Financial reporting AI: concise SEC 10-K/10-Q briefs via OpenRouter + Perplexity
Why skim 10-K/10-Q by hand when AI can extract what matters in minutes?
Who is this for?
Sales engineers, solution architects, founders, product/strategy teams, analysts, and BD reps who need fast, consistent briefs on public companies—plus a mapping to a chosen vendor’s solutions.
Use case
- Problem: SEC filings are dense. Manually summarizing financials, spotting initiatives, and aligning them to a vendor’s offerings is slow, error-prone, and often missed by busy teams.
- Use Case: An n8n workflow that takes a company name / URL / ticker, analyzes the latest 10-Q/10-K with Perplexity “Deep Research” (via OpenRouter), extracts a concise financial overview and top 4–5 initiatives (tech, cost, revenue), then maps them to one vendor’s solutions (e.g., Microsoft, Google, T-Mobile) and outputs a clean brief.
What this workflow does
-
Trigger: “When chat message received” (or webhook) accepts name / URL / ticker + target vendor.
-
AI Agent:
-
Chat Model (OpenRouter) orchestrates the prompt and formatting.
-
Tool: Perplexity Deep Research performs retrieval over the latest 10-K/10-Q and recent references.
-
-
Output: Creates a Google Drive document from the generated text (title, summary, initiatives, vendor-solution matches, suggested contacts).
- (Optional) Append a row to Google Sheets for tracking companies, initiatives, and match scores.
Prerequisites
-
n8n (Cloud or self-hosted).
-
Credentials in n8n:
-
OpenRouter API key (with access to Perplexity’s Deep Research model).
-
Google Drive (and Google Sheets, if you add the sheet step).
-
Setup
-
Import the workflow JSON into n8n.
-
Open Credentials → connect OpenRouter and Google Drive.
-
In the AI Agent node:
-
Set Chat Model to your OpenRouter model.
-
Set Tool to Perplexity’s Deep Research endpoint.
-
Paste the provided prompt that(example):
-
Summarize the key financial highlights and list the key strategic initiatives from Office Depot's most recent quarterly report. The company's URL is https://www.officedepot.com and its stock symbol is ODP.
After gathering this information, compare Office Depot's initiatives with the solutions offered by T-Mobile for Business (URL: https://www.t-mobile.com/business, stock symbol: TMUS). Finally, provide specific recommendations on T-Mobile solutions that can help Office Depot achieve its initiatives.
Explain how each recommended solution would help. Present the final response with clear headings for each section.
How to customize it for your needs
-
Ticker/Name disambiguation: Add a guardrail step that confirms the exchange + CIK before analysis.
-
EDGAR fetch (advanced): Pull the exact 10-K/10-Q document/link and pass it to the model for grounded citations.
-
Multi-vendor mapping: Loop over a list (e.g., Microsoft, Google Cloud, AWS) and produce a comparison table.
-
Contact enrichment: Add your preferred enrichment step to suggest roles (IT, Network, Data, Finance).
-
Scoring: Compute initiative ↔ solution fit scores and prioritize must-explore actions.
-
Alerts: Send the brief to Slack/Telegram/Email for your team.
Troubleshooting
-
Wrong company matched? Add a pre-check that resolves ticker → legal name and require confirmation.
-
Generic web summary? Tighten the prompt: “Use the latest 10-Q/10-K; cite sections; list initiatives with evidence.”
-
Empty Drive file? Verify the AI Agent’s {{$json}} mappings flow into the Drive node’s content.
-
No citations? Require bullet-level references; if missing, loop once with a “citations-only” follow-up prompt.
Why Use This Template?
Turn hours of filing review into a repeatable, shareable brief. You’ll get:
-
A clean financial snapshot,
-
The company’s top initiatives,
-
A vendor-aligned solution map you can act on immediately—great for prospecting, QBRs, and strategic planning.
Expected Outcome:
Need Assistance?
For setup guidance, customization, or business inquiries, Email: phoenixaiagentsolutions@gmail.com
Financial Reporting AI: Concise SEC 10-K/10-Q Briefs via OpenRouter & Perplexity
This n8n workflow automates the creation of concise financial reporting briefs from SEC 10-K and 10-Q filings. It leverages AI models via OpenRouter to summarize complex financial documents, making key information readily accessible.
What it does
This workflow streamlines the process of extracting and summarizing critical information from SEC filings through the following steps:
- Receives Chat Message: The workflow is triggered by an incoming chat message, likely containing a request for a financial brief or a link to an SEC filing.
- Edits Fields (Set): Initial data from the chat message is processed and structured for subsequent AI operations. This step might involve extracting the filing URL or company name.
- HTTP Request (Perplexity API): It makes an HTTP request to the Perplexity API to fetch the content of the specified SEC filing.
- Code (Extract Relevant Text): A custom JavaScript code block is used to parse the raw text from the Perplexity API response, likely extracting only the most relevant sections of the 10-K or 10-Q filing.
- AI Agent (OpenRouter): The extracted text is then fed into an AI Agent, which utilizes an OpenRouter Chat Model. This agent is configured to generate a concise summary or brief based on the financial document's content.
- Markdown: The AI-generated brief is formatted into Markdown for clear and readable presentation.
Prerequisites/Requirements
To use this workflow, you will need:
- n8n Instance: A running n8n instance.
- OpenRouter API Key: An API key for OpenRouter to access various AI models.
- Perplexity API Key: An API key for Perplexity to fetch and process web content.
- Chat Platform Integration: A chat platform (e.g., Slack, Telegram, Discord) integrated with n8n to trigger the workflow via the "Chat Trigger" node.
Setup/Usage
- Import the Workflow: Download the JSON provided and import it into your n8n instance.
- Configure Credentials:
- Set up your OpenRouter API Key as an n8n credential for the "OpenRouter Chat Model" node.
- Set up your Perplexity API Key as an n8n credential or configure it directly in the "HTTP Request" node if it's used as a header/query parameter.
- Configure Chat Trigger: Set up the "When chat message received" node to listen for messages from your desired chat platform.
- Customize Code Node: Review and adjust the "Code" node's JavaScript to ensure it correctly extracts the relevant sections from the Perplexity API response based on your specific needs for 10-K/10-Q filings.
- Activate the Workflow: Once configured, activate the workflow.
- Trigger the Workflow: Send a chat message to your configured chat platform to trigger the workflow. The message format will depend on how you've configured the "Edit Fields (Set)" node to extract information (e.g., a direct link to an SEC filing, a company ticker, or a specific request).
Related Templates
Track competitor SEO keywords with Decodo + GPT-4.1-mini + Google Sheets
This workflow automates competitor keyword research using OpenAI LLM and Decodo for intelligent web scraping. Who this is for SEO specialists, content strategists, and growth marketers who want to automate keyword research and competitive intelligence. Marketing analysts managing multiple clients or websites who need consistent SEO tracking without manual data pulls. Agencies or automation engineers using Google Sheets as an SEO data dashboard for keyword monitoring and reporting. What problem this workflow solves Tracking competitor keywords manually is slow and inconsistent. Most SEO tools provide limited API access or lack contextual keyword analysis. This workflow solves that by: Automatically scraping any competitor’s webpage with Decodo. Using OpenAI GPT-4.1-mini to interpret keyword intent, density, and semantic focus. Storing structured keyword insights directly in Google Sheets for ongoing tracking and trend analysis. What this workflow does Trigger — Manually start the workflow or schedule it to run periodically. Input Setup — Define the website URL and target country (e.g., https://dev.to, france). Data Scraping (Decodo) — Fetch competitor web content and metadata. Keyword Analysis (OpenAI GPT-4.1-mini) Extract primary and secondary keywords. Identify focus topics and semantic entities. Generate a keyword density summary and SEO strength score. Recommend optimization and internal linking opportunities. Data Structuring — Clean and convert GPT output into JSON format. Data Storage (Google Sheets) — Append structured keyword data to a Google Sheet for long-term tracking. Setup Prerequisites If you are new to Decode, please signup on this link visit.decodo.com n8n account with workflow editor access Decodo API credentials OpenAI API key Google Sheets account connected via OAuth2 Make sure to install the Decodo Community node. Create a Google Sheet Add columns for: primarykeywords, seostrengthscore, keyworddensity_summary, etc. Share with your n8n Google account. Connect Credentials Add credentials for: Decodo API credentials - You need to register, login and obtain the Basic Authentication Token via Decodo Dashboard OpenAI API (for GPT-4o-mini) Google Sheets OAuth2 Configure Input Fields Edit the “Set Input Fields” node to set your target site and region. Run the Workflow Click Execute Workflow in n8n. View structured results in your connected Google Sheet. How to customize this workflow Track Multiple Competitors → Use a Google Sheet or CSV list of URLs; loop through them using the Split In Batches node. Add Language Detection → Add a Gemini or GPT node before keyword analysis to detect content language and adjust prompts. Enhance the SEO Report → Expand the GPT prompt to include backlink insights, metadata optimization, or readability checks. Integrate Visualization → Connect your Google Sheet to Looker Studio for SEO performance dashboards. Schedule Auto-Runs → Use the Cron Node to run weekly or monthly for competitor keyword refreshes. Summary This workflow automates competitor keyword research using: Decodo for intelligent web scraping OpenAI GPT-4.1-mini for keyword and SEO analysis Google Sheets for live tracking and reporting It’s a complete AI-powered SEO intelligence pipeline ideal for teams that want actionable insights on keyword gaps, optimization opportunities, and content focus trends, without relying on expensive SEO SaaS tools.
Generate song lyrics and music from text prompts using OpenAI and Fal.ai Minimax
Spark your creativity instantly in any chat—turn a simple prompt like "heartbreak ballad" into original, full-length lyrics and a professional AI-generated music track, all without leaving your conversation. 📋 What This Template Does This chat-triggered workflow harnesses AI to generate detailed, genre-matched song lyrics (at least 600 characters) from user messages, then queues them for music synthesis via Fal.ai's minimax-music model. It polls asynchronously until the track is ready, delivering lyrics and audio URL back in chat. Crafts original, structured lyrics with verses, choruses, and bridges using OpenAI Submits to Fal.ai for melody, instrumentation, and vocals aligned to the style Handles long-running generations with smart looping and status checks Returns complete song package (lyrics + audio link) for seamless sharing 🔧 Prerequisites n8n account (self-hosted or cloud with chat integration enabled) OpenAI account with API access for GPT models Fal.ai account for AI music generation 🔑 Required Credentials OpenAI API Setup Go to platform.openai.com → API keys (sidebar) Click "Create new secret key" → Name it (e.g., "n8n Songwriter") Copy the key and add to n8n as "OpenAI API" credential type Test by sending a simple chat completion request Fal.ai HTTP Header Auth Setup Sign up at fal.ai → Dashboard → API Keys Generate a new API key → Copy it In n8n, create "HTTP Header Auth" credential: Name="Fal.ai", Header Name="Authorization", Header Value="Key [Your API Key]" Test with a simple GET to their queue endpoint (e.g., /status) ⚙️ Configuration Steps Import the workflow JSON into your n8n instance Assign OpenAI API credentials to the "OpenAI Chat Model" node Assign Fal.ai HTTP Header Auth to the "Generate Music Track", "Check Generation Status", and "Fetch Final Result" nodes Activate the workflow—chat trigger will appear in your n8n chat interface Test by messaging: "Create an upbeat pop song about road trips" 🎯 Use Cases Content Creators: YouTubers generating custom jingles for videos on the fly, streamlining production from idea to audio export Educators: Music teachers using chat prompts to create era-specific folk tunes for classroom discussions, fostering interactive learning Gift Personalization: Friends crafting anniversary R&B tracks from shared memories via quick chats, delivering emotional audio surprises Artist Brainstorming: Songwriters prototyping hip-hop beats in real-time during sessions, accelerating collaboration and iteration ⚠️ Troubleshooting Invalid JSON from AI Agent: Ensure the system prompt stresses valid JSON; test the agent standalone with a sample query Music Generation Fails (401/403): Verify Fal.ai API key has minimax-music access; check usage quotas in dashboard Status Polling Loops Indefinitely: Bump wait time to 45-60s for complex tracks; inspect fal.ai queue logs for bottlenecks Lyrics Under 600 Characters: Tweak agent prompt to enforce fuller structures like [V1][C][V2][B][C]; verify output length in executions
Automate invoice processing with OCR, GPT-4 & Salesforce opportunity creation
PDF Invoice Extractor (AI) End-to-end pipeline: Watch Drive ➜ Download PDF ➜ OCR text ➜ AI normalize to JSON ➜ Upsert Buyer (Account) ➜ Create Opportunity ➜ Map Products ➜ Create OLI via Composite API ➜ Archive to OneDrive. --- Node by node (what it does & key setup) 1) Google Drive Trigger Purpose: Fire when a new file appears in a specific Google Drive folder. Key settings: Event: fileCreated Folder ID: google drive folder id Polling: everyMinute Creds: googleDriveOAuth2Api Output: Metadata { id, name, ... } for the new file. --- 2) Download File From Google Purpose: Get the file binary for processing and archiving. Key settings: Operation: download File ID: ={{ $json.id }} Creds: googleDriveOAuth2Api Output: Binary (default key: data) and original metadata. --- 3) Extract from File Purpose: Extract text from PDF (OCR as needed) for AI parsing. Key settings: Operation: pdf OCR: enable for scanned PDFs (in options) Output: JSON with OCR text at {{ $json.text }}. --- 4) Message a model (AI JSON Extractor) Purpose: Convert OCR text into strict normalized JSON array (invoice schema). Key settings: Node: @n8n/n8n-nodes-langchain.openAi Model: gpt-4.1 (or gpt-4.1-mini) Message role: system (the strict prompt; references {{ $json.text }}) jsonOutput: true Creds: openAiApi Output (per item): $.message.content → the parsed JSON (ensure it’s an array). --- 5) Create or update an account (Salesforce) Purpose: Upsert Buyer as Account using an external ID. Key settings: Resource: account Operation: upsert External Id Field: taxid_c External Id Value: ={{ $json.message.content.buyer.tax_id }} Name: ={{ $json.message.content.buyer.name }} Creds: salesforceOAuth2Api Output: Account record (captures Id) for downstream Opportunity. --- 6) Create an opportunity (Salesforce) Purpose: Create Opportunity linked to the Buyer (Account). Key settings: Resource: opportunity Name: ={{ $('Message a model').item.json.message.content.invoice.code }} Close Date: ={{ $('Message a model').item.json.message.content.invoice.issue_date }} Stage: Closed Won Amount: ={{ $('Message a model').item.json.message.content.summary.grand_total }} AccountId: ={{ $json.id }} (from Upsert Account output) Creds: salesforceOAuth2Api Output: Opportunity Id for OLI creation. --- 7) Build SOQL (Code / JS) Purpose: Collect unique product codes from AI JSON and build a SOQL query for PricebookEntry by Pricebook2Id. Key settings: pricebook2Id (hardcoded in script): e.g., 01sxxxxxxxxxxxxxxx Source lines: $('Message a model').first().json.message.content.products Output: { soql, codes } --- 8) Query PricebookEntries (Salesforce) Purpose: Fetch PricebookEntry.Id for each Product2.ProductCode. Key settings: Resource: search Query: ={{ $json.soql }} Creds: salesforceOAuth2Api Output: Items with Id, Product2.ProductCode (used for mapping). --- 9) Code in JavaScript (Build OLI payloads) Purpose: Join lines with PBE results and Opportunity Id ➜ build OpportunityLineItem payloads. Inputs: OpportunityId: ={{ $('Create an opportunity').first().json.id }} Lines: ={{ $('Message a model').first().json.message.content.products }} PBE rows: from previous node items Output: { body: { allOrNone:false, records:[{ OpportunityLineItem... }] } } Notes: Converts discount_total ➜ per-unit if needed (currently commented for standard pricing). Throws on missing PBE mapping or empty lines. --- 10) Create Opportunity Line Items (HTTP Request) Purpose: Bulk create OLIs via Salesforce Composite API. Key settings: Method: POST URL: https://<your-instance>.my.salesforce.com/services/data/v65.0/composite/sobjects Auth: salesforceOAuth2Api (predefined credential) Body (JSON): ={{ $json.body }} Output: Composite API results (per-record statuses). --- 11) Update File to One Drive Purpose: Archive the original PDF in OneDrive. Key settings: Operation: upload File Name: ={{ $json.name }} Parent Folder ID: onedrive folder id Binary Data: true (from the Download node) Creds: microsoftOneDriveOAuth2Api Output: Uploaded file metadata. --- Data flow (wiring) Google Drive Trigger → Download File From Google Download File From Google → Extract from File → Update File to One Drive Extract from File → Message a model Message a model → Create or update an account Create or update an account → Create an opportunity Create an opportunity → Build SOQL Build SOQL → Query PricebookEntries Query PricebookEntries → Code in JavaScript Code in JavaScript → Create Opportunity Line Items --- Quick setup checklist 🔐 Credentials: Connect Google Drive, OneDrive, Salesforce, OpenAI. 📂 IDs: Drive Folder ID (watch) OneDrive Parent Folder ID (archive) Salesforce Pricebook2Id (in the JS SOQL builder) 🧠 AI Prompt: Use the strict system prompt; jsonOutput = true. 🧾 Field mappings: Buyer tax id/name → Account upsert fields Invoice code/date/amount → Opportunity fields Product name must equal your Product2.ProductCode in SF. ✅ Test: Drop a sample PDF → verify: AI returns array JSON only Account/Opportunity created OLI records created PDF archived to OneDrive --- Notes & best practices If PDFs are scans, enable OCR in Extract from File. If AI returns non-JSON, keep “Return only a JSON array” as the last line of the prompt and keep jsonOutput enabled. Consider adding validation on parsing.warnings to gate Salesforce writes. For discounts/taxes in OLI: Standard OLI fields don’t support per-line discount amounts directly; model them in UnitPrice or custom fields. Replace the Composite API URL with your org’s domain or use the Salesforce node’s Bulk Upsert for simplicity.