Vega


def analyze_finance():
    return insights

SELECT * FROM financial_data
WHERE quarter = 'Q2'

=SUM(A1:A10) * 1.15


{
  "revenue": 1250000,
  "expenses": 780000,
  "profit": 470000
}

import pandas as pd
df = pd.read_csv('data.csv')


CREATE TABLE financial_metrics (
  id INT,
  metric VARCHAR,
  value FLOAT
);

=VLOOKUP(A2, Sheet2!A:B, 2)


def analyze_finance():
    return insights

SELECT * FROM financial_data
WHERE quarter = 'Q2'

=SUM(A1:A10) * 1.15


{
  "revenue": 1250000,
  "expenses": 780000,
  "profit": 470000
}

import pandas as pd
df = pd.read_csv('data.csv')


CREATE TABLE financial_metrics (
  id INT,
  metric VARCHAR,
  value FLOAT
);

=VLOOKUP(A2, Sheet2!A:B, 2)


def analyze_finance():
    return insights

SELECT * FROM financial_data
WHERE quarter = 'Q2'

=SUM(A1:A10) * 1.15


{
  "revenue": 1250000,
  "expenses": 780000,
  "profit": 470000
}

import pandas as pd
df = pd.read_csv('data.csv')


CREATE TABLE financial_metrics (
  id INT,
  metric VARCHAR,
  value FLOAT
);

=VLOOKUP(A2, Sheet2!A:B, 2)


def analyze_finance():
    return insights

SELECT * FROM financial_data
WHERE quarter = 'Q2'

=SUM(A1:A10) * 1.15


{
  "revenue": 1250000,
  "expenses": 780000,
  "profit": 470000
}

import pandas as pd
df = pd.read_csv('data.csv')


CREATE TABLE financial_metrics (
  id INT,
  metric VARCHAR,
  value FLOAT
);

=VLOOKUP(A2, Sheet2!A:B, 2)


def analyze_finance():
    return insights

SELECT * FROM financial_data
WHERE quarter = 'Q2'

=SUM(A1:A10) * 1.15


{
  "revenue": 1250000,
  "expenses": 780000,
  "profit": 470000
}

import pandas as pd
df = pd.read_csv('data.csv')


CREATE TABLE financial_metrics (
  id INT,
  metric VARCHAR,
  value FLOAT
);

=VLOOKUP(A2, Sheet2!A:B, 2)


def analyze_finance():
    return insights

SELECT * FROM financial_data
WHERE quarter = 'Q2'

=SUM(A1:A10) * 1.15


{
  "revenue": 1250000,
  "expenses": 780000,
  "profit": 470000
}

import pandas as pd
df = pd.read_csv('data.csv')


CREATE TABLE financial_metrics (
  id INT,
  metric VARCHAR,
  value FLOAT
);

=VLOOKUP(A2, Sheet2!A:B, 2)


def analyze_finance():
    return insights

SELECT * FROM financial_data
WHERE quarter = 'Q2'

=SUM(A1:A10) * 1.15


{
  "revenue": 1250000,
  "expenses": 780000,
  "profit": 470000
}

import pandas as pd
df = pd.read_csv('data.csv')


CREATE TABLE financial_metrics (
  id INT,
  metric VARCHAR,
  value FLOAT
);

=VLOOKUP(A2, Sheet2!A:B, 2)


def analyze_finance():
    return insights

SELECT * FROM financial_data
WHERE quarter = 'Q2'

=SUM(A1:A10) * 1.15


{
  "revenue": 1250000,
  "expenses": 780000,
  "profit": 470000
}

import pandas as pd
df = pd.read_csv('data.csv')


CREATE TABLE financial_metrics (
  id INT,
  metric VARCHAR,
  value FLOAT
);

=VLOOKUP(A2, Sheet2!A:B, 2)


def analyze_finance():
    return insights

SELECT * FROM financial_data
WHERE quarter = 'Q2'

=SUM(A1:A10) * 1.15


{
  "revenue": 1250000,
  "expenses": 780000,
  "profit": 470000
}

import pandas as pd
df = pd.read_csv('data.csv')


CREATE TABLE financial_metrics (
  id INT,
  metric VARCHAR,
  value FLOAT
);

=VLOOKUP(A2, Sheet2!A:B, 2)


def analyze_finance():
    return insights

SELECT * FROM financial_data
WHERE quarter = 'Q2'

=SUM(A1:A10) * 1.15


{
  "revenue": 1250000,
  "expenses": 780000,
  "profit": 470000
}

import pandas as pd
df = pd.read_csv('data.csv')


CREATE TABLE financial_metrics (
  id INT,
  metric VARCHAR,
  value FLOAT
);

=VLOOKUP(A2, Sheet2!A:B, 2)

AI-Powered Data Exploration. Instant Insights.

Vega is your AI engine for rapid EDA, hypothesis testing, and research across Excel, Python, and Snowflake. Get from question to insight, faster than ever.

Explore Example Workflows

Analyze Sales Trends & Forecast

Identify sales trends, seasonality, top products, and generate a 6-month sales forecast with visualizations.

Sales forecast chart with 95% confidence interval

Reconcile Invoices & Payments

Automate invoice and payment matching, highlight discrepancies, and suggest actionable follow-ups.

Distribution of invoices days difference

Optimize Inventory Levels

Calculate reorder points and optimal stock levels for key products based on sales and inventory data.

Monthly P&L Email

Generate annual and quarterly Profit & Loss statements with standard structure and key metric breakdowns.

Segment Customers by Behavior

Perform RFM analysis to define customer segments and suggest targeted marketing or retention strategies.

Analyze Web Conversion Funnel

Visualize user flow, identify drop-off points in your conversion funnel, and get A/B test suggestions.

Unlock Your Data's Potential.

Tired of data wrangling and slow research cycles? Vega accelerates your data exploration. Connect your existing tools (Excel, Python, Snowflake) and let AI supercharge your EDA and hypothesis testing.

Manual data prep and siloed tools kill analytical agility. Vega automates the grunt work and connects your stack, so you can focus on discovery and research, not data plumbing.

financial_model.xlsx

Date	Revenue	Expenses	Net Income	Growth %
Q1 2023	$1,000,000	$650,000	$350,000	-
Q2 2023	$1,150,000	$725,000	$425,000	9.0%
Q3 2023	$1,300,000	$800,000	$500,000	11.0%
Q4 2023	$1,450,000	$875,000	$575,000	13.0%

Take a financial model update for example:

Financial analysts often start by downloading data from their ERP.
This data is then manually copied and pasted into an existing Excel model.
Formulas need to be checked and updated to reflect the new data.
Finally, the updated results are shared with stakeholders.
This entire process is manual, error-prone, and incredibly time-consuming, often requiring multiple iterations as new data arrives or errors are discovered during review.

Or take an AR reconciliation for example:

The process typically begins by exporting source datasets from an ERP, often into Excel.
Concurrently, target datasets are exported from payment platforms like Stripe, also usually into Excel.
The reconciliation itself is a manual effort, involving looking up individual invoices and meticulously matching them to remittance data.
Findings are then presented, typically in Excel, and any necessary adjustments require manual journal entries back into the ERP.

Stop choosing between manual slogs or rigid BI. Vega offers agile, AI-powered exploration.

AI for True Data Interactivity.

Snowflake

Excel

Python

ERP

AI shouldn't be just another dashboard. Vega embeds intelligence directly into your workflow, enabling dynamic data exploration and on-the-fly analysis across your entire stack.

Work with data the way you think. Ask questions in natural language, and Vega orchestrates analysis across Excel, Python, and Snowflake. Iterate on research without context switching or complex coding.

The Engine for Rapid Exploration.

Vega's AI dynamically generates and executes code (Python/JS) across your tools. Its core Asset Store enables fluid data exchange between analytical steps. This is the architecture that powers true rapid EDA and iterative research cycles.

Technical Architecture

Vega Architecture

Python

# Data processing
import pandas as pd
from vega import store

# Get data from store
data = store.get('financial_data')
df = pd.DataFrame(data)

# Process and transform
df['growth'] = df['revenue'].pct_change()

# Save results back to store
store.set('processed_data', df.to_dict())

Data Store

Cross-environment data sharing

store.get()

store.set()

JavaScript

// Excel automation
import { store } from 'vega';

async function updateExcel() {
  // Get processed data
  const data = await store.get('processed_data');
  
  // Update Excel worksheet
  await Excel.run(async (context) => {
    const sheet = context.workbook.worksheets
      .getActiveWorksheet();
    
    // Add data to sheet
    const range = sheet.getRange("A2:C10");
    range.values = formatForExcel(data);
    
    await context.sync();
  });
}

Data flows seamlessly between environments

AI-Driven Code for Analysis

Run Python and JavaScript code to process data and automate tasks across Excel, Snowflake, and other tools.

Seamless Data for Iteration

Share data between environments with simple store.get() and store.set() methods, eliminating manual transfers.

Interactive Notebooks: Your EDA Command Center.

Go from question to insight in one place. Connect data, ask Vega, and get an interactive notebook with markdown, Python, or JavaScript cells. It's your canvas for iterative EDA, data storytelling, and building reusable analytical workflows.

Refine your analysis. Tweak code, add new data sources, and rerun notebooks on demand. Vega adapts to your evolving research questions.

Excel

Database

Python

An AI That Learns Your Research Style.

Tailor Vega's analytical approach. Use custom prompts to guide its research, and leverage its evolving knowledge base to make future explorations faster and more relevant. It's AI that adapts to your way of working with data.

Customizable Prompts

Self-Improving Memory

Task Management

Pricing & Early Adopters

Contact chris@patterns.app to get access to Vega. We're seeking forward-thinking partners to refine Vega and demonstrate its transformative potential in financial operations.

Consulting Firms

Share implementation responsibilities and collaboratively develop best-in-class customer support models. Leverage reusable automation logic and domain-specific prompts for diverse client needs.

Corporate Finance Teams

Pilot AI-driven solutions for tangible efficiency gains in your financial operations and other teams driving reporting and automation inside their business functions.

Independent Analysts

Scale your services and offer more sophisticated data-driven insights to your clients using reusable automation logic and domain-specific prompts.

We are looking to work with consulting firms to share implementation responsibilities and collaboratively develop best-in-class customer support models. Your expertise in diverse client environments and ability to create reusable automation logic and domain-specific prompts will be invaluable.
We are looking to work with finance teams within corporations (and other teams driving reporting and automation inside their business functions) of all sizes who are feeling the pain of manual processes and are eager to pilot AI-driven solutions for tangible efficiency gains.
We are also interested in engaging with independent financial analysts and smaller advisory practices who can leverage Vega to scale their services, offer more sophisticated data-driven insights to their clients, and develop domain-specific prompts.

Are you a Consulting Firm or Data Agency?

Discover how Vega can empower your consultancy with white-label AI solutions, generate recurring revenue, and accelerate client delivery.

Learn More About Our Partner Program

If you work in or support a team that builds reports, monitors performance, or makes decisions based on messy data — Vega is your AI assistant. Get in touch to test it.

chris@patterns.app