90% faster data intake
AI agent for Rating Agency Analysts
From data chaos to credit clarity
Delegate the tedious work of issuer data intake to a specialized AI agent. It extracts financial metrics from models, parses management presentations, normalizes data across inconsistent formats, and validates completeness against your rating methodology requirements, delivering a standardized analytical package ready for credit assessment.

Ideal for
Credit Rating Agencies
Credit Research Teams
Rating Analysts

See AI agent for Rating Agency Analysts in action
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See AI agent for Rating Agency Analysts in action
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Issuer Rating Review - TechCorp
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Time comparison
Time comparison
Traditional way
2-3 days per issuer
With V7 Go agents
2-3 hours
Average time saved
90%
Why V7 Go
Why V7 Go
Financial Model Extraction
Automatically extracts key financial metrics from issuer-provided Excel models, including revenue projections, EBITDA calculations, debt schedules, and covenant compliance ratios, regardless of model structure or formatting.
Financial Model Extraction
Automatically extracts key financial metrics from issuer-provided Excel models, including revenue projections, EBITDA calculations, debt schedules, and covenant compliance ratios, regardless of model structure or formatting.
Financial Model Extraction
Automatically extracts key financial metrics from issuer-provided Excel models, including revenue projections, EBITDA calculations, debt schedules, and covenant compliance ratios, regardless of model structure or formatting.
Financial Model Extraction
Automatically extracts key financial metrics from issuer-provided Excel models, including revenue projections, EBITDA calculations, debt schedules, and covenant compliance ratios, regardless of model structure or formatting.
Management Presentation Analysis
Parses management presentations to extract strategic initiatives, market positioning, competitive dynamics, and forward-looking statements that inform qualitative credit assessment factors.
Management Presentation Analysis
Parses management presentations to extract strategic initiatives, market positioning, competitive dynamics, and forward-looking statements that inform qualitative credit assessment factors.
Management Presentation Analysis
Parses management presentations to extract strategic initiatives, market positioning, competitive dynamics, and forward-looking statements that inform qualitative credit assessment factors.
Management Presentation Analysis
Parses management presentations to extract strategic initiatives, market positioning, competitive dynamics, and forward-looking statements that inform qualitative credit assessment factors.
Data Normalization Across Issuers
Standardizes financial data from diverse issuer formats into your agency's consistent analytical framework, enabling direct comparability across rated entities and sectors.
Data Normalization Across Issuers
Standardizes financial data from diverse issuer formats into your agency's consistent analytical framework, enabling direct comparability across rated entities and sectors.
Data Normalization Across Issuers
Standardizes financial data from diverse issuer formats into your agency's consistent analytical framework, enabling direct comparability across rated entities and sectors.
Data Normalization Across Issuers
Standardizes financial data from diverse issuer formats into your agency's consistent analytical framework, enabling direct comparability across rated entities and sectors.
Methodology Compliance Validation
Validates that all required data points for your rating methodology are present in the submission, flagging missing information or inconsistencies that require issuer follow-up before analysis can proceed.
Methodology Compliance Validation
Validates that all required data points for your rating methodology are present in the submission, flagging missing information or inconsistencies that require issuer follow-up before analysis can proceed.
Methodology Compliance Validation
Validates that all required data points for your rating methodology are present in the submission, flagging missing information or inconsistencies that require issuer follow-up before analysis can proceed.
Methodology Compliance Validation
Validates that all required data points for your rating methodology are present in the submission, flagging missing information or inconsistencies that require issuer follow-up before analysis can proceed.
Historical Data Integration
Cross-references current submission data against historical issuer information stored in your Knowledge Hub, identifying trends, deviations, and changes that warrant analytical attention.
Historical Data Integration
Cross-references current submission data against historical issuer information stored in your Knowledge Hub, identifying trends, deviations, and changes that warrant analytical attention.
Historical Data Integration
Cross-references current submission data against historical issuer information stored in your Knowledge Hub, identifying trends, deviations, and changes that warrant analytical attention.
Historical Data Integration
Cross-references current submission data against historical issuer information stored in your Knowledge Hub, identifying trends, deviations, and changes that warrant analytical attention.
Audit Trail Documentation
Creates a complete record of data extraction with visual citations linking every metric back to its source location in the issuer submission, ensuring regulatory compliance and internal quality control.
Audit Trail Documentation
Creates a complete record of data extraction with visual citations linking every metric back to its source location in the issuer submission, ensuring regulatory compliance and internal quality control.
Audit Trail Documentation
Creates a complete record of data extraction with visual citations linking every metric back to its source location in the issuer submission, ensuring regulatory compliance and internal quality control.
Audit Trail Documentation
Creates a complete record of data extraction with visual citations linking every metric back to its source location in the issuer submission, ensuring regulatory compliance and internal quality control.
Processes any issuer submission format
To deliver standardized analytical packages.
Get started
Get started


Import your files
Microsoft Excel
,
Microsoft Sharepoint Online
,
Snowflake
Import your files from whereever they are currently stored
All types of Finance documents supported
Once imported our system extracts and organises the essentials
Customer voices
Customer voices
Connect AI to your rating methodology.
Connect AI to your rating methodology.
Turn issuer submissions into standardized analytical packages.
Turn issuer submissions into standardized analytical packages.
Finance
•
Legal
•
Insurance
•
Tax
•
Real Estate
Finance
•
Legal
•
Insurance
•
Tax
•
Real Estate
Finance
•
Legal
•
Insurance
•
Tax
•
Real Estate
Customer Voices
Industrial equipment sales
We are looking for V7 Go and AI in general to be the beating heart of our company and our growth. It will make us more productive as a company, liaising with customers, automating tasks, even finding new work.
Read the full story
Industrial equipment sales
We are looking for V7 Go and AI in general to be the beating heart of our company and our growth. It will make us more productive as a company, liaising with customers, automating tasks, even finding new work.
Read the full story
Insurance
We have six assessors. Before V7 Go, each would process around 15 claims a day, about 90 in total. With V7 Go, we’re expecting that to rise to around 20 claims per assessor, which adds up to an extra 30 claims a day. That’s the equivalent of two additional full-time assessors. Beyond the cost savings, there’s real reputational gains from fewer errors and faster turnaround times.
Read the full story
Insurance
We have six assessors. Before V7 Go, each would process around 15 claims a day, about 90 in total. With V7 Go, we’re expecting that to rise to around 20 claims per assessor, which adds up to an extra 30 claims a day. That’s the equivalent of two additional full-time assessors. Beyond the cost savings, there’s real reputational gains from fewer errors and faster turnaround times.
Read the full story
Real Estate
Prior to V7, people using the software were manually inputting data. Now it’s so much faster because it just reads it for them. On average, it saves our customers 45 minutes to an hour of work, and it’s more accurate.
Read the full story
Real Estate
Prior to V7, people using the software were manually inputting data. Now it’s so much faster because it just reads it for them. On average, it saves our customers 45 minutes to an hour of work, and it’s more accurate.
Read the full story
Industrial equipment sales
We are looking for V7 Go and AI in general to be the beating heart of our company and our growth. It will make us more productive as a company, liaising with customers, automating tasks, even finding new work.
Read the full story
Insurance
We have six assessors. Before V7 Go, each would process around 15 claims a day, about 90 in total. With V7 Go, we’re expecting that to rise to around 20 claims per assessor, which adds up to an extra 30 claims a day. That’s the equivalent of two additional full-time assessors. Beyond the cost savings, there’s real reputational gains from fewer errors and faster turnaround times.
Read the full story
Real Estate
Prior to V7, people using the software were manually inputting data. Now it’s so much faster because it just reads it for them. On average, it saves our customers 45 minutes to an hour of work, and it’s more accurate.
Read the full story
Finance
“Whenever I think about hiring, I first try to do it in V7 Go.” Discover how HITICCO uses V7 Go agents to accelerate and enrich their prospect research.
Read the full story
Finance
The experience with V7 has been fantastic. Very customized level of support. You feel like they really care about your outcome and objectives.
Read the full story
Features
Features
Results you can actually trust.
Reliable AI document processing toolkit.
Results you can trust.
Trustworthy AI document processing toolkit.
Supporting complex documents.
Up to 200 pages.
Issuer submissions arrive in every conceivable format. This agent handles complex multi-tab Excel models with intricate formulas, PowerPoint presentations with embedded charts, and PDF financial statements with varying layouts. It extracts the data you need regardless of how issuers choose to present it.
Input types
50+ languages
Complex Formulas
200 pages
Multi-modal
Document types
Excel Models
PowerPoint
Financial Tables
Charts
PDFs
Vendor_US.xlsx

3
Supply_2023.pptx

Review_Legal.pdf

Supporting complex documents.
Up to 200 pages.
Issuer submissions arrive in every conceivable format. This agent handles complex multi-tab Excel models with intricate formulas, PowerPoint presentations with embedded charts, and PDF financial statements with varying layouts. It extracts the data you need regardless of how issuers choose to present it.
Input types
50+ languages
Complex Formulas
200 pages
Multi-modal
Document types
Excel Models
PowerPoint
Financial Tables
Charts
PDFs
Vendor_US.xlsx

3
Supply_2023.pptx

Review_Legal.pdf

Supporting complex documents.
Up to 200 pages.
Issuer submissions arrive in every conceivable format. This agent handles complex multi-tab Excel models with intricate formulas, PowerPoint presentations with embedded charts, and PDF financial statements with varying layouts. It extracts the data you need regardless of how issuers choose to present it.
Input types
50+ languages
Complex Formulas
200 pages
Multi-modal
Document types
Excel Models
PowerPoint
Financial Tables
Charts
PDFs
Vendor_US.xlsx

3
Supply_2023.pptx

Review_Legal.pdf

Reach 99% accuracy rate
through GenAI reasoning.
Credit ratings demand precision. The agent uses sophisticated financial reasoning to extract metrics with exceptional accuracy, understanding the relationships between line items and validating that calculations are internally consistent before delivering data to your analysts.
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)
Reach 99% accuracy rate
through GenAI reasoning.
Credit ratings demand precision. The agent uses sophisticated financial reasoning to extract metrics with exceptional accuracy, understanding the relationships between line items and validating that calculations are internally consistent before delivering data to your analysts.
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)
Reach 99% accuracy rate
through GenAI reasoning.
Credit ratings demand precision. The agent uses sophisticated financial reasoning to extract metrics with exceptional accuracy, understanding the relationships between line items and validating that calculations are internally consistent before delivering data to your analysts.
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)
Trustworthy results,
grounded in reality.
Every extracted financial metric is visually linked to its source location in the issuer submission. Analysts can instantly verify any number by clicking through to see the original Excel cell or presentation slide, ensuring complete confidence in the data underlying credit decisions.

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
Trustworthy results,
grounded in reality.
Every extracted financial metric is visually linked to its source location in the issuer submission. Analysts can instantly verify any number by clicking through to see the original Excel cell or presentation slide, ensuring complete confidence in the data underlying credit decisions.

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
Trustworthy results,
grounded in reality.
Every extracted financial metric is visually linked to its source location in the issuer submission. Analysts can instantly verify any number by clicking through to see the original Excel cell or presentation slide, ensuring complete confidence in the data underlying credit decisions.

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 high-stake industries.
Issuer submissions contain highly confidential financial information and material non-public data. V7 Go processes all documents within your secure environment with encryption, access controls, and audit trails that meet the stringent requirements of financial regulators and rating agency compliance standards.
Certifications
GDPR
SOC2
HIPAA
ISO
Safety
Custom storage
Data governance
Access-level permissions
Enterprise grade security
for high-stake industries.
Issuer submissions contain highly confidential financial information and material non-public data. V7 Go processes all documents within your secure environment with encryption, access controls, and audit trails that meet the stringent requirements of financial regulators and rating agency compliance standards.
Certifications
GDPR
SOC2
HIPAA
ISO
Safety
Custom storage
Data governance
Access-level permissions
Enterprise grade security
for high-stake industries.
Issuer submissions contain highly confidential financial information and material non-public data. V7 Go processes all documents within your secure environment with encryption, access controls, and audit trails that meet the stringent requirements of financial regulators and rating agency compliance standards.
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 agent for Rating Agency Analysts
How does the agent handle different financial model structures?
The agent uses advanced table recognition and financial reasoning to identify key metrics regardless of how issuers structure their models. It understands common financial statement patterns and can locate revenue, EBITDA, debt, and other critical metrics even when they appear in non-standard locations or use different labeling conventions.
+
How does the agent handle different financial model structures?
The agent uses advanced table recognition and financial reasoning to identify key metrics regardless of how issuers structure their models. It understands common financial statement patterns and can locate revenue, EBITDA, debt, and other critical metrics even when they appear in non-standard locations or use different labeling conventions.
+
How does the agent handle different financial model structures?
The agent uses advanced table recognition and financial reasoning to identify key metrics regardless of how issuers structure their models. It understands common financial statement patterns and can locate revenue, EBITDA, debt, and other critical metrics even when they appear in non-standard locations or use different labeling conventions.
+
Can it extract data from both Excel models and PDF presentations?
Yes. The agent processes both structured data from Excel spreadsheets and unstructured information from PowerPoint presentations or PDF documents. It extracts quantitative metrics from financial models and qualitative insights from management commentary, delivering a comprehensive analytical package.
+
Can it extract data from both Excel models and PDF presentations?
Yes. The agent processes both structured data from Excel spreadsheets and unstructured information from PowerPoint presentations or PDF documents. It extracts quantitative metrics from financial models and qualitative insights from management commentary, delivering a comprehensive analytical package.
+
Can it extract data from both Excel models and PDF presentations?
Yes. The agent processes both structured data from Excel spreadsheets and unstructured information from PowerPoint presentations or PDF documents. It extracts quantitative metrics from financial models and qualitative insights from management commentary, delivering a comprehensive analytical package.
+
How does it ensure consistency with our rating methodology?
You configure the agent with your specific rating methodology requirements, including which financial metrics are mandatory, acceptable calculation methods, and data quality standards. The agent validates each submission against these criteria and flags any gaps or inconsistencies before the data reaches your analysts.
+
How does it ensure consistency with our rating methodology?
You configure the agent with your specific rating methodology requirements, including which financial metrics are mandatory, acceptable calculation methods, and data quality standards. The agent validates each submission against these criteria and flags any gaps or inconsistencies before the data reaches your analysts.
+
How does it ensure consistency with our rating methodology?
You configure the agent with your specific rating methodology requirements, including which financial metrics are mandatory, acceptable calculation methods, and data quality standards. The agent validates each submission against these criteria and flags any gaps or inconsistencies before the data reaches your analysts.
+
What happens when issuer data is incomplete or unclear?
The agent identifies missing required data points and ambiguous calculations, generating a specific list of clarification questions to send back to the issuer. This ensures your analysts receive complete, validated data packages rather than discovering gaps mid-analysis.
+
What happens when issuer data is incomplete or unclear?
The agent identifies missing required data points and ambiguous calculations, generating a specific list of clarification questions to send back to the issuer. This ensures your analysts receive complete, validated data packages rather than discovering gaps mid-analysis.
+
What happens when issuer data is incomplete or unclear?
The agent identifies missing required data points and ambiguous calculations, generating a specific list of clarification questions to send back to the issuer. This ensures your analysts receive complete, validated data packages rather than discovering gaps mid-analysis.
+
How does it handle confidential issuer information?
V7 Go processes all issuer data within your secure environment with enterprise-grade encryption and access controls. The platform is designed for financial institutions handling material non-public information and adheres to strict data privacy and regulatory compliance standards.
+
How does it handle confidential issuer information?
V7 Go processes all issuer data within your secure environment with enterprise-grade encryption and access controls. The platform is designed for financial institutions handling material non-public information and adheres to strict data privacy and regulatory compliance standards.
+
How does it handle confidential issuer information?
V7 Go processes all issuer data within your secure environment with enterprise-grade encryption and access controls. The platform is designed for financial institutions handling material non-public information and adheres to strict data privacy and regulatory compliance standards.
+
Can we verify the accuracy of extracted financial data?
Every extracted metric includes a visual citation linking back to its exact location in the source document. Analysts can instantly verify any number by clicking through to see the original cell in the Excel model or the specific slide in the presentation, ensuring complete transparency and auditability.
+
Can we verify the accuracy of extracted financial data?
Every extracted metric includes a visual citation linking back to its exact location in the source document. Analysts can instantly verify any number by clicking through to see the original cell in the Excel model or the specific slide in the presentation, ensuring complete transparency and auditability.
+
Can we verify the accuracy of extracted financial data?
Every extracted metric includes a visual citation linking back to its exact location in the source document. Analysts can instantly verify any number by clicking through to see the original cell in the Excel model or the specific slide in the presentation, ensuring complete transparency and auditability.
+
Next steps
Next steps
Still manually extracting data from issuer models?
Send us a sample issuer submission package, and we'll show you how our agent delivers a standardized analytical dataset in hours instead of days.
Uncover hidden liabilities
in
supplier contracts.
V7 Go transforms documents into strategic assets. 150+ enterprises are already on board:
Uncover hidden liabilities
in
supplier contracts.
V7 Go transforms documents into strategic assets. 150+ enterprises are already on board:
