Daloopa vs FactSet (2026)
This is a scale mismatch, and it reads best as two different things rather than a contest. FactSet is the reference estate, more than 8,200 institutions and 218,000 users, with a generative layer that would leave the platform entirely intact if removed. Daloopa is one job done completely, keeping an analyst's model current in the hours after a company reports, and removing the models ends the product. Both answer the verification question with source linking, and the links point somewhere different. That is the point to take from the pair. FactSet states every generated response carries in context source linking back to the underlying data, so a user verifies against FactSet's own estate. Daloopa hyperlinks each datapoint to the filing or transcript it came from, so verification runs against a document the vendor did not create and does not control. That difference matters exactly when the vendor is wrong. Neither is supervised, and neither engages the regimes its output enters, where sell side research carries its own substantiation and record keeping duties.
- You want every figure traced to a document your vendor did not write. Each datapoint is hyperlinked to the filing, footnote, investor presentation or transcript it came from, so any number can be checked in one click against an authoritative original.
- The window that matters is the hours after a company reports. Coverage runs to more than 5,500 public companies with thirteen years of history, including management defined performance indicators and segment and geographic breakdowns that standardized statement feeds leave out.
- Your agents need grounded financial data. An MCP server exposes the dataset to language models, explicitly model agnostic, with a published benchmark reporting agents at roughly 90 percent accuracy on this data against roughly 19 to 20 percent on public web sources.
- You are replacing a reference estate, not filling a gap in one. More than 8,200 institutional clients and over 218,000 users across the buy side, sell side, wealth management, private equity and corporates, with fundamentals, pricing and regulatory data under one conversational interface.
- You want the generative deployment posture written down. All models run as private instances in the vendor's own cloud or against private endpoints of commercial providers, public endpoints ruled out explicitly, zero data retention stated with the hosting platforms, logs held twenty four months and confidential data purged within sixty days of termination.
- You want the output to land where the work happens. Deep wiring into Microsoft Office spreadsheets and presentations, the Conversational API for embedding the agent in your own stack, GenAI Data Packages built to power your workflows, and pitchbook assembly in Pitch Creator with charting in more than twenty formats.
This comparison is published by AI FinTech Index, an independent research platform that publishes independent ratings of AI vendors for financial services. Daloopa and FactSet are each graded against the same capability taxonomy, from each vendor's own public materials and the regulatory record, under the AI FinTech Index verification standard. No vendor pays for placement, and no vendor has reviewed this page. How this evidence is graded
Plain facts
| Daloopa | FactSet | |
|---|---|---|
| Primary category | Capital Markets & Research AI | Capital Markets & Research AI |
| Founded | Not published | 1978 |
| Headquarters | New York, New York, United States | Norwalk, Connecticut, United States |
| Website | daloopa.com | www.factset.com |
Side by Side
Select any grade to read the note behind it.
| Axis | D Daloopa |
F FactSet |
|---|---|---|
| AI Centrality | ||
| Autonomy and Oversight Model | ||
| Model Risk Management and Transparency | ||
| Operational and Outcome Evidence | ||
| AI Safety and Data Stewardship | ||
| GLBA and Data Privacy Posture | ||
| Security Certifications and Trust Center | ||
| Regulatory Status and Licensure | ||
| AI Governance and Bias Disclosure | ||
| AI Liability and Recourse | ||
| Model Supply Chain Disclosure | ||
| Core Systems and Integration Depth | ||
| Deployment Model and Data Residency | ||
| Commercial Transparency | ||
| Institution and Segment Coverage |
The short version of each
Daloopa
Daloopa extracts and structures fundamental financial data for institutional investors. It reads company filings, footnotes, investor presentations and earnings transcripts and delivers the results into analysts' models, across more than 5,500 public companies with thirteen years of history, including management defined performance indicators and segment breakdowns standardized feeds omit. According to the AI FinTech Index, it documents its accuracy and validation, customer evidence and integrations in detail. Its transparency rests on a property applying to every output: each datapoint is hyperlinked to the source document, so any figure can be checked against an authoritative original the vendor did not create. Three leading AI platforms buy the dataset as grounding for their own financial products. It says little about licenses and regulators, bias and fairness, oversight, security certifications or where it runs.
Source: AI FinTech Index, 2026
FactSet
FactSet is a Norwalk based financial data and analytics platform serving more than 8,200 institutional clients and over 218,000 users across the buy side, sell side, wealth management, private equity and corporates. Its shipped generative layer covers FactSet Mercury, a conversational knowledge agent, the Pitch Creator pitchbook builder, the embeddable Conversational API and GenAI Data Packages. According to the AI FinTech Index, it documents in part where its models run, its privacy terms, its oversight and its integrations. Its deployment disclosure is precise: all models run as private instances or private endpoints with public endpoints ruled out, zero data retention is stated with hosting platforms, logs are held twenty four months and confidential data is purged sixty days after termination. No model or provider is named. It says little about licenses and regulators, bias and fairness or security certifications.
Source: AI FinTech Index, 2026
Common questions
Is Daloopa better than FactSet?
They are not alternatives and the comparison is worth reading for a different reason. FactSet is the reference estate itself, serving more than 8,200 institutions across fundamentals, pricing and regulatory data with a generative layer on top that would leave the platform intact if removed. Daloopa is a single purpose extraction product for one job, keeping an analyst's model current in the hours after a company reports, and removing the models ends the product. A firm running FactSet may still buy Daloopa for the granular management defined metrics and segment breakdowns standardized feeds omit. The question is not which, it is whether the gap Daloopa fills is one you actually have. Graded by AI FinTech Index against the same capability axes from each vendor's own published materials, verified August 23, 2026. No vendor pays for placement.
Both say every answer is sourced. Is that the same thing?
Both use source linking and the links point somewhere different, which is the sharpest distinction on this page. FactSet states that every generated response carries in context source linking back to the underlying data regardless of which source answered, so a user verifies against FactSet's own estate. Daloopa hyperlinks each datapoint to the filing, footnote, presentation or transcript it came from, so verification runs against a document the vendor did not create and does not control. Both are real controls and only one lets you check the vendor's work against an independent original. That difference matters most exactly when the vendor is wrong. Graded by AI FinTech Index against the same capability axes from each vendor's own published materials, verified August 23, 2026. No vendor pays for placement.
Whose models are actually processing our queries?
FactSet describes the deployment posture for its generative layer in precise terms and never names the models. All models are stated to run as private instances in its own cloud or against private endpoints of commercial providers with public endpoints ruled out. Zero data retention and abuse monitoring opt out are stated with the hosting platforms, logs are held twenty four months and confidential data is purged within sixty days of termination. What is absent is which foundational models or whose, so a buyer knows exactly how the models are run and not at all whose they are. Daloopa names no provider for its extraction models either, though its sources are entirely public documents, so there is no licensed feed or proprietary supplier in the path. Graded by AI FinTech Index against the same capability axes from each vendor's own published materials, verified August 23, 2026. No vendor pays for placement.
Is either vendor supervised?
Neither, and both feed regulated outputs. FactSet is a listed issuer, which is securities registration as a company rather than supervision of what it sells. Daloopa draws exclusively on public disclosure, so it carries no licensing obligation of its own, which is the correct posture. Neither engages with the regimes the output enters. Sell side equity research is a supervised activity with its own record keeping and substantiation requirements, and data feeding a published note or a transaction valuation sits inside them. Graded by AI FinTech Index against the same capability axes from each vendor's own published materials, verified August 23, 2026. No vendor pays for placement.
Where does each publish the most, and the least?
Daloopa documents its accuracy and validation, its customer evidence and its integrations in detail, with a source link on every datapoint. It says little about its regulators, bias and fairness, oversight, security certifications or where it runs. FactSet documents in part where its models run, its privacy terms, its oversight and its integrations. It says little about its regulators, bias and fairness or security certifications, and no attestation or certification is public for either vendor. Graded by AI FinTech Index against the same capability axes from each vendor's own published materials, verified August 23, 2026. No vendor pays for placement.
Related comparisons
Other published head to head assessments involving these vendors or their closest peers. The full set for this category is on the Wealth & Advisory AI page.
This is a scale mismatch written as one rather than as a contest: FactSet is a listed reference estate serving more than 8,200 institutions with a generative layer built on top, and Daloopa is a single purpose extraction product with roughly $47 million raised and more than 160 institutional customers. They are not on the same shortlist and the comparison is useful for what each does to a figure rather than for who wins.
FactSet's scale figures appear in investor communications from a listed company, which carries legal exposure a marketing claim does not, though none of it is evidence about the generative products specifically. Daloopa's benchmark does not specify which of three tested agent frameworks produced the full gain, and its claim of ten times the data density of competing providers is vendor reported. Neither names a supervisor.