Not if the system prompt is configured correctly. SleekAI reads from your model card and pricing pages, and the prompt should explicitly forbid invented metrics. The bot will say 'I do not have the exact number for that, but the model card shows 72.3 MMLU and full evals are at /benchmarks' rather than guess. That graceful-loss behavior is the most important credibility lever for an AI startup, because a single fabricated metric in a sales conversation undoes years of careful eval work and gets shared on Hacker News within hours.

Yes. SleekAI is multibot, so the 7B model page can run a 7B-aware bot, the 70B page can run a 70B-aware bot, and the Vision-model page can run a Vision-specific bot. Display conditions scope each bot to the right URL pattern, post type, or UTM source. This matters because the technical questions are model-specific (a Vision model gets asked about image resolution limits, a long-context model gets asked about needle-in-a-haystack scores), and a bot scoped to the right model gives denser, more credible answers.

The marketing-site bot lives in WordPress. The developer portal can be Mintlify, Stoplight, Readme, or custom. The common pattern is two surfaces: a marketing-site bot for top-of-funnel evaluation and self-qualification, and the developer-portal native search for in-product documentation. SleekAI can also ingest exported docs into the OpenAI Files vector store if you want a single bot covering both, but most AI startups split them for cleaner attribution and faster latency on the docs side.

Defensively, like any production chatbot should. The system prompt is hardened against role-override and instruction-leak attempts, the guideline filter constrains off-topic responses, and conversation logs flag suspicious patterns. Prospects probing prompt-injection robustness is actually a positive signal because it tells you they are evaluating you on production readiness. The bot should hold its boundaries calmly and route the prospect to the security page that covers your runtime defenses if the questions deepen.

Yours. SleekAI is BYO API key, so you bring your own OpenAI, Anthropic, Google, or OpenRouter key and the model bills you directly. Most AI startups use their own model on their own marketing site, which makes the chatbot a live demo of the product itself. SleekAI supports any OpenAI-compatible endpoint via OpenRouter or direct config, so pointing it at your own inference endpoint is straightforward. Per-message markups on a third-party chat tool would be embarrassing for an AI startup; BYO key avoids that entirely.

Yes. The system prompt can include rules like 'researchers asking about reproducibility, training data, or collaboration route to research@, enterprise customers route to enterprise@, self-serve developers route to docs'. Research-partnership intake usually wants different fields than a sales intake (institutional affiliation, intended publication venue, dataset sharing posture), and the bot can capture them conversationally rather than through a long form. Hand-off is to your existing form or inbox, so the data architecture stays clean.

Only if you point it at a weaker model than your own. SleekAI lets you pick the model per bot, and most AI startups use the strongest reasonable model on their marketing site precisely because the bot doubles as a live demo. Choosing a cheap model for marketing-site Q&A creates a credibility gap that prospects notice in the first session. The right pattern is to use your own production model for the bot when feasible, or the strongest commodity model otherwise, and to design the bot for high-quality short replies rather than long generations.

Conversations are stored on your WordPress install. You control retention and exports, and you can pipe interesting conversations to a Slack channel via webhook for the GTM team. For AI startups serving enterprise buyers, the fact that conversations never touch a third-party SaaS, except the LLM API you chose, is the deciding factor over hosted alternatives in security review. You can also configure aggressive retention windows (24 hours, 7 days) for marketing-site conversations to match the data-minimization expectations your enterprise prospects bring to the evaluation.