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 finding a buyer should 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, which is why it holds A on model risk management and transparency in the AI FinTech Index. 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 records 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 need 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 standardised 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
| 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 |
The short version of each
Daloopa
Daloopa extracts and structures fundamental financial data for institutional investors, reading company filings, footnotes, investor presentations and earnings transcripts and delivering the results into analysts' models, across more than 5,500 public companies with thirteen years of history. The AI FinTech Index grades it A on model risk management and transparency, A on operational and outcome evidence and A on core systems and integration depth, documenting four of the nine regulatory axes the index tracks against an index average of 2.93 across 489 vendors. Its transparency grade 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 corpus is entirely public disclosure, so no licensed feed or proprietary supplier sits in the path. Regulatory status, governance and bias, autonomy, security certifications and deployment residency are graded C.
Source: AI FinTech Index, 2026
MioTech
MioTech builds sustainability and ESG data infrastructure for Asian capital markets, using natural language processing and a knowledge graph to mine more than 12,000 public sources across over 800,000 companies, then combining that with supply chain, shareholding and investment data to produce ratings, indexes, real time risk monitoring and research. The AI FinTech Index grades it A on operational and outcome evidence, with B on regulatory status, model risk management, autonomy, core systems integration and model supply chain, documenting four of the nine regulatory axes the index tracks. It exists because the underlying data is largely unreported in Asia, so its models must infer what European and American markets can simply collect, and a major independent assurance body's verification services are integrated directly into the platform. Governance and bias, GLBA posture, liability and recourse, security certifications and deployment residency are graded C.
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, which is why it holds A on model risk management and transparency in the AI FinTech Index. MioTech grades B: it 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 genuine 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 has to come from published accuracy and rating distribution figures, and none is published.
Does coverage quality vary across companies?
Both records carry the same warning and neither publishes the analysis. 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 favour 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. Ask both for coverage and accuracy broken down by company size, geography and 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.
How does the AI FinTech Index grade Daloopa and MioTech?
Both are graded on the same fifteen capability axes, with every grade traceable to the public artifact it was read from and the date it was verified. Each documents four of the nine regulatory axes at A or B, against an index average of 2.93 across 489 vendors, and the AI FinTech Index publishes no composite score. Daloopa holds A on model risk management, operational evidence and core systems integration, with B on GLBA posture, liability and supply chain. MioTech holds A on operational evidence, with B on regulatory status, model risk, autonomy, integration and supply chain. Both grade C on governance and bias, security certifications and deployment residency.
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 here 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, while 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 records 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.