90% less data prep time

AI Market Data Integration Agent

From fragmented sources to unified intelligence

Stop manually aggregating market data from disparate sources. This specialized agent connects to your data feeds, normalizes inconsistent formats, reconciles conflicting values, and delivers a single source of truth—ready for analysis, modeling, and decision-making.

Ideal for

Investment Research

Quantitative Analysis

Portfolio Management

  • Mercedes-Benz logo
    SMC  logo
    Mercedes-Benz logo
    Centerline logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Alaris logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Foobar logo
    ABL logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Brotherhood Mutual logo
    Mercedes-Benz logo
    Paige logo
    Roche logo
    Sony logo
    Munch Energie Logo
    Certainty Sofrware logo
    Raft logo
    Bayer Logo
    Mercedes-Benz logo
    Mercedes-Benz logo

See AI Market Data Integration Agent in action

Play video

  • Mercedes-Benz logo
    SMC  logo
    Mercedes-Benz logo
    Centerline logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Alaris logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Foobar logo
    ABL logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Brotherhood Mutual logo
    Mercedes-Benz logo
    Paige logo
    Roche logo
    Sony logo
    Munch Energie Logo
    Certainty Sofrware logo
    Raft logo
    Bayer Logo
    Mercedes-Benz logo
    Mercedes-Benz logo

See AI Market Data Integration Agent in action

Play video

  • Mercedes-Benz logo
    SMC  logo
    Mercedes-Benz logo
    Centerline logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Alaris logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Foobar logo
    ABL logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Brotherhood Mutual logo
    Mercedes-Benz logo
    Paige logo
    Roche logo
    Sony logo
    Munch Energie Logo
    Certainty Sofrware logo
    Raft logo
    Bayer Logo
    Mercedes-Benz logo
    Mercedes-Benz logo

See AI Market Data Integration Agent in action

Play video

Q3 Market Data Pipeline

  • Mercedes-Benz logo
    SMC  logo
    Mercedes-Benz logo
    Centerline logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Alaris logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Foobar logo
    ABL logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Mercedes-Benz logo
    Brotherhood Mutual logo
    Mercedes-Benz logo
    Paige logo
    Roche logo
    Sony logo
    Munch Energie Logo
    Certainty Sofrware logo
    Raft logo
    Bayer Logo
    Mercedes-Benz logo
    Mercedes-Benz logo

See AI Market Data Integration Agent in action

Play video

Time comparison

Time comparison

Traditional way

6-8 hours per week

With V7 Go agents

30-45 minutes

Average time saved

90%

Why V7 Go

Why V7 Go

Multi-Source Data Aggregation

Connects to Bloomberg, Reuters, SEC filings, proprietary feeds, and custom data sources simultaneously. Pulls all relevant market data in a single operation, eliminating manual copy-paste workflows.

Multi-Source Data Aggregation

Connects to Bloomberg, Reuters, SEC filings, proprietary feeds, and custom data sources simultaneously. Pulls all relevant market data in a single operation, eliminating manual copy-paste workflows.

Multi-Source Data Aggregation

Connects to Bloomberg, Reuters, SEC filings, proprietary feeds, and custom data sources simultaneously. Pulls all relevant market data in a single operation, eliminating manual copy-paste workflows.

Multi-Source Data Aggregation

Connects to Bloomberg, Reuters, SEC filings, proprietary feeds, and custom data sources simultaneously. Pulls all relevant market data in a single operation, eliminating manual copy-paste workflows.

Intelligent Format Normalization

Automatically detects and converts disparate data formats—CSV, JSON, XML, Excel, API responses—into a unified schema. Handles unit conversions, date standardization, and currency normalization without manual intervention.

Intelligent Format Normalization

Automatically detects and converts disparate data formats—CSV, JSON, XML, Excel, API responses—into a unified schema. Handles unit conversions, date standardization, and currency normalization without manual intervention.

Intelligent Format Normalization

Automatically detects and converts disparate data formats—CSV, JSON, XML, Excel, API responses—into a unified schema. Handles unit conversions, date standardization, and currency normalization without manual intervention.

Intelligent Format Normalization

Automatically detects and converts disparate data formats—CSV, JSON, XML, Excel, API responses—into a unified schema. Handles unit conversions, date standardization, and currency normalization without manual intervention.

Conflict Resolution & Reconciliation

When the same data point appears in multiple sources with different values, the agent identifies the discrepancy, applies your reconciliation rules, and flags outliers for review.

Conflict Resolution & Reconciliation

When the same data point appears in multiple sources with different values, the agent identifies the discrepancy, applies your reconciliation rules, and flags outliers for review.

Conflict Resolution & Reconciliation

When the same data point appears in multiple sources with different values, the agent identifies the discrepancy, applies your reconciliation rules, and flags outliers for review.

Conflict Resolution & Reconciliation

When the same data point appears in multiple sources with different values, the agent identifies the discrepancy, applies your reconciliation rules, and flags outliers for review.

Real-Time Data Freshness Monitoring

Tracks update timestamps across all sources and alerts you when data becomes stale. Ensures your analysis is always based on the most current information available.

Real-Time Data Freshness Monitoring

Tracks update timestamps across all sources and alerts you when data becomes stale. Ensures your analysis is always based on the most current information available.

Real-Time Data Freshness Monitoring

Tracks update timestamps across all sources and alerts you when data becomes stale. Ensures your analysis is always based on the most current information available.

Real-Time Data Freshness Monitoring

Tracks update timestamps across all sources and alerts you when data becomes stale. Ensures your analysis is always based on the most current information available.

Structured Output for Immediate Use

Delivers aggregated data in your preferred format—Excel, CSV, JSON, or direct database ingestion. Ready to feed into models, dashboards, and analysis tools without additional transformation.

Structured Output for Immediate Use

Delivers aggregated data in your preferred format—Excel, CSV, JSON, or direct database ingestion. Ready to feed into models, dashboards, and analysis tools without additional transformation.

Structured Output for Immediate Use

Delivers aggregated data in your preferred format—Excel, CSV, JSON, or direct database ingestion. Ready to feed into models, dashboards, and analysis tools without additional transformation.

Structured Output for Immediate Use

Delivers aggregated data in your preferred format—Excel, CSV, JSON, or direct database ingestion. Ready to feed into models, dashboards, and analysis tools without additional transformation.

Audit Trail & Data Lineage

Every data point is tagged with its source, timestamp, and any transformations applied. Complete transparency into where your numbers came from and how they were processed.

Audit Trail & Data Lineage

Every data point is tagged with its source, timestamp, and any transformations applied. Complete transparency into where your numbers came from and how they were processed.

Audit Trail & Data Lineage

Every data point is tagged with its source, timestamp, and any transformations applied. Complete transparency into where your numbers came from and how they were processed.

Audit Trail & Data Lineage

Every data point is tagged with its source, timestamp, and any transformations applied. Complete transparency into where your numbers came from and how they were processed.

Aggregates data from any source

And delivers unified, analysis-ready datasets.

Get started

Get started

Import your files

Snowflake

,

Google Cloud

,

Tableau

Import your files from whereever they are currently stored

Customer voices

Customer voices

Unify your market intelligence.

Unify your market intelligence.

Stop wasting time on data plumbing.

Stop wasting time on data plumbing.

Finance

Legal

Insurance

Tax

Real Estate

Finance

Legal

Insurance

Tax

Real Estate

Finance

Legal

Insurance

Tax

Real Estate

Customer Voices

Features

Features

Results you can actually trust.
Reliable AI document processing toolkit.

Results you can trust.
Trustworthy AI document processing toolkit.

Supporting any data format.

From any source.

Market data arrives in countless formats from countless sources. This agent handles CSV, JSON, XML, Excel, API responses, and proprietary formats. It normalizes everything into your preferred schema without requiring manual configuration for each new source.

Input types

CSV & Excel

JSON & XML

API Responses

Custom Formats

Document types

Spreadsheets

APIs

Databases

Web Feeds

Text Files

Vendor_US.xlsx

3

Supply_2023.pptx

Review_Legal.pdf

Supporting any data format.

From any source.

Market data arrives in countless formats from countless sources. This agent handles CSV, JSON, XML, Excel, API responses, and proprietary formats. It normalizes everything into your preferred schema without requiring manual configuration for each new source.

Input types

CSV & Excel

JSON & XML

API Responses

Custom Formats

Document types

Spreadsheets

APIs

Databases

Web Feeds

Text Files

Vendor_US.xlsx

3

Supply_2023.pptx

Review_Legal.pdf

Supporting any data format.

From any source.

Market data arrives in countless formats from countless sources. This agent handles CSV, JSON, XML, Excel, API responses, and proprietary formats. It normalizes everything into your preferred schema without requiring manual configuration for each new source.

Input types

CSV & Excel

JSON & XML

API Responses

Custom Formats

Document types

Spreadsheets

APIs

Databases

Web Feeds

Text Files

Vendor_US.xlsx

3

Supply_2023.pptx

Review_Legal.pdf

Deterministic transformations

ensure perfect consistency.

Data normalization isn't guesswork—it's rule-based transformation. The agent applies your reconciliation logic consistently across millions of data points. Every conversion is auditable and reproducible, eliminating the data quality issues that plague manual aggregation.

Model providers

Security note

V7 never trains models on your private data. We keep your data encrypted and allow you to deploy your own models.

Answer

Type

Text

Tool

o4 Mini

Reasoning effort

Min

Low

Mid

High

AI Citations

Inputs

Set a prompt (Press @ to mention an input)

Deterministic transformations

ensure perfect consistency.

Data normalization isn't guesswork—it's rule-based transformation. The agent applies your reconciliation logic consistently across millions of data points. Every conversion is auditable and reproducible, eliminating the data quality issues that plague manual aggregation.

Model providers

Security note

V7 never trains models on your private data. We keep your data encrypted and allow you to deploy your own models.

Answer

Type

Text

Tool

o4 Mini

Reasoning effort

Min

Low

Mid

High

AI Citations

Inputs

Set a prompt (Press @ to mention an input)

Deterministic transformations

ensure perfect consistency.

Data normalization isn't guesswork—it's rule-based transformation. The agent applies your reconciliation logic consistently across millions of data points. Every conversion is auditable and reproducible, eliminating the data quality issues that plague manual aggregation.

Model providers

Security note

V7 never trains models on your private data. We keep your data encrypted and allow you to deploy your own models.

Answer

Type

Text

Tool

o4 Mini

Reasoning effort

Min

Low

Mid

High

AI Citations

Inputs

Set a prompt (Press @ to mention an input)

Complete data lineage

for every data point.

Know exactly where every number came from. The agent tags each data point with its source, timestamp, and transformation history. When a number doesn't match expectations, you can trace it back to its origin instantly.

Visual grounding in action

00:54

Deliberate Misrepresentation: During the trial, evidence was presented showing that John Doe deliberately misrepresented his income on multiple occasions over several years. This included falsifying documents, underreporting income, and inflating deductions to lower his tax liability. Such deliberate deception demonstrates intent to evade taxes.

Pattern of Behavior: The prosecution demonstrated a consistent pattern of behavior by John Doe, spanning several years, wherein he consistently failed to report substantial portions of his income. This pattern suggested a systematic attempt to evade taxes rather than mere oversight or misunderstanding.

Concealment of Assets: Forensic accounting revealed that John Doe had taken significant steps to conceal his assets offshore, including setting up shell companies and using complex financial structures to hide income from tax authorities. Such elaborate schemes indicate a deliberate effort to evade taxes and avoid detection.

Failure to Cooperate: Throughout the investigation and trial, John Doe displayed a lack of cooperation with tax authorities. He refused to provide requested documentation, obstructed the audit process, and failed to disclose relevant financial information. This obstructionism further supported the prosecution's argument of intentional tax evasion.

Prior Warning and Ignoring Compliance

02

01

01

02

Complete data lineage

for every data point.

Know exactly where every number came from. The agent tags each data point with its source, timestamp, and transformation history. When a number doesn't match expectations, you can trace it back to its origin instantly.

Visual grounding in action

00:54

Deliberate Misrepresentation: During the trial, evidence was presented showing that John Doe deliberately misrepresented his income on multiple occasions over several years. This included falsifying documents, underreporting income, and inflating deductions to lower his tax liability. Such deliberate deception demonstrates intent to evade taxes.

Pattern of Behavior: The prosecution demonstrated a consistent pattern of behavior by John Doe, spanning several years, wherein he consistently failed to report substantial portions of his income. This pattern suggested a systematic attempt to evade taxes rather than mere oversight or misunderstanding.

Concealment of Assets: Forensic accounting revealed that John Doe had taken significant steps to conceal his assets offshore, including setting up shell companies and using complex financial structures to hide income from tax authorities. Such elaborate schemes indicate a deliberate effort to evade taxes and avoid detection.

Failure to Cooperate: Throughout the investigation and trial, John Doe displayed a lack of cooperation with tax authorities. He refused to provide requested documentation, obstructed the audit process, and failed to disclose relevant financial information. This obstructionism further supported the prosecution's argument of intentional tax evasion.

Prior Warning and Ignoring Compliance

02

01

01

02

Complete data lineage

for every data point.

Know exactly where every number came from. The agent tags each data point with its source, timestamp, and transformation history. When a number doesn't match expectations, you can trace it back to its origin instantly.

Visual grounding in action

00:54

Deliberate Misrepresentation: During the trial, evidence was presented showing that John Doe deliberately misrepresented his income on multiple occasions over several years. This included falsifying documents, underreporting income, and inflating deductions to lower his tax liability. Such deliberate deception demonstrates intent to evade taxes.

Pattern of Behavior: The prosecution demonstrated a consistent pattern of behavior by John Doe, spanning several years, wherein he consistently failed to report substantial portions of his income. This pattern suggested a systematic attempt to evade taxes rather than mere oversight or misunderstanding.

Concealment of Assets: Forensic accounting revealed that John Doe had taken significant steps to conceal his assets offshore, including setting up shell companies and using complex financial structures to hide income from tax authorities. Such elaborate schemes indicate a deliberate effort to evade taxes and avoid detection.

Failure to Cooperate: Throughout the investigation and trial, John Doe displayed a lack of cooperation with tax authorities. He refused to provide requested documentation, obstructed the audit process, and failed to disclose relevant financial information. This obstructionism further supported the prosecution's argument of intentional tax evasion.

Prior Warning and Ignoring Compliance

02

01

01

02

Enterprise-grade security

for sensitive market data.

Market data is proprietary and sensitive. V7 Go processes all data within your secure environment, integrating with your existing data governance policies. Data is never shared or used for external purposes.

Certifications

GDPR

SOC2

HIPAA

ISO

Safety

Custom storage

Data governance

Access-level permissions

Enterprise-grade security

for sensitive market data.

Market data is proprietary and sensitive. V7 Go processes all data within your secure environment, integrating with your existing data governance policies. Data is never shared or used for external purposes.

Certifications

GDPR

SOC2

HIPAA

ISO

Safety

Custom storage

Data governance

Access-level permissions

Enterprise-grade security

for sensitive market data.

Market data is proprietary and sensitive. V7 Go processes all data within your secure environment, integrating with your existing data governance policies. Data is never shared or used for external purposes.

Certifications

GPDR

SOC2

HIPAA

ISO

Safety

Custom storage

Data governance

Access-level permissions

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Answers

Answers

What you need to know about our

AI Market Data Integration Agent

Which data sources can the agent connect to?

The agent supports major financial data providers like Bloomberg, Reuters, Yahoo Finance, and SEC Edgar, as well as custom APIs, databases, and file uploads. If you have a specific data source, we can configure a connector for it.

+

Which data sources can the agent connect to?

The agent supports major financial data providers like Bloomberg, Reuters, Yahoo Finance, and SEC Edgar, as well as custom APIs, databases, and file uploads. If you have a specific data source, we can configure a connector for it.

+

Which data sources can the agent connect to?

The agent supports major financial data providers like Bloomberg, Reuters, Yahoo Finance, and SEC Edgar, as well as custom APIs, databases, and file uploads. If you have a specific data source, we can configure a connector for it.

+

How does it handle conflicting data from different sources?

You define reconciliation rules based on your firm's methodology—whether that's using the most recent data, averaging values, or applying a weighted hierarchy. The agent applies these rules consistently and flags any unresolved conflicts for manual review.

+

How does it handle conflicting data from different sources?

You define reconciliation rules based on your firm's methodology—whether that's using the most recent data, averaging values, or applying a weighted hierarchy. The agent applies these rules consistently and flags any unresolved conflicts for manual review.

+

How does it handle conflicting data from different sources?

You define reconciliation rules based on your firm's methodology—whether that's using the most recent data, averaging values, or applying a weighted hierarchy. The agent applies these rules consistently and flags any unresolved conflicts for manual review.

+

Can it work with real-time data feeds?

Yes. The agent can be scheduled to run continuously or on-demand, pulling the latest data from your feeds and updating your aggregated dataset. It's ideal for intraday trading, portfolio monitoring, and live market analysis.

+

Can it work with real-time data feeds?

Yes. The agent can be scheduled to run continuously or on-demand, pulling the latest data from your feeds and updating your aggregated dataset. It's ideal for intraday trading, portfolio monitoring, and live market analysis.

+

Can it work with real-time data feeds?

Yes. The agent can be scheduled to run continuously or on-demand, pulling the latest data from your feeds and updating your aggregated dataset. It's ideal for intraday trading, portfolio monitoring, and live market analysis.

+

What happens if a data source goes down or becomes unavailable?

The agent detects missing sources and alerts you immediately. You can configure fallback sources or use the last known good data while the primary source is restored. No silent failures.

+

What happens if a data source goes down or becomes unavailable?

The agent detects missing sources and alerts you immediately. You can configure fallback sources or use the last known good data while the primary source is restored. No silent failures.

+

What happens if a data source goes down or becomes unavailable?

The agent detects missing sources and alerts you immediately. You can configure fallback sources or use the last known good data while the primary source is restored. No silent failures.

+

How accurate is the data normalization?

The agent uses deterministic transformation rules you define, so accuracy is 100% for format conversion. For reconciliation of conflicting values, accuracy depends on your rules—the agent applies them consistently and flags edge cases for review.

+

How accurate is the data normalization?

The agent uses deterministic transformation rules you define, so accuracy is 100% for format conversion. For reconciliation of conflicting values, accuracy depends on your rules—the agent applies them consistently and flags edge cases for review.

+

How accurate is the data normalization?

The agent uses deterministic transformation rules you define, so accuracy is 100% for format conversion. For reconciliation of conflicting values, accuracy depends on your rules—the agent applies them consistently and flags edge cases for review.

+

Can we integrate this with our existing data warehouse?

Absolutely. The agent can write directly to your data warehouse, update your BI tools, or export to your preferred format. It integrates seamlessly with Snowflake, BigQuery, Redshift, and standard databases.

+

Can we integrate this with our existing data warehouse?

Absolutely. The agent can write directly to your data warehouse, update your BI tools, or export to your preferred format. It integrates seamlessly with Snowflake, BigQuery, Redshift, and standard databases.

+

Can we integrate this with our existing data warehouse?

Absolutely. The agent can write directly to your data warehouse, update your BI tools, or export to your preferred format. It integrates seamlessly with Snowflake, BigQuery, Redshift, and standard databases.

+

Next steps

Next steps

Still manually aggregating market data from multiple sources?

Send us your data sources and we'll show you how to unify them into a single, analysis-ready dataset in minutes.

Uncover hidden liabilities

in

supplier contracts.

V7 Go transforms documents into strategic assets. 150+ enterprises are already on board:

  • Mercedes-Benz logo
    SMC  logo
    Centerline logo
    Alaris logo

Uncover hidden liabilities

in

supplier contracts.

V7 Go transforms documents into strategic assets. 150+ enterprises are already on board:

  • Mercedes-Benz logo
    SMC  logo
    Centerline logo
    Alaris logo