Chatbot
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A chatbot is a software interface that accepts natural language input and returns a generated or retrieved response, using rule-based logic, a retrieval system, a large language model, or some combination of all three.
Why the word "chatbot" obscures what actually runs underneath
The term lumps together systems with radically different capabilities. A rule-based chatbot from 2015 follows decision trees and fails outside its scripted paths. An LLM-backed chatbot in 2024 generates novel text by predicting token sequences against a foundational model trained on billions of parameters. The distinction matters operationally: a rule-based system cannot interpret a question phrased in an unexpected way, while an LLM-backed system can, but it can also hallucinate a confident answer with no factual basis. In a proposal context, the failure mode shifts from "I don't understand" to "here is a plausible but fabricated project description," which is a harder error to catch.
Where chatbots actually appear in a pursuit workflow
Most AEC marketing teams encounter chatbots through one of three entry points: a general-purpose interface like ChatGPT used ad hoc by a seller-doer drafting a cover letter, a copilot embedded inside a CRM or proposal platform, or an internally deployed tool pointed at a firm's own content library. Each carries a different trust threshold. The ad hoc tool has no knowledge of your firm's past performance or client history; it invents plausible-sounding project details unless you supply them through careful contextual prompting. A copilot connected to structured firm data can surface real project data, but only as reliably as the underlying data is tagged, current, and scoped correctly. The gap between what a chatbot interface implies it knows and what it actually has access to is where proposal errors originate.
The retrieval question every team should ask before deploying one
Chatbot quality in a pursuit context is almost entirely a retrieval problem, not a generation problem. The generation layer of any modern LLM is capable of producing fluent, well-structured prose; the constraint is whether the system has access to accurate, firm-specific context before it generates. Retrieval-augmented generation addresses this by pulling verified content from a vector database before the model writes anything, which replaces fabrication with grounded recall. Teams that skip this architecture and route queries directly to a base LLM will get polished language around hallucinated project scopes, wrong client names, and fee figures that bear no resemblance to actual history. Kantiv connects the chatbot interface to verified pursuit records, project data, and personnel history so that what gets generated reflects what the firm has actually done.
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