AI research workspace for analysts
The research workspace
Serious research work — diligence, underwriting, analysis — dies in tabs: filings in one, notes in another, the thesis in someone's head. A research workspace puts the documents, the extracted facts, and the evolving thesis in one place, with AI doing the gathering and a person doing the thinking.
01
What it is
A workspace organized around the research object — a company, a deal, a claim — rather than around files. Documents centralize under the object, key facts get extracted with their sources attached, and the thesis is tracked as it evolves instead of living in the last analyst's memory.
The AI's role is leverage on the reading: surfacing what matters in a new filing, extracting comparable figures across documents, flagging what changed since last quarter. The weighting, the skepticism, and the call stay with the analyst. It's the difference between a chat assistant and a system of record for thinking.
02
When it's the right wedge — and when it isn't
It's the right first build when:
- Analysts spend more time assembling material than analyzing it — and they'll say so if asked.
- The research has a repeatable shape: deal after deal, claim after claim, the same questions against new documents.
- Conclusions must be defensible later — sourced facts beat remembered impressions when the decision gets revisited.
- The team needs shared context, not private chat histories that leave with the analyst.
It's the wrong wedge for one-off research questions — a good general assistant covers those. If the workflow has no repeatable shape yet, build the habit before the software. And if the team won't move out of email and spreadsheets, the workspace becomes a second place to not look — adoption is a precondition we test for at the wedge stage, not a feature we add later.
03
How it's built
The four layers:
- Entities — research object, document, sourced fact, note, thesis version. The unit of organization is the thing being researched.
- Workflow — intake documents → extract and source → assemble → analyze → record the call, so the object's state is always inspectable.
- Agents — ingestion and fact extraction with citations, cross-referencing across the object's documents, and watch agents that flag new filings or material changes.
- Surface — the workspace itself: object-centric, with the source panel one click from every extracted fact, because analysts trust what they can check.
Built on the Sprinter Platform's document and agent modules, with the same grounding discipline as document-grounded RAG: no fact without a source.
04
Evidence boundary
MortgageQ provides research-prototype evidence for a related workspace problem: source-aware mortgage-program lookup across wholesale lender guidelines. A research workspace for another domain would still need its own users, source corpus, permissions, and acceptance tests; no customer outcome or scale is inferred from the prototype.
05
What a v1 proves in weeks
One research shape — one deal type, one claim type — supported end to end for a small analyst group. The v1 proves that assembly time drops enough that analysts feel it and say so, that extracted facts carry citations an analyst will actually click and trust, and that the thesis record survives a handoff: a new analyst picks up the object and knows where it stands without a meeting.
That handoff test is the sharpest one. If the workspace can onboard a person to a live piece of research faster than the old way, it's earning its place; if not, better to know after one deal type than after a platform rollout.
FAQ
Questions this pattern has to answer.
Does the AI write the analysis?
It drafts the assembly — sourced facts, summaries, comparisons across documents. The judgment is the analyst's. A workspace that pretends otherwise gets ignored by exactly the professionals it's built for, and they'd be right to ignore it.
How is this different from a data room?
A data room stores documents. The workspace extracts and cross-references what's in them, attaches sources to every fact, and tracks the thinking over time. One is a filing cabinet; the other is where the work happens.
Our sources are subscription services with usage terms.
Respected in the spec: ingestion is scoped to what you're licensed to process, and the boundaries get written down before build. That conversation happens at spec time, not after a terms-of-service surprise.
Is our research used to train models?
No. The workspace runs against your storage, and model calls are scoped to the task and logged in the agent harness. Your diligence is your asset — the system's job is to compound it for you, not for a model vendor.
Next step
Score the wedge before you build.
Bring the workflow, owner, data, and proof line. The review turns that into a build, fix-first, or don’t-build call.