Selecting a vendor usually means one person reading six 40-page PDFs, building a spreadsheet by hand, and hoping they didn't miss the one clause that matters. By the time the comparison is done, half the team has stopped trusting it — because no one can see where each number came from.
Sharper takes a different approach. Point it at your documents and it becomes an answer engine: ask a question in plain English, get a structured answer back, click any claim to jump to the exact passage it came from, and explore the related concepts surfaced beside it.
To show it end to end, we ran a real example — choosing a commercial-insurance vendor from six competing proposals.
What is knowledge base question answering? KB Q&A lets you ask natural-language questions across a set of documents and get a direct, cited answer instead of a list of links. It reads and reasons over the full text, then shows the source passage behind every claim so you can verify it.
The setup: six proposals, four real questions
We built a knowledge base from six commercial-insurance proposals — Sentinel, Vanguard, Beacon, Meridian, Crestline, and Apex — the kind of dense, table-heavy PDFs a procurement team normally reconciles by hand. Then we put the knowledge base through four questions a buyer actually asks:
- "Rank the vendors by the maximum liability protection available, including umbrella layers."
- "We want to capture every early-payment discount. Which vendor rewards paying fast, and how much?"
- "We're a 200-person SaaS company needing cyber coverage, NetSuite e-invoicing, and a fast quote. Who fits?"
- "Which vendors support structured e-invoicing/ERP integration, and which don't?"
Here's how Sharper goes from a folder of PDFs to a cited answer for each — and how you'd do the same with your own documents.
Follow along. Download the six sample proposals used in this walkthrough, then build the knowledge base and ask the questions below yourself.
Step 1 — Upload the files to build documents
Drop the six PDFs into a knowledge base. Sharper parses each one — tables, endorsements, fine print and all — into clean, searchable documents, and keeps the original alongside the extracted text, so you're always one click from the source.
The six vendor proposals as parsed documents, with the original PDF one click away.
Step 2 — Sharper builds the concepts for you
You didn't tag anything. Sharper automatically extracts the concepts that run across the whole set — "Umbrella & Excess Liability," "Prompt-Payment Credit," "Cyber Liability," "E-Invoicing / ERP Integration," and dozens more. Each concept gets a plain-English summary and a "Mentioned in" list linking back to every proposal where it appears.
Auto-extracted concepts with summaries and source citations across the document set.
Step 3 — Explore the knowledge graph
The knowledge graph maps how concepts and documents connect — which vendors share coverage types, where they diverge, and how the corpus fits together. It's the fastest way to spot gaps and overlaps before you ask a single question.
The knowledge graph links concepts and vendor documents.
Step 4 — Ask your questions, get cited answers
Now the knowledge base earns its keep. This is the second move: you start a new task, select the knowledge base you just built, and ask any of the four questions in plain English. Sharper answers only from the selected documents — returning a structured answer with inline citations on every figure and the related concepts beside it, so you can both verify the result and explore the ideas behind it.
Start with the ranking: "Rank the vendors by the maximum liability protection available, including umbrella layers." Sharper returns a ranked table — vendor, maximum liability protection, and the reasoning behind each placement — with a citation on every number. Click one and the source PDF opens to the exact passage, so the ranking isn't a black box; it's auditable.
A ranked vendor comparison with inline citations linked to the source document.
Alongside the answer, Sharper surfaces the related concepts — each with its own summary and sources — so you can verify the ranking and follow the ideas behind it without losing your place. That's the difference between an answer you read and one you can actually explore.
Related concepts, with summaries and citations, shown beside the answer.
The same flow handles the more specific asks:
- Early-payment discounts — "Which vendor rewards paying fast, and how much?" Sharper pulls the exact prompt-payment credit from each proposal and names the best one, citing the clause.
- Fit for a profile — "We're a 200-person SaaS company needing cyber coverage, NetSuite e-invoicing, and a fast quote. Who fits?" Sharper checks all three requirements against every vendor and recommends the ones that match — with the supporting passages.
And when the question is a "who does and who doesn't" split — "Which vendors support structured e-invoicing/ERP integration, and which don't?" — Sharper answers with two clean tables, platforms named and every cell cited.
A two-part comparison splitting vendors by structured e-invoicing/ERP support.
What makes Sharper different
Most AI tools give you an answer. Sharper gives you an answer you can defend — and a knowledge base you can keep exploring. That comes down to a few things:
- Every claim is cited. Each figure and ranking links to the exact sentence in the source document. No hallucinated numbers — if it's in the answer, it's in a document, and you can see which one.
- Zero setup. Upload the files and Sharper builds the documents, concepts, and knowledge graph automatically. No tagging, schema design, or manual indexing.
- Answers plus concepts. Every answer comes with its related concepts, so you can verify a claim and explore the surrounding ideas in the same place.
- Grounded in your documents. Sharper answers only from your files — not the open internet or model memory — so the result is yours, and it's auditable.
For procurement, risk, and compliance, that means faster sign-off and an answer to "where did this come from?" every single time.
FAQ
What file types can Sharper read?
Sharper handles PDFs, Word, PowerPoint, and other common document formats, parsing tables and structured content — not just plain text.
Does Sharper work outside of insurance?
Yes. The insurance proposals in the walkthrough are just an example. The same workflow applies to RFP responses, contracts, research reports, policy libraries, and any document set you need to question and compare.
How is this different from regular search or ChatGPT?
Search returns links; a general chatbot answers from memory and can hallucinate. Sharper answers only from your documents and cites the source passage behind every claim, so answers are grounded and verifiable.
What are concepts, and why do they matter?
Concepts are the ideas Sharper extracts automatically across your document set — like 'Umbrella & Excess Liability' or 'Prompt-Payment Credit.' Each answer surfaces its related concepts, so you can verify a claim and explore the surrounding ideas without leaving the page.
How long does setup take?
Upload your documents and Sharper builds the documents, concepts, and knowledge graph automatically — no tagging, schema design, or manual indexing.