Building a Chat to Buy Flow for Complex Products in HoverBot

Most chat widgets on ecommerce sites were built for simple customer support. They answer short questions, surface a help article, maybe hand the user to a human. They are not built for a lab manager equipping a new PCR room or a mechanic trying to find the exact brake parts for a specific VIN and driving profile.

HoverBot's new chat to buy flow targets those harder cases. It is designed for marketplaces that sell complex, high stakes products such as life science equipment or automotive parts, where a wrong choice is costly and the buying journey looks more like a consultation than a quick search.

Why chat to buy is different for complex catalogs

In a basic ecommerce scenario, a user types a keyword, lands on a category page, scrolls and clicks. The main job of a chatbot is to answer simple questions like "where is my order". That pattern breaks down as soon as products require technical context and tight compatibility rules.

Typical issues in complex catalogs include:

• Buyers start with scenarios, not SKUs. They say things like "We are setting up a teaching lab", "We need a centrifuge for this volume range", or "I need front pads and rotors for a 2019 Corolla".
• Specifications are dense. Voltage ranges, speed profiles, material compatibility, fitment tables and regulatory constraints are hard to reason about from a basic search result.
• Wrong choices are expensive. Mis ordered instruments, incompatible consumables or incorrect automotive parts waste time and money and damage trust.

The usual fallback is a long email thread or a phone call with a specialist. It can work, but it does not scale and it is hard to measure.

HoverBot's chat to buy flow treats this as a guided consultation. The buyer describes their scenario in natural language. The system interprets it using a domain tuned LLM, asks a small number of targeted follow up questions, then returns a compact set of safe, relevant options and clear next actions. The heavy thinking moves into the conversation instead of being pushed into a large filter sidebar.

What HoverBot is actually building

Conceptually, the new flow combines three core elements:

• Domain tuned LLMs, specialised for life science and automotive contexts and grounded in your catalog and documentation.
• Scenario aware matching, which translates free form questions into structured constraints and applies compatibility and business rules.
• A guided interface layer, using conversation chips and configurable result cards to keep the experience clear and on rails.

To the user, it feels like a single assistant embedded into the marketplace. Behind the scenes, the system is continuously switching between language understanding, structured filtering and interface decisions.

Entry into the flow, from chat bubble to buying session

The flow can start at several points on a marketplace. A floating assistant on category or search pages, a "Not sure if this fits" entry on product pages, or a "Talk to an expert" button on help pages can all lead into the same engine. Regardless of where the conversation begins, HoverBot opens a buying session and ties it to the user and, optionally, their current cart.

That session becomes the backbone of analytics. It allows the marketplace to see which paths users follow, which conversations lead to orders and where drop offs occur.

Understanding the scenario, not just the query

The first task for the LLM is to interpret what the user is trying to achieve. A life science user might write "We are setting up a basic PCR teaching lab for ten students" or "Can you suggest equipment for this assay with a budget of this size". An automotive buyer might ask for "front pads and rotors for a 2019 Toyota, mainly city driving, not too noisy".

From these messages, the system does three things:

• It classifies the intent, for example recommend equipment, check compatibility, find a replacement or troubleshoot.
• It extracts key parameters such as application, sample volume, throughput, voltage, vehicle make, model and year, driving style and budget.
• It detects missing critical details that must be clarified before a safe recommendation.

Instead of dumping a long form into the chat, HoverBot asks only for what matters. If the user wants brake parts but has not provided a VIN or model year, the assistant will request it explicitly. If a lab manager needs centrifuges but never mentions sample volume or throughput, the system will seek that information, but it will not demand irrelevant data.

Conversation chips, keeping the dialogue fast and structured

This is where conversation chips play a central role. Chips are short, clickable options that appear below the chat input. They might be used to specify lab size, usage scenario, budget range, driving profile or stock preferences.

They serve two purposes at the same time. For the buyer, they make the flow faster and easier, since it is simpler to tap "Mixed driving" than to type a paragraph. For the marketplace, they convert vague language into structured parameters that the catalog and business rules can understand. Each chip selection becomes clean input to the backend.

Chips are generated dynamically. They depend on what the system already knows from earlier messages, which gaps remain and what policies the merchant has configured. A life science marketplace may require application type for certain categories. An automotive marketplace may always ask for model year before any fitment sensitive recommendation. Those requirements translate into chip options that appear at the right moment in the conversation.

From conversation to catalog, how matching works

Once the scenario is clear enough, HoverBot begins matching it against the catalog. This is not just a search query with a few filters. The engine blends three layers:

• LLM reasoning, to interpret the scenario in domain terms and determine what types of products or bundles make sense.
• Structured filters and compatibility checks, using fitment tables, voltage ranges, rotor compatibility matrices, regulatory flags and stock information to enforce hard constraints.
• Business logic, such as preferred brands, inventory priorities, margin bands and rules about when to route requests into a quote flow instead of direct checkout.

In a life science example, a request for a PCR teaching setup may lead to a suggested bundle that includes thermocyclers, microcentrifuges, pipettes and starter consumables, filtered to appropriate voltage and price bands. In an automotive example, a request for pads and rotors for a specific model and driving style will be mapped to parts that are confirmed compatible, with compound choices aligned to noise and performance expectations.

Language drives the search, but hard rules decide what can and cannot be recommended.

Presenting options, cards, links and explanations

When HoverBot has a reasonable set of options, it must decide how to present them in the chat.

Product cards are the main workhorse. A card typically shows an image, product name, a short scenario focused description, two or three critical specs, price, availability and a clear action. That action could be "Add to cart", "View details", "Save configuration" or "Request a quote". Secondary actions might be exposed as chips, such as "Show alternatives" or "See compatible accessories".

In some cases, simple links work better. The assistant might point a user to a detailed comparison table, a technical data sheet, a fitting guide or a full product page when the decision demands more information than fits in a card.

There are also moments where plain text is the right tool. This includes explaining why certain products were selected, stating that compatibility cannot be confirmed without extra data or advising that a particular scenario should be escalated to a human specialist.

All of this is configurable. Marketplaces can choose which fields appear on cards for each category, how actions are wired and when to require a quote or human handover. The visual design can match the existing storefront so that cards feel like a native extension of the site, not a disconnected widget.

Why we rely on domain tuned LLMs

A generic large language model can write fluent sentences but does not automatically understand centrifuge rotor compatibility or brake fitment constraints. For chat to buy flows, that is not good enough.

HoverBot uses domain tuned models that have been trained and adapted on life science and automotive content, including product descriptions, manuals, fitment tables, protocols, frequently asked questions and historic question and answer data. On top of that, a retrieval layer feeds the model with relevant slices of the merchant's own catalog and documentation, so recommendations are grounded in real data rather than guesses.

Business and safety rules sit around the model. They define when a recommendation is allowed, when a human must be involved and when the assistant should stop and say that it does not know enough to answer safely. That combination of domain tuning, retrieval and rule enforcement is what lets the system handle prompts like "Can you suggest me equipment for this use case" without inventing SKUs or ignoring constraints.

From recommendation to order and to insight

Once a user acts on a recommendation, the system moves from advice to execution. When they add a card's product to the cart, save a bundle, open a full product page or request a quote, those events are recorded against the same buying session. The assistant sends a short confirmation in plain language and may propose the next logical step, such as checking accessories, downloading documentation or scheduling a call.

Because every step is captured as structured data, marketplaces gain visibility into how buyers actually make complex decisions. They can see which conversation openings convert best, which chips are most useful, where users stall and which product families frequently trigger handovers to humans. Over time, this drives improvements in both the language layer, such as prompts and tuning, and the interface layer, such as chips, card designs and flows.

What it means for marketplaces and merchants

For marketplaces selling complex equipment and parts, the chat to buy flow aims to shift product selection from unstructured, manual consultation into a scalable, measurable pattern directly on the storefront.

Buyers get faster and more confident decisions with fewer mis orders. Internal specialists can focus on genuinely difficult edge cases rather than answering the same "Which one should I pick" question all day. Product and growth teams gain concrete data on how technical buyers talk, what they care about and where they need help most.

HoverBot is rolling this flow out first with life science and automotive marketplaces and will extend it to other verticals as the tooling and patterns mature. If you operate a complex catalog and want to explore a chat driven buying journey, the HoverBot team can help design and test a scenario tailored to your products and workflows.