Daloopa vs MioTech (2026)
One extracts what companies did report; the other infers what they did not. Daloopa reads filings, footnotes, presentations and transcripts to structure figures that exist in documents. MioTech mines more than 12,000 sources across over 800,000 companies precisely because sustainability data is largely unreported across Asia, so a knowledge graph infers what disclosure does not state. The verification property inverts with that difference, and it is the point to carry into both calls. Every Daloopa datapoint links to the document it came from, so a figure is checkable against an authoritative original the vendor did not create. An inferred rating cannot be checked against a document that does not exist, which is the whole point of MioTech's product, so validation would have to come from published accuracy and rating distribution figures, and none is published. Both also carry the same coverage warning: the issuers already least followed are the likeliest to be thinly or inaccurately represented, and neither publishes accuracy or coverage variation by company size, geography or reporting language.
- You want what companies actually reported, checkable. Each datapoint is hyperlinked to the filing, footnote, investor presentation or transcript it came from, so any figure can be verified in one click against an authoritative document the vendor did not create.
- 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 standardized statement feeds omit.
- Your agents need grounded numbers rather than web results. A model context protocol server exposes the dataset to language models and agents, and a published benchmark reports agents reaching roughly 90 percent accuracy on this data against roughly 19 to 20 percent on public web sources.
- What you need was never reported at all. Natural language processing mines more than 12,000 public sources across over 800,000 companies and a knowledge graph cross references supply chain, shareholding and investment data so correlations surface what disclosure does not state.
- Your exposure is Asian and the reporting is nascent. MioTech serves around 2,000 clients from Hong Kong, Shanghai, Beijing and Singapore, with several global financial institutions as both shareholders and customers, and roughly half its clients are listed companies using the software for their own disclosure.
- You want a third party check inside the workflow. A major independent assurance body's verification services are integrated directly into the platform, so a sustainability claim can be tested by an outside party without leaving the system that produced it.
This comparison is published by AI FinTech Index, an independent research platform that publishes independent ratings of AI vendors for financial services. Daloopa and MioTech 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 | MioTech | |
|---|---|---|
| Primary category | Capital Markets & Research AI | Capital Markets & Research AI |
| Founded | Not published | 2016 |
| Headquarters | New York, New York, United States | Hong Kong |
| Website | daloopa.com | www.miotech.com |
Side by Side
Select any grade to read the note behind it.
| Axis | D Daloopa |
M MioTech |
|---|---|---|
| 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 |
Product updates
The latest logged changes from each vendor, newest first, each linked to its source.
Daloopa
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Integration / interoperability
Daloopa's MCP integration now delivers its verified, source linked fundamental data inside Model ML, an AI platform that drafts pitch decks, due diligence reports and investment memos for financial and professional services firms.
Source -
Integration / interoperability
Daloopa's fundamental financial data is now natively integrated into OpenAI's ChatGPT for Financial Services, launched the same day, as one of its founding data partners.
Source -
Product / capability
Daloopa made Scout generally available, an AI agent inside Excel that builds and modifies financial models from plain language prompts.
Source
MioTech
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Acquisition / corporate
MioTech signed definitive agreements to combine with the green finance and ESG business of CCX Group, forming CCX-MioTech.
Source
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. According to the AI FinTech Index, its validation, customer evidence and integrations are documented in detail. Its transparency rests on a property applying to every output rather than to the process around it: each datapoint is hyperlinked to the document it came from, so any figure a model produced can be checked against an authoritative original the vendor did not create. Its source material is entirely public disclosure, so no licensed feed or proprietary supplier sits in the path. It says little about its regulators, bias or oversight, and little about security certifications or where data sits.
Source: AI FinTech Index, 2026
MioTech
MioTech builds sustainability and ESG data infrastructure for Asian capital markets. It uses natural language processing and a knowledge graph to mine more than 12,000 public sources across over 800,000 companies, then combines that with supply chain, shareholding and investment data to produce ratings, indexes, real time risk monitoring and research. According to the AI FinTech Index, its customer evidence is documented in detail. Its regulators, validation and oversight are documented in part, as are its integrations and the models it is built on. It exists because the underlying data is largely unreported in Asia, so its models infer what European and American markets can simply collect, and a major independent assurance body's verification services are integrated directly into the platform. It says little about bias, privacy terms or what happens when it is wrong, and little about security certifications or where data sits.
Source: AI FinTech Index, 2026
Common questions
Is Daloopa better than MioTech?
One extracts what companies did report and the other infers what they did not, which is a difference in kind rather than in quality. Daloopa reads filings, footnotes, presentations and transcripts to structure figures that exist in documents, aimed at fundamental analysts building models. MioTech mines more than 12,000 sources across over 800,000 companies precisely because sustainability data is largely unreported across Asia, so a knowledge graph infers what disclosure does not state. They do not compete for the same budget. If your gap is speed and granularity on reported financials, Daloopa. If your gap is that the information does not exist, MioTech. 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.
Can we check either vendor's output?
Daloopa can and MioTech structurally cannot, and that follows from the product rather than from effort. Every Daloopa datapoint is hyperlinked to the filing or transcript it was extracted from, so a figure is checkable against an authoritative original. MioTech describes its method, resting on a knowledge graph of cross references and correlation across a stated source count, and integrates a major assurance body's verification into the platform, which is a real external check. But an inferred rating cannot be checked against a document that does not exist, which is the point of the product, so validation would rest on published accuracy and rating distribution figures, and none is published. 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.
Does coverage quality vary across companies?
Both carry the same warning. Daloopa's universe of 5,500 companies necessarily excludes most listed issuers globally, and extraction is easier on dense, well structured English language disclosure, so the issuers already least followed by analysts are the likeliest to be thinly represented in a dataset increasingly used to ground automated research. MioTech's ratings inferred from public sources favor companies generating more observable material, meaning larger, listed, English reporting firms, so smaller or domestically reporting companies score worse for reporting less rather than behaving worse. Neither publishes coverage or accuracy broken down by company size, geography or reporting language. 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 publishes most on validation, customer evidence and integrations, and documents privacy terms, liability and the data it is built on in part. MioTech publishes most on customer evidence, and documents its regulators, validation and oversight in part. Both say little about bias, security certifications or where data sits. 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 Capital Markets & Research AI page.
The verification asymmetry is structural rather than a difference in diligence, and it follows from what each product is for: an extracted figure can be checked against the document it came from, and an inferred rating cannot be checked against a document that does not exist. On evidence quality, Daloopa's benchmark does not specify which of three tested agent frameworks produced the full gain, and its data density claim is vendor reported.
MioTech publishes no accuracy, coverage completeness or rating validation figure at all, which matters because environmental and governance ratings are known to diverge substantially between providers. Both also carry the same coverage warning: the issuers already least followed are the likeliest to be thinly or inaccurately represented, and neither publishes accuracy or coverage variation by company size, geography or reporting language.