SYNTEX CONTENT QUERY
Under the suite of Microsoft Syntex, content query is an advanced search experience that scans extracted metadata to find specific documents in a library. Content query provides a quick and focused way to help users exponentially decrease the amount of time looking for a document. Evolving from a Microsoft SharePoint’s classic keyword search, this experience helps customers find specific content in files based on extracted metadata.
Increase the number of Syntex licenses that are purchased
Metadata search is aimed at helping people find documents based on metadata. When you have a large library of similar documents, this is much more powerful than the keyword search which exists in the search box today and only looks for matches with no context. You can imagine that “total”=”$25” is a much more specific search than a keyword search that looks for the word “total” and the number “25” anywhere in a document. You can also make complex queries which look for multiple aspects like “total” = “$25” AND “status” = “approved” or “pending”. You would not be able to do this with the keyword search that exists today.
Feature team includes 1 design, 1 PM, 4 engineers, 1 researcher
Syntex add-on
How it works with AI models
Document libraries house thousands of documents, each file have column representations which signify metadata. AI models are run to extract content information and display them as metadata in the library, search can be run utilizing that extracted data
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Opportunity and motivation
Many customers refused to rely on search and instead organize folders and browse, which they admit is not their preferred solution. For those that do search, they have to review pages and pages of results and can only rely on metadata filters, if they exist, to get to the information they seek. In the midst of manual discovery, content query pushes to exponentially decrease time to success and increase the value of search for customers. By providing a full circle search experience along side AI models, users can find what need quickly and efficiently.
After
Before
Research intake & competitive analysis
An advanced search experience is nothing new f / Research helped create a realistic approach for our feature in short term and long term. Also identified technical constraints and challenges early on. Helped us vision and plan a north star, realistic MVP and planning for the next year. Identify growth opportunities and constant conversations with customers.
Collaboration with Microsoft search team all up, alignment with different M365 product areas
Work with internal group of customers
Key findings and principles
Not a query builder
Scalable and systematic
Growth opportunity
User scenarios
Customer conversations and understanding
Accounts payable / Accounts receivable
A customer calls in with a question about their statement. The call center employee asks the customer for their name, account number and the month of the statement in question. They then execute a search and can see all the notes that have been made internally about this invoice.
Human resources (Recruiter, HR staff)
- A recruiter is looking for all resumes submitted since last Tuesday for job ID 231569 where status is "new" or "in Review".
- An HR pro wants to call up an employees W2 as there was an inquiry about a potential error.
Insurance claims
A field agent is heading to a site and must pull up all relevant documents associated with a specific claim.
Design principles
Design iterations
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Solution
Provide customers with a set of default parameters they can begin their search with. Starting with keyword search, provide the top 5 query criteria. If a model is running on this library, provide an affordance to add extracted metadata options to search by as well.