SEC data consulting for teams shipping AI agents and financial AI products. From prototype to production — in weeks, not months.
LLMs hallucinate revenue numbers. Structured XBRL fixes it. Almost nobody knows how to use it. We do.
edgar.tools offers SEC data consulting for teams building AI agents and financial AI products. Created by Dwight Gunning — author of the open-source edgartools Python library (3M+ downloads, 100K+ daily, 2,000+ GitHub stars) — our consulting practice helps teams solve the hardest problems in SEC data: MCP and AI agent integration, XBRL extraction, RAG grounding for financial accuracy, and production EDGAR infrastructure. Three fixed-price engagements from $5,000.
⊕ Last updated · May 2026
LLMs hallucinate on financial data. Structured XBRL fixes it — but almost nobody knows how to use it.
Per the FinanceBench benchmark, GPT-4 answers 81% of financial questions incorrectly. Research shows that using structured XBRL data instead of raw HTML reduces extraction errors by 74× — from 8.16% to 0.11%. Yet LLMs achieve only 17% accuracy on XBRL taxonomy concept linking across the 18,000-element US-GAAP taxonomy. XBRL is the answer to financial AI accuracy, but with extension mechanisms, dimensional structures, and 18,000 elements — you need someone who's spent years in the weeds.
Productized engagements with fixed scope, fixed price, and defined deliverables.
Go from zero to working SEC data pipeline in days. We work with your actual data to build a functioning prototype — not slides, not a report.
Find out what your SEC data pipeline is getting wrong — and exactly how to fix it. A deep audit of your current architecture with prioritized recommendations.
Production-grade SEC data infrastructure, built by the team behind edgartools. Working component of your data pipeline or MCP server — code you own and your team can maintain.
Need ongoing advisory or an embedded expert? We offer retainer engagements for teams with continuous SEC data needs. Let's talk.
We solve the hard problems that sit between raw SEC filings and reliable AI products.
Wire SEC data into your LLM agents via Model Context Protocol. Production-ready tool servers, structured outputs designed for agentic composition, and grounded context for finance workflows.
Ground your LLM outputs in authoritative, structured SEC data. Reduce financial hallucinations from 81% to near-zero.
Structured financial data extraction across thousands of companies. Navigate the 18,000-element US-GAAP taxonomy without getting lost. Learn more →
Pipelines that handle EDGAR rate limits, format inconsistencies, filing amendments, and the edge cases that break naive parsers.
Design systems that ingest, normalize, and serve SEC data at scale. From startup MVP to enterprise-grade platform.
Test your financial AI against FinanceBench or custom evaluation sets. Measure accuracy before and after XBRL integration.
Teams building the next generation of financial intelligence products.
"Our outputs cite numbers that aren't in the filing."
We ground your LLM outputs in structured XBRL data so your financial AI gets facts right.
"Our parser breaks on every novel filing format."
We build infrastructure that handles EDGAR's rate limits, format changes, and 10,000+ filing edge cases.
"Bloomberg doesn't expose this — and we need it now."
We extract custom SEC data — insider transactions, institutional holdings, executive comp — that terminal vendors don't cover.
I'm Dwight Gunning, creator of edgartools — the most-downloaded open-source Python library for SEC data, with 3M+ lifetime downloads, 100K+ daily downloads, and 2,000+ GitHub stars.
When your AI agent gets revenue numbers wrong, when your MCP server returns half-parsed XBRL, when EDGAR's rate limiter blocks your entire pipeline during earnings season — I've already solved those problems. In production.
You're not hiring a generic consulting firm. You're working with the person who wrote the library your developers already use.
Where the open-source edgartools library shows up
Public usage signals from the open-source library — these are not consulting customers; they're teams running edgartools in their own products, repos, and pipelines.
Public deployments include hyperscale cloud sample repositories, ML platform tutorials, top-tier consulting firms' GenAI training programs, multi-hundred-million-dollar wealth managers, YC-backed AI startups, and university research groups — alongside the conda-forge feedstock and an active community of MCP server maintainers.
30 minutes. Tell us your SEC data challenge. We'll tell you exactly how we'd solve it — and whether we're the right fit.
You get a fixed-price proposal with clear deliverables, timeline, and acceptance criteria. No surprises, no scope creep.
We build on your actual data. You get working code, documentation, and a handoff your team can run with. Full source code ownership.
Yes — MCP integration is the most common engagement we run today. We build production-grade MCP servers that expose SEC data as tools your LLM agents can call directly: structured outputs, proper error handling, rate-limit safety, and integration with the edgartools library underneath. Typical scope: 2 weeks, $30,000–$50,000.
That's a frequent ask. Many teams prototype an agent on edgartools and then hit production challenges: SEC rate limits, XBRL inconsistencies across filings, retry logic, ground-truth evaluation, deployment. We work alongside your engineers to take the prototype to production — typically a 2–4 week Pipeline Build engagement.
Every Pipeline Build includes 2 weeks of post-delivery support. Beyond that, we offer monthly retainer engagements for teams with continuous SEC data needs — covering bug fixes, feature additions, EDGAR API changes, and an SLA on response time. Discuss on the discovery call.
Yes — that's exactly what the Architecture Review is for. We audit your existing pipeline, identify accuracy gaps and performance bottlenecks, and deliver a prioritized remediation plan. Most teams find issues they didn't know they had.
100%. All custom code, documentation, and deliverables are yours. We include a team handoff session and 2 weeks of post-delivery support so your engineers can maintain and extend everything independently.
Python-first. We build on edgartools (our open-source library), with production deployments typically using PostgreSQL, Redis, and cloud infrastructure (AWS, GCP, or Azure). We integrate with your existing stack — not replace it.
30 minutes. You describe your SEC data challenge, we ask clarifying questions, and we tell you exactly how we'd approach it — including which engagement type fits and a rough timeline. No pressure, no pitch deck. If we're not the right fit, we'll tell you.
The SEC Data Sprint starts at $5,000 and delivers a working prototype in 1-3 days. Compare that to hiring a data engineer ($140K+/yr) who'll spend 6 months learning EDGAR's quirks. For early-stage teams, the sprint is often the fastest path to validating whether SEC data solves your problem.
Typically within 1-2 weeks of signing. A Sprint can start within days if schedules align. We scope and price within 48 hours of the discovery call, so the only bottleneck is your availability.
Yes. We sign mutual NDAs before any technical discussion and include confidentiality clauses in all engagement agreements. We work with hedge funds and fintech companies where data sensitivity is paramount.
Book a free 30-minute discovery call, or tell us about your project. We'll respond within 24 hours.
30 minutes. Tell us your SEC data challenge — we'll tell you how we'd solve it.
Pick a timeOr email directly: hello@edgar.tools
Three fixed-price engagements. Working code in days, not months. Full source ownership.
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