No. The system prompt declines FMCSA-regulation interpretation, carrier-compliance opinions, fault assessments, and case-value estimates. Those are attorney calls that depend on the police report, the carrier's records, and the FMCSA database. The bot captures the structural intake and routes to a consultation. Audit the refusal quarterly with adversarial prompts ("is the carrier liable", "did they violate hours-of-service rules") to confirm the guardrail holds. Compliance opinions are the most common ethics issue in trucking-PI chatbots.

The bot recognises trucking-specific time pressure: ELD data retention can be as short as six months under FMCSA rules, dashcam footage often rolls on a 30-day or shorter cycle, and driver logs require fast preservation. When the visitor indicates a recent crash the bot escalates the booking posture and surfaces the soonest available case-screening slot. The system prompt does not promise specific preservation outcomes; that's an attorney call. It captures the urgency signal and routes accordingly.

Primarily by state and county because jurisdiction drives venue and applicable law. For firms with deep relationships against specific carriers or insurers, the bot can also surface that context to the attorney through the intake fields. Multibot lets large firms split intake by case type (over-the-road, intrastate, HazMat) if the practice supports that level of specialisation. Most firms run a single trucking bot with state-level routing and let the attorney decide further specialisation on the call.

Yes. The bot captures the structural facts (number of vehicles, position of the truck, sequence of impacts as the visitor recalls them) and routes to the case-screening attorney. Multi-vehicle trucking crashes often involve multiple potential defendants (carrier, driver, broker, shipper, maintenance contractor) and the conflicts intake captures known parties for the conflicts team. The attorney handles the actual liability analysis; the bot captures the inputs.

Yes. Many trucking-PI practices serve Spanish-speaking families, particularly along major freight corridors. SleekAI replies in the language the visitor writes in. The booked consultation flags preferred language so the attorney prepares accordingly. Legal-advice refusal language remains intact across translation because the prompt rules apply in any language. For markets with Vietnamese, Korean, or Punjabi speakers (also common in trucking communities) the same multilingual capability extends naturally.

BYO API key with the standard terms means data is not used for training by default. Conversation logs live in your WordPress database under your control. For trucking practices the typical posture is medium-length retention with structured handoff to the case-management system once an engagement starts. Document the bot in your privacy notice and align retention with engagement-letter terms. For high-value trucking matters that may move into litigation quickly, retention should align with discovery-readiness requirements.

The bot captures the structural inputs your case-screening team uses: injury severity, treatment status, time since crash, carrier visibility, insurance posture. It does not make the viability decision; that is for the screening attorney. The intake gives the attorney the structured fields before the consultation so low-viability matters get routed to a decline letter rather than a 30-minute call, and high-value matters get the senior-partner attention they need on a fast cadence.

Configure the bot to recognise HazMat and oversize indicators in the visitor's description (tanker, placards, oversize markings, road-closure context) and route to attorneys with experience in those subsets. HazMat and oversize matters involve different regulatory layers (DOT-PHMSA for HazMat, state-permit requirements for oversize) and the intake fields adjust accordingly. The bot does not interpret the regulations; it captures the facts and flags the matter for the attorney with the relevant background.