The AI Tool That Could Replace Half Your Research Workload

Answer this is pitching itself as an end-to-end “research copilot” that goes well beyond chatbot-style answers—aiming to help scholars find literature gaps, map citations, extract insights from papers, and draft research writing with tighter academic controls. The core appeal is that it combines research discovery (including PDF upload and web/database searching) with analysis and writing workflows in one interface, then adds an extra layer: an AI editor that lets users iteratively rewrite, summarize, generate outlines, and even pull in new sources to support claims.

In practice, the workflow starts like a research-focused chat. Users type a question, optionally add tags, and then choose an “auto” mode that selects an appropriate review approach. The tool can search the internet, scan uploaded PDFs, and query a user’s library. What separates it from generic AI assistants is the emphasis on academic constraints: users can set minimum citation thresholds, choose databases, toggle web searches, and filter by journal quality tiers (with options like Q1/Q2 and broader “all” for early-stage exploration). A sample literature-review request on self-healing electrodes and nano composite materials produced a reference list and a review draft that the reviewer found useful for sourcing—even if not the most detailed review compared with specialized literature-review tools.

Where Answer this leans hardest into “research acceleration” is its side panel of deeper analysis. After selecting papers, it can run biblometric analysis—showing publications by year, citation trends, citation impact, word clouds, and top authors/terms. It also supports follow-up Q&A tied to available PDFs, plus tools for exploring a research area through citation networks. The library features extend this: table views can add columns such as “research gaps” and “future work” for each paper, while key findings can be summarized across a set of documents. For discovery, “search papers” targets specific topics (e.g., nano composite OPV devices), and citation mapping functions like a network graph around a seed paper, highlighting most cited/connected papers and top contributing authors.

Writing and control are handled through an “edit with AI” environment. Instead of treating generated text as a final output, users can highlight sections and request rewording, summarization, question generation, or custom prompts. The editor can also search for new sources to back up what’s being written, and it supports continuing drafts from a cursor position while maintaining structure. Additional productivity features include extracting data from papers and generating structured outputs like diagrams (flowcharts, mind maps, and other diagram types), though some diagram formats may be more useful in certain fields than others.

Finally, Answer this adds a meta-feature: building custom academic AI tools. Users can define a tool name, provide descriptions, set a system prompt, select capabilities, and upload PDFs—effectively creating a tailored assistant such as a literature review assistant. The overall message is that Answer this is trying to become a one-stop shop for the full research lifecycle, with the most distinctive differentiator being its combination of academic filtering, citation/bibliometric analysis, and an editor that keeps users in charge of iterative writing and sourcing.