An AI-driven microservice that powers a quoting widget for B2B content publishers. It uses AI to parse and normalize unstructured user inputs (e.g., comma-separated list of desired publications, shorthand, misspellings) against a master catalog and then applies pricing rules and discounts to generate an instant, structured quote in JSON format.
Content resellers and B2B publishers that sell corporate subscriptions receive requests written in every possible way: abbreviations, misspellings, informal titles and seat counts buried in a sentence. A corporate librarian might type 'FT, the Economist, Aus Fin Review x 50 seats' and expect a price. Sales staff then spend time decoding the request, matching each title to the right product in the catalogue, looking up list prices and applying discount and fee rules by hand, which slows the response and invites inconsistent margins.
An AI quoting engine for publishers splits the job in two. An LLM does what it is good at: reading messy text and matching each item to an official product in the master catalogue, with a confidence score. Conventional code then does what it is good at: applying list prices, volume discounts, platform fees and tax exactly as your rules say. The output is a structured quote the website widget can display instantly, and uncertain matches are shown to the buyer or sent to sales rather than guessed.
A web widget collects free-text requests and sends them to a quoting microservice written in Python or JavaScript through a RESTful API.
An LLM parses the text into line items and seat counts, then proposes matches from the master catalogue, returning structured output with a confidence level for each item.
Low-confidence or unmatched items are returned to the user with suggested alternatives, such as 'Did you mean this title?', before any price is calculated.
The pricing module applies list prices, seat-based tiers, negotiated discounts, platform fees and GST using deterministic rules held in configuration, not in the prompt.
A JSON quote with line items, totals and validity date is returned to the widget and logged, with an option to convert to an order or send to sales.
A corporate librarian enters 'WSJ, NY Times, The Econ, 50 seats' into a web widget. The AI normalizes the inputs to their official product names, retrieves the list prices, applies a 20% discount plus a platform fee, and instantly returns a detailed quote, ensuring speed and margin control.
Python/JavaScript, LLM for input parsing (GPT), Web Widget, RESTful API
Quotes shown to buyers should state whether GST is included and present a clear total, consistent with Australian Consumer Law expectations on price representations, even in B2B settings where misleading conduct provisions still apply. If you resell third-party publications, check your distribution agreements permit the discounts and bundles the engine can generate. Quote requests may include buyer names and organisation details, so store logs securely and cover them in your privacy policy. Keep issued quotes and their pricing basis with your business records to support ATO record-keeping requirements when quotes become invoices.
This solution requires careful evaluation of cost-benefit ratio. Consider implementation only after thorough analysis and risk assessment.
LLMs are good at interpreting language but are not reliable calculators, and pricing must be exact and repeatable. Keeping pricing in deterministic code means the same request always produces the same quote, discounts can be audited, and changing a rate is a configuration update rather than a prompt rewrite.
The engine flags it as unmatched rather than inventing a product. The buyer sees the item highlighted with any close suggestions, and the request can be routed to a salesperson. Those gaps are useful signals for adding aliases or new products to the catalogue.
The scope is narrow, which keeps the timeline short, but getting it right requires careful work: structured outputs, confidence handling, precise pricing logic and thorough testing against real, messy requests. Errors directly affect margin, so the testing effort is higher than the size of the service suggests.
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