# ✉️ SEC Comment Letter & Correspondence Monitor (`nexgendata/sec-comment-letter-monitor`) Actor SEC staff comment letters (UPLOAD) and responses (CORRESP) - **URL**: https://apify.com/nexgendata/sec-comment-letter-monitor.md - **Developed by:** [NexGenData](https://apify.com/nexgendata) (community) - **Categories:** Business - **Stats:** 4 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $5.00 / 1,000 products This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## ✉️ SEC Comment Letter & Correspondence Monitor Track **SEC staff comment letters** (form type `UPLOAD`) and registrant responses (`CORRESP`) the moment they hit EDGAR's full-text search index — the early-warning channel where the SEC's Division of Corporation Finance privately challenges a company's filings on revenue recognition, non-GAAP metrics, segment reporting, going-concern, crypto disclosure, and dozens of other accounting and disclosure issues. These letters routinely surface accounting risk **months before** it shows up in a restatement, an 8-K, or a stock move. This actor pulls them as structured JSON — company, ticker, form type, filed date, and a direct document link — filtered by date window and optional keyword. > **One actor. One token. The accounting-risk early-warning feed that forensic-accounting and short-research desks build by hand from EDGAR — delivered as pay-per-record JSON.** ### Why use this - **Earliest public signal of accounting scrutiny.** A `UPLOAD` letter is the SEC staff formally questioning a filing. By the time the market reacts to a restatement, this channel flagged the issue quarters earlier. - **Keyword targeting across the letter index.** Pass a `query` phrase (e.g. `revenue recognition`, `going concern`, `non-GAAP`) and AND it with the form filter to isolate the specific accounting topic you cover. - **Direct document links.** Every record carries a `documentUrl` to the letter itself, so your analysts (or your NLP pipeline) go straight from alert to source text. - **Date-window or lookback control.** Use `daysBack` for a rolling monitor or explicit `startDate`/`endDate` for a historical sweep of a coverage universe. - **Official source, ETL-ready.** Reads SEC EDGAR full-text search. Stable flat JSON loads straight into Snowflake, BigQuery, Postgres, or a Slack alerting workflow. ### What you get Each record represents one comment-letter or correspondence filing matching your filters. Core fields: - `companyName` — the registrant the letter concerns - `ticker` — the company's stock ticker (when resolvable) - `formType` — the SEC form type (`UPLOAD` = SEC staff comment letter) - `filedDate` — the date the letter was filed on EDGAR - `documentUrl` — direct link to the comment-letter document A typical monitoring workflow keys off `companyName` + `filedDate` for dedupe and routes the `documentUrl` to a reader or NLP step. ### Use cases - **Forensic-accounting / short research** — Run a daily monitor with `query="revenue recognition"` or `query="going concern"` and route every new `UPLOAD` to your analyst queue. SEC staff comments are a leading indicator of restatement risk that short desks weaponize before the broader market notices. - **Audit-committee & controller risk monitoring** — Track comment letters across your industry peer set to see which accounting topics the SEC staff is pressing this cycle (e.g. crypto-asset disclosure, supplier-finance arrangements, segment changes) and get ahead of the same questions on your own filings. - **Securities litigation sourcing** — Plaintiff and defense firms screen for `UPLOAD` letters on a coverage universe to identify disclosure disputes that may foreshadow securities-fraud claims. - **Event-driven / credit research** — A comment letter challenging going-concern language or non-GAAP add-backs is a tradeable signal for event-driven equity and distressed-credit desks. - **Compliance benchmarking** — Corporate IR and reporting teams study the SEC's live comment themes to calibrate their own MD&A and non-GAAP disclosures before filing. - **Academic / policy research** — Aggregate comment-letter volume and topics over time for studies on SEC enforcement priorities and disclosure-quality trends. ### How the comment-letter process works (and why the timing is the edge) When a company files a 10-K, 10-Q, S-1, or registration statement, the SEC's Division of Corporation Finance may **review it and send a private comment letter** challenging specific disclosures — how revenue is recognized, how a non-GAAP metric is reconciled, whether a going-concern qualification is warranted, how a segment is defined, or how a novel area (crypto-assets, SPAC accounting, supplier-finance programs) is presented. The company responds in writing; the staff may issue follow-ups. **The entire exchange is made public on EDGAR after the review concludes** — and that publication is the moment this actor catches. The reason this feed is valuable is sequencing. A disclosure problem typically travels: *comment letter → company response → possible amendment/restatement → 8-K → market reaction.* By the time the last step prints, the `UPLOAD` letter was searchable quarters earlier. Monitoring `UPLOAD` filings on a keyword and a coverage universe puts you at the front of that chain rather than the back of it. ### Common monitoring recipes - **Going-concern early warning:** `{"forms": "UPLOAD", "query": "going concern", "daysBack": 7}` on a daily schedule — surfaces companies the SEC staff is pressing on viability. - **Revenue-recognition scrutiny:** `{"forms": "UPLOAD", "query": "revenue recognition", "daysBack": 30}` — the single most common high-impact comment theme. - **Non-GAAP pushback:** `{"forms": "UPLOAD", "query": "non-GAAP", "daysBack": 30}` — tracks the SEC's recurring focus on adjusted-metric prominence and add-backs. - **Historical sweep of a window:** `{"forms": "UPLOAD", "startDate": "2026-01-01", "endDate": "2026-06-30", "maxResults": 500}` — build a back-test set of letters for a coverage universe. ### Sample output ```json { "companyName": "EXAMPLE TECHNOLOGIES INC", "ticker": "EXTC", "formType": "UPLOAD", "filedDate": "2026-06-18", "documentUrl": "https://www.sec.gov/Archives/edgar/data/0001234567/000000000026000123/filename.htm" } ```` ### Input parameters | Parameter | What it does | |-----------|--------------| | `forms` | SEC comment-letter form type. `UPLOAD` = SEC staff comment letters. Note: `CORRESP` currently errors on the SEC full-text search backend. | | `query` | Optional exact-phrase keyword to AND with the form filter (e.g. `revenue recognition`, `non-GAAP`, `going concern`). | | `daysBack` | Look back this many days from today (ignored if explicit dates are set). | | `startDate` | Override start of the window (`YYYY-MM-DD`). | | `endDate` | Override end of the window (`YYYY-MM-DD`). | | `maxResults` | Maximum number of filings to return. | | `userAgentContact` | SEC requires a User-Agent with contact info; supply your contact string here. | ### How to use #### Python (apify-client) ```python from apify_client import ApifyClient client = ApifyClient("YOUR_TOKEN") run = client.actor("nexgendata/sec-comment-letter-monitor").call(run_input={ "forms": "UPLOAD", "query": "revenue recognition", "daysBack": 30, "maxResults": 200, "userAgentContact": "Your Name your@email.com", }) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item["filedDate"], item["companyName"], item["ticker"], item["documentUrl"]) ``` #### cURL ```bash curl -X POST "https://api.apify.com/v2/acts/nexgendata~sec-comment-letter-monitor/run-sync-get-dataset-items?token=YOUR_TOKEN" \ -H "Content-Type: application/json" \ -d '{ "forms": "UPLOAD", "query": "going concern", "startDate": "2026-01-01", "endDate": "2026-06-30", "maxResults": 500, "userAgentContact": "Your Name your@email.com" }' ``` #### No-code (Zapier / Make) Use the Apify "Run actor and get dataset items" action with actor ID `nexgendata/sec-comment-letter-monitor`, schedule it daily, and fire a Slack message or CRM task whenever a new `UPLOAD` matching your keyword appears — mapping `companyName`, `filedDate`, and `documentUrl` into the alert. ### Pricing This actor runs on Apify's **pay-per-event (PPE)** model — **$0.10 per result** (one charge per comment-letter record returned), plus a sub-cent actor-start event. No subscription, no seat licence, no minimum. Worked example: - A daily monitor returning 15 new letters → **$1.50** - A monthly keyword sweep returning 200 letters → **$20.00** - A historical coverage-universe pull returning 500 letters → **$50.00** Because billing is per result, a quiet day with no matching letters costs effectively nothing. ### How this compares to Bloomberg Terminal Bloomberg's terminal surfaces SEC correspondence inside a **~$24,000-per-seat-per-year** workspace, and dedicated comment-letter analytics products (Audit Analytics, Intelligize, Ideagen Disclosure) run on **enterprise contracts** that price most individual researchers and small funds out entirely. Those platforms add taxonomies, full-text analytics, and curated alerting you may still want at scale. But if your workflow is a **keyword-targeted monitor on a coverage universe** — "tell me every new SEC comment letter mentioning going concern" — you do not need a Bloomberg seat to get the company, date, and document link. You need this actor, queryable from your own code at a fraction of the cost, with the document URL ready for your own NLP. ### FAQ **Q: What exactly is an `UPLOAD` filing?** A: `UPLOAD` is the EDGAR form type for a comment letter sent by the SEC's Division of Corporation Finance staff to a registrant, questioning aspects of a filing. `CORRESP` is the registrant's written response. Both are made public on EDGAR after the review concludes. **Q: Why does `CORRESP` sometimes error?** A: The SEC full-text search backend currently returns errors for `CORRESP` form queries in some cases. The `UPLOAD` (staff letters) path is the reliable one and is where the accounting-risk signal originates. Track `UPLOAD` and follow links to the related `CORRESP` on the company's EDGAR page when needed. **Q: How fresh are the results?** A: Comment letters become searchable when the SEC publishes them on EDGAR (typically after the review process closes). Each run queries the live index; schedule daily for an early-warning monitor. **Q: Do I need the `userAgentContact`?** A: Yes. The SEC requires every automated request to identify itself with a contact (name + email). Supply yours so the SEC can reach the request's operator per its fair-access policy. **Q: Can I get the letter's full text?** A: This actor returns the `documentUrl` to each letter. Follow the link (or pipe it into a downstream fetch/NLP step) to retrieve and analyze the full letter text. ### Schema stability & versioning This actor follows NexGenData's **additive-only schema** contract. New fields may be added over time (appearing as new JSON keys, defaulting to `null` for older runs), but existing fields are never renamed or removed without a major-version bump and advance notice. Field semantics — date formats, value-sets — are never silently changed. Build production pipelines on this output with confidence. ### Compliance & legal - This actor reformats **public SEC EDGAR data**. It is not affiliated with, endorsed by, or sponsored by the SEC. - Comment letters and correspondence are official public filings. Nothing here is legal or investment advice. - For any regulated decision — securities research, litigation, compliance — **verify against the primary EDGAR filing** and consult qualified counsel. - Requests identify themselves to the SEC per its fair-access User-Agent policy. You are responsible for compliant downstream use. ### Related NexGenData actors Part of NexGenData's **SEC / Disclosure intelligence** cluster — pair this actor with: - [SEC Form 8-K Event Monitor](https://apify.com/nexgendata/sec-8k-event-monitor?fpr=2ayu9b) - [SEC Executive Compensation Proxy Tracker](https://apify.com/nexgendata/sec-exec-comp-proxy-tracker?fpr=2ayu9b) - [SEC XBRL Financial Frames](https://apify.com/nexgendata/sec-xbrl-financial-frames?fpr=2ayu9b) - [SEC Form 4 Insider Monitor](https://apify.com/nexgendata/sec-form-4-insider-monitor?fpr=2ayu9b) - [SEC Delisting & Deregistration Tracker](https://apify.com/nexgendata/sec-delisting-deregistration-tracker?fpr=2ayu9b) - [SEC Going-Private Tracker](https://apify.com/nexgendata/sec-going-private-tracker?fpr=2ayu9b) Browse the full 200+ actor catalog at **https://apify.com/nexgendata?fpr=2ayu9b**. # Actor input Schema ## `forms` (type: `string`): SEC comment-letter form (UPLOAD = SEC staff letters). Note: CORRESP currently errors on SEC FTS. ## `query` (type: `string`): Optional exact-phrase to AND with the form filter. ## `daysBack` (type: `integer`): Look back this many days (ignored if dates set). ## `startDate` (type: `string`): Override start (YYYY-MM-DD). ## `endDate` (type: `string`): Override end (YYYY-MM-DD). ## `maxResults` (type: `integer`): Maximum filings to return. ## `userAgentContact` (type: `string`): SEC requires a UA with contact info. ## Actor input object example ```json { "forms": "UPLOAD", "daysBack": 120, "maxResults": 300 } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "forms": "UPLOAD" }; // Run the Actor and wait for it to finish const run = await client.actor("nexgendata/sec-comment-letter-monitor").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "forms": "UPLOAD" } # Run the Actor and wait for it to finish run = client.actor("nexgendata/sec-comment-letter-monitor").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "forms": "UPLOAD" }' | apify call nexgendata/sec-comment-letter-monitor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nexgendata/sec-comment-letter-monitor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "✉️ SEC Comment Letter & Correspondence Monitor", "description": "SEC staff comment letters (UPLOAD) and responses (CORRESP)", "version": "0.0", "x-build-id": "udyBwDXLVHnf2VcrQ" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nexgendata~sec-comment-letter-monitor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nexgendata-sec-comment-letter-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/nexgendata~sec-comment-letter-monitor/runs": { "post": { "operationId": "runs-sync-nexgendata-sec-comment-letter-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/nexgendata~sec-comment-letter-monitor/run-sync": { "post": { "operationId": "run-sync-nexgendata-sec-comment-letter-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "forms": { "title": "Form types", "type": "string", "description": "SEC comment-letter form (UPLOAD = SEC staff letters). Note: CORRESP currently errors on SEC FTS.", "default": "UPLOAD" }, "query": { "title": "Keyword (optional)", "type": "string", "description": "Optional exact-phrase to AND with the form filter." }, "daysBack": { "title": "Days back", "minimum": 1, "maximum": 3650, "type": "integer", "description": "Look back this many days (ignored if dates set).", "default": 120 }, "startDate": { "title": "Start date", "type": "string", "description": "Override start (YYYY-MM-DD)." }, "endDate": { "title": "End date", "type": "string", "description": "Override end (YYYY-MM-DD)." }, "maxResults": { "title": "Max results", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum filings to return.", "default": 300 }, "userAgentContact": { "title": "SEC User-Agent contact", "type": "string", "description": "SEC requires a UA with contact info." } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```