
Ronex Cars
Sales pipeline with lead scoring and fair auto-assignment, car inventory, reservations and finance, commissions and HR — one operating layer for the showroom floor, with Meta lead ads flowing in live.
Visit the live siteAI that reads your own documents and data, wired into the systems you run. Automated where it pays, a human on the big calls.
Vendors and consultants pitch AI like it’s a turn-key add-on. In reality, off-the-shelf AI bolted onto your tools pulls up the wrong customer’s contract, leaks data through the wrong connection, and stops working the moment the underlying model updates.
Real AI integration is plumbing: answering from YOUR data, checking against YOUR rules, asking a human when it is unsure, and watching the costs. Most projects skip the plumbing and ship the demo. Then it breaks in week three.
We define what success looks like in real numbers: minutes saved per task, tickets deflected, response time cut. If the math doesn’t justify the build, we say so before we start.
Your documents and data made searchable so the AI answers from YOUR truth, not from whatever it picked up in training. Made-up answers drop sharply.
Answers checked against your rules, defenses against people trying to trick it, and filtering of sensitive data. A human reviews anything it is unsure about. Plus monitoring and cost dashboards.
Deploy alongside your existing systems (CRM, support tools, internal portals). Model + provider can be swapped (OpenAI → Claude → open-source) without rewriting the integration.

Sales pipeline with lead scoring and fair auto-assignment, car inventory, reservations and finance, commissions and HR — one operating layer for the showroom floor, with Meta lead ads flowing in live.
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Leads, reservations, inventory, agents and money on one screen. In production since early 2025: 5,800+ leads managed (about 870 a month), 280+ reservations, 190+ units.
Visit the live siteWe were running everything across spreadsheets and WhatsApp groups. Felukaa built us one system in three weeks — now every lead, every reservation, every agent is in one place. Nothing slips through anymore.
OpenAI, Anthropic Claude, Google Gemini, and open-source models you can run on your own servers when data has to stay in-house. We pick the provider per use-case, not “whichever has the best brand recognition.” Many clients run multi-provider with fallback for cost + reliability.
Three layers: the AI answers from your own data (not from training), every answer is checked against your business rules, and anything it is unsure about goes to a person. We measure how often it gets things wrong before and after launch. It’s a tracked number, not a hope.
Depends on volume and provider. We build with cost monitoring + caching from Day 1, so you can see exactly which use-cases pay for themselves. Inference cost scales with usage: light use-cases run cheap; high-volume support deflection costs more but generally saves multiples of that in agent time.
Yes, with open-source models you can run on your own servers or private cloud. We’ve built document-reading AI that never touches an outside provider. A bit slower than the big closed models, but completely under your control.
Active concern, not a footnote. We layer input sanitization, structured output formats, content filtering, and rate limiting. Production AI surfaces are security-tested before launch. Public-facing chat is different from internal document-reading AI. We scope security to the actual threat model, not a generic checklist.
Tell us where AI would actually pay off in your operation. We’ll send back a scoped plan: what’s worth building, what isn’t, and a realistic ROI baseline.