SOLUTION · AUTOMOTIVE
Automotive
An AI platform for aftersales, dealer, and connected-vehicle services. Traceable diagnostic guidance, parts recommendations, and predictive maintenance—integrated with your OEM and dealer systems, with no retraining for technicians.
Turn aftersales tribal knowledge into a capability every technician can use
The real bottleneck in automotive aftersales isn't a lack of data—it's data scattered across systems and locked in the heads of your most experienced technicians. Repair manuals live in PDFs, TSBs sit on the OEM portal, work-order history is in the DMS, and complaint records are in yet another service system. When a car rolls in, the technician has to jump between all of them and rely on instinct. When a veteran retires, that instinct walks out the door—new hires ramp slowly, and first-time-fix rates drop.
Tenten AI consolidates those fragmented sources into a single diagnostic copilot built for the technician. Using a RAG architecture, we connect repair manuals, TSBs, work-order history, and warranty policy so that every recommendation carries a traceable citation—the technician can see why a given part is suggested, not just a black-box answer. That traceability is what makes diagnostic accountability real: when a recommendation is wrong, you can trace exactly which document and which work order it was based on.
We don't ask you to rip and replace. The system integrates via API with your OEM DMS, dealer management system, and contact center, so technicians work inside the interface they already know. Our FDE engineers embed on-site at your service center, start from one real diagnostic scenario, and have technicians validating on actual vehicles within weeks—not stuck in a demo.
Capabilities
01
Traceable diagnostic copilot
A technician-facing diagnostic assistant wired to repair manuals, TSBs, and work-order history, where every recommendation carries a source citation—so diagnostic decisions stay traceable and auditable.
02
Parts recommendation and fitment checks
Recommends the correct part by model, year, and fault code, auto-matching VIN and applicability to cut wrong orders, returns, and repeat visits.
03
Connected-vehicle predictive maintenance
Uses connected-vehicle telemetry and fault-code data to predict likely failures and service windows, reaching out before a customer breaks down—turning reactive repair into proactive service.
04
Drop-in OEM integration
Integrates via API with your OEM DMS, dealer management system, and contact center, reusing existing permissions and data governance—no data migration, no system swap.
05
Dealer service support
Assists service advisors with talk tracks and quoting, translating diagnostic findings into plain-language explanations and transparent estimates that build trust and close jobs.
06
Technicians keep their workflow
Embedded in the interface and process technicians already use, in three steps or fewer—no retraining, low barrier to adoption.
Use cases
Fault-code diagnostic copilot
A technician enters a fault code and symptoms; the system cross-references manuals, TSBs, and similar past work orders to return ranked likely causes and repair steps, each backed by a source.
Parts recommendation with fitment gating
Pulls the correct part number and compatible alternatives by VIN and model, catching non-applicable parts before the order is written to reduce returns and parts-wait delays.
Proactive connected-vehicle recall outreach
Telemetry from fleet or warranty vehicles triggers an anomaly alert; the system judges the service window and generates an outreach recommendation so the center contacts the owner before failure.
Service-advisor quoting support
Turns the technician's diagnosis into a customer-facing explanation and itemized estimate, flagging warranty coverage so advisors can give a clear, credible quote on the spot.
First-time-fix improvement
For tough, repeat-visit cases, it surfaces cross-location work-order history and proven fixes so newer technicians can draw on veteran experience and fix it right the first time.
Delivery cadence
WEEK 1-2
Scenario selection and system audit
Work with your service-center team to pick one high-value diagnostic scenario and map your data sources and integration points—DMS, manuals, TSBs.
WEEK 3-6
Copilot pilot goes live
FDEs embed to build the diagnostic copilot, wire it to existing systems, and have technicians validate on real work orders—not a demo.
WEEK 7-10
Accuracy tuning and accountability governance
Tune recommendation quality from technician feedback and establish source traceability and human-in-the-loop checks so diagnostic accountability is auditable.
WEEK 11+
Roll out across centers
Replicate the proven pattern across more service centers and dealer locations, layering in advanced use cases like predictive maintenance.
Traceable
Cited on every recommendation
OEM-native
No rip-and-replace
Weeks
Diagnostic copilot live
FAQ
If a diagnostic recommendation is wrong, who's accountable—and how is it traced?
Every recommendation carries a source citation, so you can trace exactly which repair manual, TSB, or past work order it was based on. We position the copilot as an assist, not a replacement—the technician still confirms the final diagnosis, with the system providing an auditable basis for the decision. That keeps accountability clear and gives you a full decision trail when warranty disputes arise.
We already run an OEM DMS and dealer system—do we have to replace them?
No. The system integrates via API with your existing DMS, dealer management system, and contact center, reusing your current permissions and data governance. We don't migrate your data or ask you to swap systems—technicians use it inside the interface they already know.
Will technicians actually adopt it? Do we need to retrain staff?
We embed the copilot into the workflow technicians already use, in three steps or fewer, with no separate system to learn. During rollout, our FDE engineers embed on-site and tune the recommendations alongside frontline technicians—so adoption happens because it's genuinely useful, not because it's mandated.
What data does connected-vehicle predictive maintenance need, and how accurate is it?
We combine connected-vehicle telemetry and fault-code data with your repair history to predict failures and service windows. Accuracy depends on data quality and coverage, so we start with a single model or fleet pilot, validate against real recall outcomes, and scale from there—rather than promising unrealistic numbers up front.
How is data security and privacy handled? Could owner or warranty data leak?
It can be deployed inside your VPC so data never leaves your environment, reusing your existing access controls and audit mechanisms. We design data pipelines to standards like SOC 2 and GDPR, and you define the scope of how connected-vehicle and customer data is processed.
How fast do we see results, and is a big build required first?
No large build up front. We start from one real diagnostic scenario and typically have technicians validating the copilot on actual vehicles within weeks. We prove measurable results—like first-time-fix or return rates—at a single service center first, then replicate to other locations.
A new era of
AI-native products
Ship your first AI use case in weeks, not quarters.