Multi-Source Research
Search across SharePoint, Confluence, Jira, email, and more simultaneously. Get synthesized answers with source citations instead of searching each tool separately.
I watched a consultant spend 45 minutes answering a single question about a government project. Not because the question was hard. Because the answer was spread across four systems.
The project decisions lived in Confluence. The risk assessments were in SharePoint. The implementation status was in Jira tickets. The client feedback was buried in Outlook threads. She searched each system individually, mentally merged the results, forgot where she saw one specific paragraph, and searched again.
She was not doing research. She was doing tab management.
This is the use case I find most compelling for Knowably. Not because it is the simplest - it is not. But because the time savings are the most dramatic. When your job is synthesizing information from multiple systems, and you can suddenly do it in one query instead of forty-five minutes of tab switching, the difference is not incremental. It is transformative.
The Tab Problem
If you are an analyst, a consultant, or part of any research-heavy team, you know this pain.
A question like "What were the key decisions about the data migration project and what risks were flagged?" sounds like it should take five minutes. But the decisions live in Confluence. The risk assessments are in SharePoint. The implementation issues are in Jira tickets. The client feedback is in email.
Four systems. Four different search interfaces. Four different query syntaxes. Four different relevance algorithms. And you are the integration layer, holding it all together in your head.
The worst part is not the time. It is what you miss. Each system has its own search limitations. Documents that use different terminology slip through the cracks. A Jira ticket that perfectly answers your question never surfaces because you searched for "data migration" and the ticket says "database transition."
You do not even know what you did not find.
What Changes With Cross-System Search
You ask one question. Knowably searches SharePoint, Confluence, Jira, GitHub, Outlook, Google Drive, S3, and whatever else you have connected - all at once. The answer synthesizes findings from every system, with citations that link back to the original document in its original location.
A Confluence page citation opens Confluence. A Jira ticket citation opens Jira. A SharePoint file citation opens SharePoint. Click through and you are looking at the source, not a cached copy from three months ago.
That is functionally different from "enterprise search." Enterprise search gives you a ranked list of links across systems. This gives you an answer with provenance. For a consultant who needs to back up every finding, for an analyst who needs an audit trail, for a compliance team that needs traceability - the citations are not a nice-to-have. They are the whole point.
Why This Use Case Demands More From the Pipeline
I built Knowably's retrieval pipeline as a configurable set of stages, and research is where that configurability matters most. Two capabilities are essential here.
Query decomposition is the first. Research questions are almost never simple. "What decisions were made about the authentication architecture and what security vulnerabilities have been reported in the last six months?" is really two questions. The pipeline splits this into independent sub-queries, searches each one separately, and merges the results with deduplication.
This produces dramatically better recall. A single broad search favors documents that happen to mention both authentication and security. Decomposition finds the best documents for each sub-topic, even when they live in completely different systems.
Hybrid search is the second. Research across multiple teams means different vocabulary for the same concepts. Engineering calls it "data migration." The client calls it "database transition." Legal calls it "system transfer." Hybrid search combines keyword matching with semantic (meaning-based) retrieval, so terminology differences stop being a wall.
Both are configurable per agent. Heavy on keyword matching for technical research where exact terms matter. Heavy on semantic for policy research where the same concept gets described ten different ways. The right balance depends on what you are researching.
Research Scenarios That Actually Happened
Due diligence
During a government project, we connected SharePoint (financial documents, contracts), Confluence (internal analysis), and Email (stakeholder correspondence). The question was about revenue patterns and compliance risks across a set of entities.
Query decomposition split this into financial performance and compliance risk sub-queries. Semantic search caught an email where someone wrote "I have concerns about their regulatory standing" - a phrase nobody would have searched for explicitly. The answer synthesized findings from spreadsheets, wiki pages, and email threads, with each claim traced to its source.
Manually, this would have been a half-day exercise. With Knowably, it was a conversation.
Product intelligence
A product team connected Jira (customer feedback), Confluence (market research), and uploaded analyst reports. They wanted to know which features customers were requesting most and how those aligned with the current roadmap.
Hybrid search caught both exact feature names in Jira tickets and semantically similar requests phrased differently. Reranking surfaced the highest-signal feedback alongside the specific roadmap sections that addressed those requests. The product manager got a synthesized view that would have taken hours to compile manually.
Compliance and policy
This is where semantic search earns its keep. Regulatory language varies wildly between formal policy documents, informal email discussions, and public regulatory guidance. "Data retention" in one document is "record keeping" in another and "information disposal" in a third.
Connect the policy documents, the regulatory correspondence, and the public guidance. Ask what changed and whether internal policies are current. Query expansion adds the related terms automatically. The citations create the audit trail a compliance review requires.
Tuning for Research
Different research tasks need different configurations. I designed the agent system specifically for this.
Set reranking thresholds higher for precision-critical work where false positives waste time. Lower them for exploratory research where broader recall matters more. Enable query decomposition for complex multi-faceted questions. Disable it for simple lookups.
Create focused knowledge bases for specific engagements and broad ones for cross-organizational research. One agent for financial due diligence, another for technical assessments, a third for general knowledge. Each with its own search strategy tuned to the domain.
For sensitive research, Knowably supports self-hosted LLMs through Ollama. Queries and documents never leave your infrastructure. Your security team signs off, not because you convinced them, but because they verified it.
Stop Searching. Start Researching.
The gap between "searching for information" and "doing research" is where most of your team's time disappears.
That consultant I mentioned? She runs her cross-system queries through Knowably now. What used to take 45 minutes takes about 90 seconds. She does more analysis in a day than she used to do in a week.
The information was always there. The retrieval was broken.