Answers Engine

This is the moment this feature was built for.

It is 8:40 in the morning. The meeting is at nine. Someone senior wants to know how your pricing compares to the two competitors you worry about, and whether the research supports the position you are about to take.

A search box returns twelve documents. Three are relevant. Two of those are 60 page decks. You have twenty minutes and you are going to spend all of them skimming, then walk into the room having read a third of the evidence and hoping nobody asks a follow-up.

The Answers Engine does that reading. You ask a question in plain language, and it synthesizes an answer across every source in your hub that you have permission to see, with citations attached to each claim, so you can check the work before you put your name on it.

What makes it different from search

Traditional search is a retrieval problem. You give it terms, it gives you documents, you do the synthesis. That model has been unchanged since roughly 1998 and it works fine when you know which document you want.

The Answers Engine is a synthesis problem. You give it a question, it reads across the corpus, and it composes a response drawn from multiple sources at once.

The difference shows up on questions that no single document answers. "How has agent sentiment on our claims process changed over the last three years" is not in any one file. It is distributed across four annual surveys, two focus group transcripts, and a broker advisory summary. A search box cannot answer it. The Answers Engine can, because it pulls from all six and tells you which is which.

Citations are the entire point

Every claim in an answer links to the source it came from, down to the document and the section. You can open the underlying report in one click and read the original context.

We are firm about this for two reasons.

First, nobody in a regulated industry, or in any industry with a functioning legal department, is going to act on an unsourced AI assertion. The citation is what converts an interesting output into a usable one.

Second, the citation is your defense against the failure mode everyone worries about. If the engine gets something subtly wrong, the source is right there and you will catch it in fifteen seconds. An answer you cannot verify is no faster than reading the document, and it is riskier.

Permissions are enforced at the answer layer

This sounds like a footnote and it is the hardest engineering problem in the feature.

The Answers Engine only reads what the person asking is allowed to read. If a user cannot access the M&A hub, no content from the M&A hub informs their answer, and no trace of it appears in the citations. Two people in different roles asking the identical question can receive different answers, correctly.

Anyone who has watched an enterprise AI deployment go wrong knows why this matters. The failure mode is the AI cheerfully summarizing the restricted compensation analysis for someone three levels below the people who were supposed to see it.

Use cases, by the person asking the question

Insights and research: stop rerunning studies you already ran

Before commissioning anything, ask the engine what the organization already knows. It reads across every study in the hub and tells you what has been established, what is contested, and where the coverage is thin, with sources.

Our customers describe this as the duplicate research check, and it is often the first business case they write down, because a single avoided study usually costs more than the annual platform fee.

Executives and chiefs of staff: twenty minutes before the board meeting

"What do we know about growth in the mid-market segment, and what has changed since last year?"

You get a synthesized answer with citations, which you can read on your phone in the elevator, and you can drill into the source if someone pushes. This is the use case that gets the platform renewed, because it is the one the CEO personally experiences.

Competitive intelligence: the question that spans everything you have collected

"What has Competitor X signaled about their enterprise strategy over the last six months?"

The evidence lives in an earnings transcript, four clipped press articles, a partner conversation summary, a job postings analysis, and a conference recap. Six sources, one answer, with the receipts.

Product and strategy: pressure-test a hypothesis

"Does our research support the claim that switching cost is the main barrier in this category?"

The engine gives you the supporting evidence and, when it exists, the contradicting evidence. The contradicting evidence is the valuable half and it is the half a manual search will never surface, because people search for confirmation. We all do.

Underwriting, claims, and risk: the specific technical question

"What do we have on wildfire exposure modeling in the Pacific Northwest, and what were the stated limitations?"

Technical questions with technical caveats, answered with the caveats intact. The limitations section of a study is the part that gets lost when someone summarizes from memory, and it is the part that gets you in trouble.

Brand and marketing: the brief that writes itself faster

"What has our testing shown about humor in this category, and how did it perform by age cohort?"

Brief writing changes from an archaeology exercise into an editing exercise. The quality floor across the organization rises, which is a much larger effect than making one good strategist slightly faster.

New hires and transitions: three months of context in an afternoon

A new brand manager, analyst, or executive can interrogate the organization's accumulated knowledge conversationally instead of booking twelve introductory meetings. Ask what the company believes about a segment, why the last launch underperformed, what the tracker has shown since 2022.

We have customers who make this the first task on the onboarding checklist. It is the cheapest onboarding improvement available to a large company.

What we are not

We are not a general-purpose chatbot. The engine answers from your hub. It does not have opinions about your strategy, it will not draft your deck, and if the answer is not in your content, it says so rather than filling the gap with something plausible. That last behavior is deliberate and it is the single most important design decision in the product.

We are not a substitute for reading the study when the stakes are high. The engine is excellent at getting you to the right three documents with the right context in ninety seconds. If you are about to make a nine figure decision, open the documents. We built the citations specifically so that you can.

We do not train foundation models on your content. Your hub is your hub. This question comes up in every security review and the answer has never changed.

Objections we hear, answered

"How do we know it is not making things up?" Because every claim carries a citation you can open, and because the engine is constrained to your corpus rather than generating freely. The verification loop is built into the interface rather than left to your good intentions.

"Our content is messy. Will this even work?" Better than you expect, because content is processed, summarized, and tagged on arrival rather than depending on anyone having filed it correctly. Messy content is the normal starting condition. It is why the feature exists.

"We already have an AI assistant from our productivity suite." It searches everything, including twelve years of meeting invitations and the office move announcement. Sharpr works over a curated knowledge base, which means the signal to noise ratio going in is fundamentally different, and that ratio is most of what determines answer quality.

Try this

Take the hardest question your leadership asked your team this year. The one that took three people two days to answer.

Bring it to a demo. We will run it against a sample hub and you can judge the output the way you would judge an analyst's memo, which is by checking the sources.