Dataglade

Name: Dataglade
Brand: Dataglade

AI Native

The complete AI-powered financial research terminal.

Est 2022|Valuation $7.1M|Raised $500K

LinkedIn 1K|Website|FTEs 3

MarketsInvestment Research & Market Intelligence Platforms Market Data & Reference Data

Published Aug 26, 2026

01Common complaints

Organizations should consider alternatives in these situations: 1. Need for a fully de-risked incumbent with extensive content breadth, an established compliance posture, and a long operational track record (consider Bloomberg, FactSet, Refinitiv). 2. Requirement for public proof of security compliance (SOC 2 Type II / ISO) and the vendor is unable or unwilling to provide documentation under NDA. 3. Need for a wide body of third-party reviews to support internal procurement decisions; the limited review footprint increases decision risk. 4. Being a small team that requires price transparency and a straightforward self-serve procurement motion (tools like Koyfin publish pricing and have a clear self-serve path). 5. Requirement for extensive licensed research content (sell-side research, expert calls); other vendors may have stronger content licensing for those use cases.

Deal disqualifiers (poor fit)

Organizations that require publicly documented SOC 2/ISO certifications before engaging with vendors
Teams that require fully transparent self-serve pricing and a free trial
Workflows requiring the maximum breadth of historical content and incumbent integrations

Vendor-acknowledged limitations

No explicit public vendor disclosures identifying known issues or a public roadmap were found in the reviewed sources.

Practical limitations implied by sources

- Terms of service link issue: The site’s Terms of Service link appears to point to a misspelled domain (datagalde.com) and returned a 502 error when accessed during review. This may be temporary, but accessible contracting terms are an important procurement expectation. - Security certifications not publicly documented: This may delay enterprise procurement cycles if documentation is not available under NDA. - Sparse third-party validation: Limited presence on review platforms means evaluation should rely more on pilots and direct references.

02Why they buy

Differentiators (vendor-claimed)

Source-linked fundamentals: The vendor states that fundamental numbers link back to source documents, which supports traceability.
Integrated market signals: Options flow and insider transactions are presented within the same environment as research outputs.
Internal data linking: The product claims the ability to integrate internal data to assist analysis and surface proprietary signals.

Where competitors may provide stronger capabilities

Incumbent terminals: broader content, deeper integrations, and established compliance packages.
AlphaSense: broader content aggregation and enterprise-oriented intelligence workflows.
Koyfin: greater price transparency and accessibility for smaller teams.

03Field reports

Review platform coverage reality

Dataglade has limited presence on major B2B review platforms (G2, TrustRadius, Gartner Peer Insights) based on accessible search results. - Gartner Peer Insights: No accessible Dataglade product page found in the reviewed search results. - G2: No Dataglade listing surfaced in accessible searches. - TrustRadius: No Dataglade listing surfaced in accessible searches. As a result, third-party review evidence is constrained; most readily accessible public content is vendor-authored marketing or directory-style summaries.

Positive signals (vendor and community-adjacent)

The vendor states that fundamental numbers link to sources, which implies traceability and auditability for research workflows.
A Dataglade LinkedIn announcement describes a product update labeled "Hummingbird v2.0" and states training on a large corpus of financial data and UX improvements. This is vendor-authored content and not independent validation.

Negative signals / skepticism indicators

Vendor claims of very large traded volume do not include a disclosed methodology.
Limited independent third-party validation on major review platforms.
Security certifications are not publicly documented.

Direct quotes / end-user review constraints

No accessible Gartner/G2/TrustRadius/Hacker News/Reddit end-user review quotes about Dataglade were found in the reviewed sources.
The LinkedIn content referenced above is vendor-authored rather than a customer review.

Implication: Prospective buyers should request named reference customers, run a pilot or proof of concept, and obtain detailed demonstrations and Q&A to compensate for the limited public review footprint.

Public case studies

No public Dataglade case studies with named customers and independently verifiable metrics were found in the reviewed sources.

Vendor metrics and claims (critical view)

- The vendor has made claims of substantial daily traded volume. The methodology behind this metric (e.g., customer AUM, inferred order flow, user counts) is not disclosed publicly. Recommendation for prospective buyers: Request the vendor to provide: - Named reference calls that match the buyer's segment - Before/after analyst time studies demonstrating measurable workflow improvements - Data QA methodology (source lists, normalization approach, handling of restatements) - Model evaluation methodology for AI outputs, including hallucination mitigation and testing procedures

04Best fit

Company sizes

A couple of plausible ICP bands (to validate during qualification):

Small-to-mid buy-side / research shops: small-to-mid sized analyst teams, where a newer tool can be adopted rapidly.
Fintechs / developer teams: organizations needing a data API for equities/fundamentals/market data.

Industries / verticals

Hedge funds and asset managers
Wealth management firms
Fintech product teams
Investment banking / consulting research teams

Roles and personas

Buy-side analysts, portfolio managers
Options/flow-focused traders
Research associates responsible for comps and earnings-call summaries
Data engineers / quants integrating market/fundamental data via API

Pain points addressed

Time spent locating and linking filings and transcripts
Need for faster synthesis and comparable analysis
Monitoring events (earnings, insider trades, options flow)
Generating structured analysis frameworks efficiently

Qualification criteria (good fit)

Teams that value AI-assisted research but require traceability to source documents
Organizations able to run a pilot with a newer vendor and provide feedback
Workflows that benefit from integrated options-flow and insider monitoring

05The pitch

What the product does (objective value proposition)

Dataglade is a market-intelligence platform focused on public markets research and trading workflows. The vendor describes the product as an AI-enabled terminal that combines: - Fundamental financial data and company analytics - Earnings materials (results, call summaries, transcripts) - Market features such as options flow and insider trade monitoring - Natural-language research outputs with citations and traceability to source material Dataglade also offers a developer-facing data API (separate from the UI terminal).

Target market and positioning

Dataglade targets a mix of:

Buy-side institutions (hedge funds)
Wealth managers
Fintech companies
Individual traders (vendor messaging suggests accessibility for individual users)

Positioning focus: positioned to serve institutional workflows with an emphasis on speed and traceability of source material, presented as an alternative to legacy terminals.

Get a comprehensive analysis of Dataglade including market position, competitive landscape, adoption trends, and peer benchmarks.

Download full report →

Published Aug 26, 2026

01Common complaints

Organizations should consider alternatives in these situations:

Need for a fully de-risked incumbent with extensive content breadth, an established compliance posture, and a long operational track record (consider Bloomberg, FactSet, Refinitiv).
Requirement for public proof of security compliance (SOC 2 Type II / ISO) and the vendor is unable or unwilling to provide documentation under NDA.
Need for a wide body of third-party reviews to support internal procurement decisions; the limited review footprint increases decision risk.
Being a small team that requires price transparency and a straightforward self-serve procurement motion (tools like Koyfin publish pricing and have a clear self-serve path).
Requirement for extensive licensed research content (sell-side research, expert calls); other vendors may have stronger content licensing for those use cases.

Deal disqualifiers (poor fit)

Organizations that require publicly documented SOC 2/ISO certifications before engaging with vendors
Teams that require fully transparent self-serve pricing and a free trial
Workflows requiring the maximum breadth of historical content and incumbent integrations

Vendor-acknowledged limitations

No explicit public vendor disclosures identifying known issues or a public roadmap were found in the reviewed sources.

Practical limitations implied by sources

Terms of service link issue: The site’s Terms of Service link appears to point to a misspelled domain (datagalde.com) and returned a 502 error when accessed during review. This may be temporary, but accessible contracting terms are an important procurement expectation.
Security certifications not publicly documented: This may delay enterprise procurement cycles if documentation is not available under NDA.
Sparse third-party validation: Limited presence on review platforms means evaluation should rely more on pilots and direct references.

02Why they buy

Differentiators (vendor-claimed)

Source-linked fundamentals: The vendor states that fundamental numbers link back to source documents, which supports traceability.
Integrated market signals: Options flow and insider transactions are presented within the same environment as research outputs.
Internal data linking: The product claims the ability to integrate internal data to assist analysis and surface proprietary signals.

Where competitors may provide stronger capabilities

Incumbent terminals: broader content, deeper integrations, and established compliance packages.
AlphaSense: broader content aggregation and enterprise-oriented intelligence workflows.
Koyfin: greater price transparency and accessibility for smaller teams.

03Field reports

Review platform coverage reality

Dataglade has limited presence on major B2B review platforms (G2, TrustRadius, Gartner Peer Insights) based on accessible search results.

Gartner Peer Insights: No accessible Dataglade product page found in the reviewed search results.
G2: No Dataglade listing surfaced in accessible searches.
TrustRadius: No Dataglade listing surfaced in accessible searches.

As a result, third-party review evidence is constrained; most readily accessible public content is vendor-authored marketing or directory-style summaries.

Positive signals (vendor and community-adjacent)

The vendor states that fundamental numbers link to sources, which implies traceability and auditability for research workflows.
A Dataglade LinkedIn announcement describes a product update labeled "Hummingbird v2.0" and states training on a large corpus of financial data and UX improvements. This is vendor-authored content and not independent validation.

Negative signals / skepticism indicators

Vendor claims of very large traded volume do not include a disclosed methodology.
Limited independent third-party validation on major review platforms.
Security certifications are not publicly documented.

Direct quotes / end-user review constraints

No accessible Gartner/G2/TrustRadius/Hacker News/Reddit end-user review quotes about Dataglade were found in the reviewed sources.
The LinkedIn content referenced above is vendor-authored rather than a customer review.

Public case studies

No public Dataglade case studies with named customers and independently verifiable metrics were found in the reviewed sources.

Vendor metrics and claims (critical view)

The vendor has made claims of substantial daily traded volume. The methodology behind this metric (e.g., customer AUM, inferred order flow, user counts) is not disclosed publicly.

Recommendation for prospective buyers: Request the vendor to provide:

Named reference calls that match the buyer's segment
Before/after analyst time studies demonstrating measurable workflow improvements
Data QA methodology (source lists, normalization approach, handling of restatements)
Model evaluation methodology for AI outputs, including hallucination mitigation and testing procedures

04Best fit

Company sizes

A couple of plausible ICP bands (to validate during qualification):

Small-to-mid buy-side / research shops: small-to-mid sized analyst teams, where a newer tool can be adopted rapidly.
Fintechs / developer teams: organizations needing a data API for equities/fundamentals/market data.

Industries / verticals

Hedge funds and asset managers
Wealth management firms
Fintech product teams
Investment banking / consulting research teams

Roles and personas

Buy-side analysts, portfolio managers
Options/flow-focused traders
Research associates responsible for comps and earnings-call summaries
Data engineers / quants integrating market/fundamental data via API

Pain points addressed

Time spent locating and linking filings and transcripts
Need for faster synthesis and comparable analysis
Monitoring events (earnings, insider trades, options flow)
Generating structured analysis frameworks efficiently

Qualification criteria (good fit)

Teams that value AI-assisted research but require traceability to source documents
Organizations able to run a pilot with a newer vendor and provide feedback
Workflows that benefit from integrated options-flow and insider monitoring

05The pitch

What the product does (objective value proposition)

Dataglade is a market-intelligence platform focused on public markets research and trading workflows. The vendor describes the product as an AI-enabled terminal that combines:

Fundamental financial data and company analytics
Earnings materials (results, call summaries, transcripts)
Market features such as options flow and insider trade monitoring
Natural-language research outputs with citations and traceability to source material

Dataglade also offers a developer-facing data API (separate from the UI terminal).

Target market and positioning

Dataglade targets a mix of:

Buy-side institutions (hedge funds)
Wealth managers
Fintech companies
Individual traders (vendor messaging suggests accessibility for individual users)

Positioning focus: positioned to serve institutional workflows with an emphasis on speed and traceability of source material, presented as an alternative to legacy terminals.

Get a comprehensive analysis of Dataglade including market position, competitive landscape, adoption trends, and peer benchmarks.

Download full report →