Daloopa

Name: Daloopa
Brand: Daloopa

The financial data layer for the agentic era

Est 2018|Valuation $307M|Raised $106.6M

LinkedIn 34K|Web traffic 42K|FTEs 194|HQ New York, New York, US

MarketsVerified Financial Database Investment Research & Market Intelligence Platforms

Published Oct 7, 2026

01Common complaints

1) Organizations with strict vendor compliance requirements that cannot proceed without published SOC 2/ISO evidence - If procurement requires a SOC 2 Type II report up front, evaluation may be delayed unless the vendor can provide documentation under NDA. 2) Use cases that primarily require qualitative research discovery (search across expert calls, broker research, news, transcripts) rather than structured fundamentals - Document-search platforms may deliver more immediate value for those needs. 3) Need for a full terminal ecosystem - Bloomberg, FactSet, and CapIQ may be better choices if integrated news, analytics, messaging, and extensive datasets in a single environment are required. 4) Organizations that cannot tolerate workflow change in Excel models - If teams will not retrofit models to accommodate an add-in or linked cells, implementation ROI may be limited. 5) Requirements for guaranteed coverage of specific geographies, small caps, or highly specialized KPIs without prior validation - Coverage claims should be validated in a pilot or via references.

Deal disqualifiers (indicative of a poor fit)

- Organizations that require publicly documented SOC 2 Type II or ISO 27001 prior to engagement (unless the vendor can provide such documentation under NDA) - Teams whose primary need is document search and qualitative research rather than structured time series (document search platforms may provide better immediate value) - Organizations that already rely on a terminal/workstation environment (Bloomberg/FactSet/CapIQ) and do not require additional disclosed-KPI detail

Publicly observable limitations

Limited independent review footprint on major review platforms in the sources examined
Limited public documentation of security certifications (SOC 2/ISO)
Pricing opacity ("Talk to Sales") which complicates direct ROI comparisons

Product maturity signals

Scout is in beta with limited customer access, indicating ongoing development and potential maturity risk for early adopters.

Coverage and normalization limits (category-level risk)

Even with extensive extraction of disclosed KPIs, practical limits include:

Metric definition drift across companies
Inconsistent segment reporting formats
Non-GAAP normalization challenges

02Why they buy

Product design strengths supported by documentation and product behavior

Auditability via source links: the vendor emphasizes hyperlinks to original documents for datapoints to support lineage and traceability.
Excel-native workflow: an add-in and the Scout concept focus on analyst workflows that center on spreadsheets.
Agent/LLM integration strategy: MCP server and connector ecosystem are positioned to enable LLMs to query a structured dataset with source links.

Claims that require verification

Claims of more complete historical coverage (e.g., claims of substantially higher datapoint volumes)
High accuracy metrics reported by the vendor (e.g., figures presented without independent audit)

Areas where competitors may have an advantage

Incumbents (Bloomberg/FactSet/CapIQ): broader coverage, longer procurement track records, and visible enterprise compliance documentation
AlphaSense: document search scale, alerting, and qualitative research workflows
Canalyst: standardized modeled datasets optimized for comparability in specific workflows

03Field reports

Review-platform availability (G2 / Gartner Peer Insights / TrustRadius)

- G2: Daloopa has a product presence and an alternatives/competitors page, but public crawling did not surface robust, accessible user review excerpts in the sources retrieved. - Gartner Peer Insights: searches did not yield accessible Daloopa review pages in the sources retrieved; it may not be listed or may have limited visibility. - TrustRadius: no Daloopa-specific TrustRadius review page was retrieved in the sources reviewed. Implication: for buyer diligence, it may be necessary to rely more on (a) vendor-provided references, (b) peer network checks, and (c) community discussions, because mainstream review platforms did not provide substantial, high-signal coverage in this research set.

Community/social proof (Reddit / LinkedIn) — what was identified

Reddit: historical posts from earlier years reference Daloopa as a mailing list distributing free corporate/industry Excel models; these posts do not provide contemporary enterprise product reviews but indicate early grassroots distribution.
LinkedIn: vendor and employee posts announce beta access for Scout (AI Excel agent) and include supportive comments; these posts are informative for product direction but are not independent reviews.

Vendor claims that substitute for "reviews"

The vendor makes performance and coverage claims on its website, including statements of very high accuracy for certain datasets and rapid update timings for some subscription tiers. These are vendor claims and should be validated through independent testing or customer references.

Common buyer reasons to choose Daloopa (inferred from positioning and product design)

Reducing analyst manual effort during model updates
Need for traceability (hyperlinks to source documents)
Operationalizing LLMs/agents with a data layer that provides source lineage
Faster earnings-season updates compared to fully manual processes (as represented by vendor materials)

Common buyer objections / reasons to leave (inferred from market dynamics)

These are risk areas inferred from product materials and market context rather than established churn drivers from public independent reviews: - Lack of publicly documented security/compliance certifications (e.g., SOC 2/ISO) - Potential coverage gaps for specific tickers, KPIs, or bespoke disclosures - Preference for standardized modeled datasets over disclosed KPIs - Pricing opacity necessitating enterprise negotiation - Potential workflow lock-in to Excel add-in or specific update mechanisms

Vendor-published benchmarks (vendor-authored)

Daloopa publishes internal benchmarks related to agent accuracy when using MCP vs web-only retrieval.

Critical evaluation:

These benchmarks are vendor-produced and should be treated as vendor research.
They are useful to understand product direction but are not a substitute for independent validation.

Practical case study evidence found

No detailed, third-party case studies with independently verifiable outcomes were retrieved in the public sources examined. The vendor presents product videos, claims of broad institutional adoption, and some customer narratives, but public documentation in this research set does not contain extensive, independent ROI case studies. Recommendation for procurement: - Request several customer references in the buyer's target segment (e.g., hedge fund vs bank) - Run a pilot with defined accuracy and timeliness test plans across a representative sample of covered names

04Best fit

Company sizes

A best fit appears to be institutional investing and banking teams where many analysts maintain models and repeated earnings updates are common. Typical organization sizes may range from mid-market teams to large institutions.

Industries/verticals

Hedge funds, long-only asset managers, and private equity firms
Investment banks (equity research, investment banking coverage)
Corporate finance and strategy teams that perform peer benchmarking

Roles and personas

Equity research associates and analysts
Buy-side analysts and sector specialists
Data and engineering teams building internal analytics
Research platforms and quant/research teams deploying LLM-driven workflows

Pain points addressed

Manual extraction of KPIs and financial statement history from filings
Earnings-season update workload
Requirements for auditability and data lineage
Feeding structured fundamentals into LLM workflows with traceability

Qualification criteria (indicative of a strong fit)

Teams that work primarily in Excel and maintain multiple models across reporting cycles
Processes that depend on non-standard disclosures (KPIs, guidance, segment detail)
Need for a programmable data layer (API/Snowflake) combined with source lineage

05The pitch

What the product does (core value proposition)

Daloopa is a financial data and workflow platform that converts unstructured public-company disclosures (e.g., SEC filings, press releases, investor presentations, and some transcripts) into structured, source-linked fundamental datasets that can be consumed in Excel workflows, via API, and via cloud-native data delivery (e.g., Snowflake / Databricks). The company presents the product as a data layer intended to support auditability and to reduce manual data entry and LLM hallucinations by providing verifiable numbers with lineage to original documents. Key delivery formats/products highlighted by Daloopa: - Data Sheets (downloadable historical datasets for specific tickers) - Excel Add-in (one-click updates and model retrofit) - API (programmatic access; webhooks for updates) - Cloud integrations (native delivery into Snowflake/Databricks) - MCP (Model Context Protocol) server for LLM integrations (e.g., connector to third-party LLMs) - Scout (AI Excel agent; in beta)

Target market and positioning

Target customers include institutional finance (hedge funds, mutual funds, private equity, and investment banks), teams building internal analytics/agents that require a vetted fundamental data layer, and organizations integrating LLM/agent ecosystems that require data lineage and source linkage to support grounded outputs.

Coverage in press reporting and product materials indicates a focus on institutional finance and analytics teams.

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