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The AI-native research workspace

for investors

Hire teams of AI research analysts. Answer questions at scale

Request a demo

The AI-native research workspace for investors

Hire teams of AI research analysts

Answer questions at scale

Request demo

AI that gets the details right

AI that gets the details right

at every scale

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F

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Chevron

GM

Rivian

F

F

Tesla

Ford

Analyze the impact of changes to bonus depreciation in the Trump tax bill on Ford

Hormel

F

F

General Mills

Tyson

Chevron

F

F

F

F

F

F

F

F

F

F

F

F

Chevron

GM

Rivian

F

F

Tesla

Ford

Analyze the impact of changes to bonus depreciation in the Trump tax bill on Ford

Hormel

F

F

General Mills

Tyson

Chevron

F

F

F

F

F

F

F

F

F

F

F

F

Chevron

GM

Rivian

F

F

Tesla

Ford

Analyze the impact of changes to bonus depreciation in the Trump tax bill on Ford

Hormel

F

F

General Mills

Tyson

Chevron

F

F

F

F

F

F

Enabled by our unique approach to financial research

Enabled by our unique approach to financial research

Assistants live in a shared workspace, read documents, update tables, and use tools—like EDGAR search, and web search—exactly as you do.

Assistants live in a shared workspace, read documents, update tables, and use tools—like EDGAR search, and web search—exactly as you do.

Truly autonomous.

Best-in-class.

Truly autonomous.

Best-in-class.

Runs until the job is done — whether it's 10 minutes or 10 hours

Runs until the job is done — whether it's 10 minutes or 10 hours

Scores highest in accuracy for public company research (Vals AI)

Scores highest in accuracy for public company research (Vals AI)

56%

Perplexity

80%

Deep Research

92%

Village

Accuracy based on benchmark questions over SEC EDGAR and web (Vals AI, 2025)

Total transparency. For every assistant.

Total transparency,

for every assistant

Shared task logs that you can use to audit the research plan

Every doc created, SEC filing or earnings call read, and search made

A complete timeline of the assistant’s tabs, reasoning, and steps

Use cases

Read every document,

challenge every assumption,

scale your best ideas.

Read every document,

challenge every assumption,

scale your best ideas.

Track KPIs alongside commentary

Extract hard-to-find KPIs with context across an entire industry.

Understand management’s tone

Comprehensively understand the evolution of management’s thinking

Never miss a headline

Monitor every press release or news site for news material to your target companies.

Covenant analysis project

Lay out every debt basket with verbatim text and plain‑language interpretation in parallel columns.

Management tone over time

Track how leaders discuss growth drivers across 8 quarters; flag shifts in sentiment and guidance.

KPI table for theaters

Attendance, ATP, F&B per patron, U.S. box office revenue — by chain and quarter.

Disclosure comp for UBER/CART

Pull commentary across 12 quarters of filings and earnings materials; extract relevant passages.

100+ more use cases

Industry read-throughs

Debt covenant analysis

Supply chain impact analysis

Bottoms-up market sizing

Real work requires a structured approach

Real work requires a structured approach

Assistants create dozens of documents of research and bubble up the most important information to the user.

Have the confidence that every document was read and the ability to zoom into the details when you need more information.

UBER and CART Disclosures (Last 12 Quarters) — For DoorDash Analysis

Uber — Disclosures (Last 12 Quarters)

Uber — Delivery Profitability Disclosures (Q3'22–Q4'23)

Uber — Delivery Profitability Disclosures (Q1'24–Q2'25)

Uber — Grocery Profitability Disclosures (Q3'22–Q4'23)

Uber — Delivery Profitability Disclosures (Q1'24–Q2'25)

Uber — Grocery Profitability Disclosures (Q3'22–Q4'23)

Uber — Advertising Disclosures (Q1'24–Q2'25)

Uber — Definitions and Metrics (Q3'22–Q2'25)

Subtask: Instacart / Maplebear (CART) — Disclosures for DoorDash Analysis (Last 12 Quarters)

Instacart — Profitability Quotes (Q3'2022–Q2'2025)

Instacart — Ads (% of GTV/GOV) & Profitability (Q3'2022–Q2'2025)

Instacart — Grocery Delivery Economics (Q3'2022–Q2'2025)

Subtask: Instacart S-1/S-1(A) Unit Economics Extraction

Subtask: EDGAR Filings (10-Q/10-K/8-K PR) Q3’2022–Q2’2025

Subtask: IR Materials (Transcripts/Presentations) Q3’2022–Q2’2025

Instacart — Definitions & Metrics

Subtask: Instacart S-1/S-1/A — Extract KPI Definitions

Subtask: Instacart 10-K/10-Q (Q3’2023–Q2’2025) — Confirm/Track Definition Changes

Subtask: S-1/S-1A Baseline — CART KPI Definitions

Subtask: 2023 Filings (Q3’23 10-Q, FY2023 10-K) — KPI Definitions

Subtask: 2024 Filings (All 10-Qs + FY2024 10-K) — KPI Definitions

Subtask: DoorDash (DASH) — Definitions and Metric Basis for Comparison

UBER and CART Disclosures (Last 12 Quarters) — For DoorDash Analysis

Uber — Disclosures (Last 12 Quarters)

Uber — Delivery Profitability Disclosures (Q3'22–Q4'23)

Uber — Delivery Profitability Disclosures (Q1'24–Q2'25)

Uber — Grocery Profitability Disclosures (Q3'22–Q4'23)

Uber — Delivery Profitability Disclosures (Q1'24–Q2'25)

Uber — Grocery Profitability Disclosures (Q3'22–Q4'23)

Uber — Advertising Disclosures (Q1'24–Q2'25)

Uber — Definitions and Metrics (Q3'22–Q2'25)

Subtask: Instacart / Maplebear (CART) — Disclosures for DoorDash Analysis (Last 12 Quarters)

Instacart — Profitability Quotes (Q3'2022–Q2'2025)

Instacart — Ads (% of GTV/GOV) & Profitability (Q3'2022–Q2'2025)

Instacart — Grocery Delivery Economics (Q3'2022–Q2'2025)

Subtask: Instacart S-1/S-1(A) Unit Economics Extraction

Subtask: EDGAR Filings (10-Q/10-K/8-K PR) Q3’2022–Q2’2025

Subtask: IR Materials (Transcripts/Presentations) Q3’2022–Q2’2025

Instacart — Definitions & Metrics

Subtask: Instacart S-1/S-1/A — Extract KPI Definitions

Subtask: Instacart 10-K/10-Q (Q3’2023–Q2’2025) — Confirm/Track Definition Changes

Subtask: S-1/S-1A Baseline — CART KPI Definitions

Subtask: 2023 Filings (Q3’23 10-Q, FY2023 10-K) — KPI Definitions

Subtask: 2024 Filings (All 10-Qs + FY2024 10-K) — KPI Definitions

Subtask: DoorDash (DASH) — Definitions and Metric Basis for Comparison

What would you do with 1,000 research analysts?

What would you do with 1,000 research analysts?

Request a demo

Request a demo

Request a demo

Deeper, broader, more collaborative

Deeper, broader, more collaborative

VillageChatGPTClaude FinancePerplexity for FinanceAlphasense grid
Chat with the assistant✓✓✓✓×
Dedicated EDGAR integration✓××✓✓
Earnings calls✓×✓×✓
Build on prior work✓LimitedLimited××
Granular citations✓××××
Ability to work at scale✓×××Limited
Transparent reasoning✓××××
Lets you give feedback at scale✓××××
Collaborate with users on artifacts✓××××
Vals AI performance96%82%—56%—
Context windowFunctionally unlimited128K200K32K—
Village
  • Chat with the assistant
    ✓
  • Dedicated EDGAR integration
    ✓
  • Earnings calls
    ✓
  • Build on prior work
    ✓
  • Granular citations
    ✓
  • Ability to work at scale
    ✓
  • Transparent reasoning
    ✓
  • Lets you give feedback at scale
    ✓
  • Collaborate with users on artifacts
    ✓
  • Vals AI performance
    96%
  • Context window
    Functionally unlimited
ChatGPT
  • Chat with the assistant
    ✓
  • Dedicated EDGAR integration
    ×
  • Earnings calls
    ×
  • Build on prior work
    Limited
  • Granular citations
    ×
  • Ability to work at scale
    ×
  • Transparent reasoning
    ×
  • Lets you give feedback at scale
    ×
  • Collaborate with users on artifacts
    ×
  • Vals AI performance
    82%
  • Context window
    128K
Claude Finance
  • Chat with the assistant
    ✓
  • Dedicated EDGAR integration
    ×
  • Earnings calls
    ✓
  • Build on prior work
    Limited
  • Granular citations
    ×
  • Ability to work at scale
    ×
  • Transparent reasoning
    ×
  • Lets you give feedback at scale
    ×
  • Collaborate with users on artifacts
    ×
  • Vals AI performance
    —
  • Context window
    200K
Perplexity for Finance
  • Chat with the assistant
    ✓
  • Dedicated EDGAR integration
    ✓
  • Earnings calls
    ×
  • Build on prior work
    ×
  • Granular citations
    ×
  • Ability to work at scale
    ×
  • Transparent reasoning
    ×
  • Lets you give feedback at scale
    ×
  • Collaborate with users on artifacts
    ×
  • Vals AI performance
    56%
  • Context window
    32K
Alphasense grid
  • Chat with the assistant
    ×
  • Dedicated EDGAR integration
    ✓
  • Earnings calls
    ✓
  • Build on prior work
    ×
  • Granular citations
    ×
  • Ability to work at scale
    Limited
  • Transparent reasoning
    ×
  • Lets you give feedback at scale
    ×
  • Collaborate with users on artifacts
    ×
  • Vals AI performance
    —
  • Context window
    —

Case Study: Tracking Telecom Earnings

Case Study: Tracking Telecom Earnings

400+ rows of commentary in 1 hour

400+ rows of commentary in 1 hour

Follow passage level citations for every excerpt, sort and filter the output table by company or commentary type, and remix the assistant’s research

Follow passage level citations for every excerpt, sort and filter the output table by company or commentary type, and remix the assistant’s research

50+ assistants working together

50+ assistants working together

10 hours of research in < 1 hour

10 hours of research in < 1 hour

The confidence that you’ve covered all of your bases

The confidence that you’ve covered all of your bases

Set up an earnings tracker table for major telecom companies: AT&T, VZ, TMUS, CHTR, CMCSA. Go through the historical earnings calls and get any comments about the following topics: net adds, fiber, fixed wireless, convergence, operating costs, capex, and capital allocation.

Set up an earnings tracker table for major telecom companies: AT&T, VZ, TMUS, CHTR, CMCSA. Go through the historical earnings calls and get any comments about the following topics: net adds, fiber, fixed wireless, convergence, operating costs, capex, and capital allocation.

Explore what unbounded intelligence can do for your firm

Explore what unbounded intelligence can do for your firm

Request a demo

Request a demo

Request a demo

Village

Contact us

Contact us

tae@village.dev

tae@village.dev

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