sieve

Name: sieve
Brand: sieve

AI Native

A service that allows you to store all your bookmarks, notes, movies and images in one place.

Est 2025|Raised $500K

LinkedIn 684|Web traffic 2K|FTEs 1

MarketsVerified Financial Database

Published Jun 25, 2026

01Common complaints

- You require a full financial data terminal with broad standardized datasets, identifiers, screening, news, and charting functions; terminals and large data vendors are more appropriate. - You require publicly documented security certifications (SOC 2, ISO) before beginning an evaluation; usesieve.com did not present public security collateral in the sources accessed for this report. - You need transparent self-serve pricing to enable rapid procurement; this vendor appears to use contact-led, quote-based pricing. - Your organization prefers a DIY internal platform with broad connectors and orchestration under internal control; data-prep platforms or a modern data stack may be better aligned. - Your Microsoft 365 tenant policy blocks Office add-ins or makes add-in rollout difficult at scale.

Deal disqualifiers

Organizations requiring publicly documented security certifications and penetration-test reports prior to consideration
Teams that need full terminal capabilities (real-time feeds, broad datasets, screening, news, charting)
Teams that have standardized on terminals like CapIQ/FactSet/Bloomberg and only need minor data transformations

Vendor-acknowledged limitations

No explicit limitations or product roadmap documentation were located in the public materials reviewed.

Practical limitations inferred from available information

Limited public transparency for pricing, security/compliance, and customer references.
Dependence on Excel add-in distribution channels which can be constrained by Microsoft 365 tenant policies and add-in governance.

Why customers might leave (inferred)

Cost relative to building internal capabilities or leveraging existing data terminals.
Data coverage gaps for niche sources or edge-case extractions.
Turnaround times may not meet time-sensitive trading or investment workflows if human review introduces latency.
Security or compliance requirements that the vendor cannot meet or document to satisfaction.

02Why they buy

Claimed differentiators

The vendor presents a combined automated extraction plus human review model and delivers outputs directly into Excel and via an API, which is positioned to reduce the manual steps analysts take when preparing data.
Dual interface (Excel add-in and API) supports both analyst workflows and programmatic consumption.

Where evidence is thin

Public, third-party validation of accuracy, turnaround times, and realized ROI is limited or not publicly available.

03Field reports

Reality check: limited third-party review coverage for usesieve.com

Public review platforms did not surface a dedicated product profile or a set of independent user reviews for usesieve.com during this research session. Multiple search results for the term "Sieve" refer to unrelated products. Available third-party signals were primarily vendor or partner listings and founder communications rather than independent customer reviews. These signals include accelerator/company listings and a marketplace listing for an Excel add-in; they do not substitute for third-party review coverage.

Positive signals (vendor/partner listings and announcements)

- Accelerator/company program listing describing the company as addressing data cleaning for hedge funds and investment firms. - Marketplace listing for an Excel add-in that describes the product as delivering cleaned, human-reviewed data into spreadsheets. - A founder announcement describing launch of an Excel plug-in integrated with an API to deliver cleaned and reviewed data into Excel models. These are vendor-side signals and should be treated as such rather than independent customer validation.

Negative/critical signals (limitations in available evidence)

No dedicated review pages on major review platforms or a corpus of independent user feedback were located during this research.
Potential deployment friction: enterprise Microsoft 365 policies can restrict add-in installation and execution, which can complicate rollout of Excel add-ins in larger organizations.
General marketplace review-site reliability concerns apply when evaluating vendors that later present badges or ratings from these platforms.

Why customers might choose this product (inferred)

Excel-integrated delivery reduces the need for analysts to switch tools during modeling.
The vendor's model combines automated extraction with human review to validate high-value data points.
Outsourcing extraction and cleaning can be faster to deploy for some teams compared with building bespoke parsing pipelines.

Case Studies

No public case studies with named customers and quantified ROI were found for Sieve in the sources accessed for this report.

Critical note: where vendor-provided ROI figures are presented in sales discussions, treat them as vendor estimates until validated with customer references and a documented methodology.

04Best fit

Company size

Inferred fit:

Small-to-mid investment firms and hedge funds (small to mid-sized organizations) where Excel remains a core modeling tool and in-house data-engineering resources are limited.
Larger funds could use the product for specific operational bottlenecks, but vendor risk and integration demands will typically be higher in those organizations.

Industries

Hedge funds
Asset managers
Investment research teams
Potential adjacent use: corporate finance and corporate development teams with heavy Excel workflows

Roles/personas

Decision makers: Head of Research, COO/Head of Ops, Head of Data, CTO/CISO (vendor risk and procurement)
End users: investment analysts, research associates, quantitative researchers, operations analysts

Pain points addressed

Manual cleaning and reconciliation of inconsistent fields (dates, metrics, entity names)
Need for validated data points to reduce the risk of errors in financial analysis
Desire to keep workflows within Excel to limit context switching for analysts

05The pitch

What the product does (core value proposition)

Sieve (vendor product pages) is a data-cleaning and data-extraction service targeted at financial workflows. It offers an API and an Excel add-in that deliver cleaned data into spreadsheet models for analyst use. The vendor describes its output as data that has been validated by a combination of automated models and human review. A differentiating design element relative to purely software-based data-preparation tools is the use of an automated process combined with human review (human-in-the-loop verification). The vendor states this approach is intended to reduce the time analysts spend resolving messy, inconsistent, or unstructured source data.

Target market and positioning

Primary target: hedge funds and investment firms, specifically trading and investment teams that use Excel-heavy workflows.
Positioning: intended to reduce manual cleaning and one-off scripting by delivering cleaned outputs into existing Excel workflows rather than requiring a separate data engineering stack.

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

Download full report →

Published Jun 25, 2026

01Common complaints

You require a full financial data terminal with broad standardized datasets, identifiers, screening, news, and charting functions; terminals and large data vendors are more appropriate.
You require publicly documented security certifications (SOC 2, ISO) before beginning an evaluation; usesieve.com did not present public security collateral in the sources accessed for this report.
You need transparent self-serve pricing to enable rapid procurement; this vendor appears to use contact-led, quote-based pricing.
Your organization prefers a DIY internal platform with broad connectors and orchestration under internal control; data-prep platforms or a modern data stack may be better aligned.
Your Microsoft 365 tenant policy blocks Office add-ins or makes add-in rollout difficult at scale.

Deal disqualifiers

Organizations requiring publicly documented security certifications and penetration-test reports prior to consideration
Teams that need full terminal capabilities (real-time feeds, broad datasets, screening, news, charting)
Teams that have standardized on terminals like CapIQ/FactSet/Bloomberg and only need minor data transformations

Vendor-acknowledged limitations

No explicit limitations or product roadmap documentation were located in the public materials reviewed.

Practical limitations inferred from available information

Limited public transparency for pricing, security/compliance, and customer references.
Dependence on Excel add-in distribution channels which can be constrained by Microsoft 365 tenant policies and add-in governance.

Why customers might leave (inferred)

Cost relative to building internal capabilities or leveraging existing data terminals.
Data coverage gaps for niche sources or edge-case extractions.
Turnaround times may not meet time-sensitive trading or investment workflows if human review introduces latency.
Security or compliance requirements that the vendor cannot meet or document to satisfaction.

02Why they buy

Claimed differentiators

The vendor presents a combined automated extraction plus human review model and delivers outputs directly into Excel and via an API, which is positioned to reduce the manual steps analysts take when preparing data.
Dual interface (Excel add-in and API) supports both analyst workflows and programmatic consumption.

Where evidence is thin

Public, third-party validation of accuracy, turnaround times, and realized ROI is limited or not publicly available.

03Field reports

Reality check: limited third-party review coverage for usesieve.com

Available third-party signals were primarily vendor or partner listings and founder communications rather than independent customer reviews. These signals include accelerator/company listings and a marketplace listing for an Excel add-in; they do not substitute for third-party review coverage.

Positive signals (vendor/partner listings and announcements)

Accelerator/company program listing describing the company as addressing data cleaning for hedge funds and investment firms.
Marketplace listing for an Excel add-in that describes the product as delivering cleaned, human-reviewed data into spreadsheets.
A founder announcement describing launch of an Excel plug-in integrated with an API to deliver cleaned and reviewed data into Excel models.

These are vendor-side signals and should be treated as such rather than independent customer validation.

Negative/critical signals (limitations in available evidence)

No dedicated review pages on major review platforms or a corpus of independent user feedback were located during this research.
Potential deployment friction: enterprise Microsoft 365 policies can restrict add-in installation and execution, which can complicate rollout of Excel add-ins in larger organizations.
General marketplace review-site reliability concerns apply when evaluating vendors that later present badges or ratings from these platforms.

Why customers might choose this product (inferred)

Excel-integrated delivery reduces the need for analysts to switch tools during modeling.
The vendor's model combines automated extraction with human review to validate high-value data points.
Outsourcing extraction and cleaning can be faster to deploy for some teams compared with building bespoke parsing pipelines.

Case Studies

No public case studies with named customers and quantified ROI were found for Sieve in the sources accessed for this report.

Critical note: where vendor-provided ROI figures are presented in sales discussions, treat them as vendor estimates until validated with customer references and a documented methodology.

04Best fit

Company size

Inferred fit:

Small-to-mid investment firms and hedge funds (small to mid-sized organizations) where Excel remains a core modeling tool and in-house data-engineering resources are limited.
Larger funds could use the product for specific operational bottlenecks, but vendor risk and integration demands will typically be higher in those organizations.

Industries

Hedge funds
Asset managers
Investment research teams
Potential adjacent use: corporate finance and corporate development teams with heavy Excel workflows

Roles/personas

Decision makers: Head of Research, COO/Head of Ops, Head of Data, CTO/CISO (vendor risk and procurement)
End users: investment analysts, research associates, quantitative researchers, operations analysts

Pain points addressed

Manual cleaning and reconciliation of inconsistent fields (dates, metrics, entity names)
Need for validated data points to reduce the risk of errors in financial analysis
Desire to keep workflows within Excel to limit context switching for analysts

05The pitch

What the product does (core value proposition)

A differentiating design element relative to purely software-based data-preparation tools is the use of an automated process combined with human review (human-in-the-loop verification). The vendor states this approach is intended to reduce the time analysts spend resolving messy, inconsistent, or unstructured source data.

Target market and positioning

Primary target: hedge funds and investment firms, specifically trading and investment teams that use Excel-heavy workflows.
Positioning: intended to reduce manual cleaning and one-off scripting by delivering cleaned outputs into existing Excel workflows rather than requiring a separate data engineering stack.

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

Download full report →