2026
Four use cases in development already run as working prototypes, exercised on real data: VEGA Asistent with grounded answers from the official manuals, natural-language search over new-vehicle stock, and the pipelines for Golden Moment sales and new-vehicle push. The starting point was an earlier prototype of AI in car configurators and price-list management tools (GA Ford); planned next are an agent for new-vehicle sales to fleets, an AI service-booking assistant and spare-parts inventory activation. None of this is a production deployment yet.
Insights
From our Insights.
- The hybrid organisation: people and agents on one team September 2026
- AI that works where the work is August 2026
- Modernisation without disruption July 2026
FAQ