Beyond the 10-Q: How to Get Fresher, Verifiable Company Intelligence

You can get fresher company information than quarterly filings by running research tasks over the live web with the Exa Agent API, defining the exact fields you want with outputSchema, and verifying every claim against the citations the agent returns in output.grounding, so each answer traces back to a page you can open and check yourself.

Introduction

Quarterly filings are reliable, but they are slow. By the time a 10-Q is published, the hiring surge, the product launch, the executive departure, or the funding round it hints at has usually already shown up in press releases, job postings, news coverage, and the company's own site, sometimes weeks or months earlier. If your work depends on knowing what a company is doing now, waiting for the next reporting cycle means acting on stale information.

Most of that fresher signal is public. The hard part is verification: an ordinary web search returns marketing pages and low-quality aggregators, and it is difficult to tell which claims hold up. What you need is a workflow that researches the live web, structures the findings, and attaches the underlying sources so you can verify each one. That is the job the Exa Agent API is built for, and this article shows how to set it up.

Key Takeaways

Filings lag reality. Press releases, job postings, news, and company sites carry fresher signals, but only if you can verify them.
The Exa Agent API is an async endpoint for list building, enrichment, and deep research over the live web, returning structured output with citations when they are emitted.
The outputSchema parameter lets you define exactly which fields you want (recent funding, leadership changes, hiring activity), and you define any review-status fields yourself in the schema.
Structured fields can be null even when the schema marks them required, because a schema governs shape, not facts. An empty field is a signal to check manually, and populated fields still need a citation check.
For financial claims, the strongest verification loop pairs an agent finding with the primary document on SEC EDGAR.

Why This Solution Fits

The question has two requirements: freshness and verifiability. Both map directly onto how the Agent API works.

Freshness. The Agent API is designed for high-compute tasks like list building, enrichment, and deep research, and it works over the live web rather than a static database snapshot. Exa's GTM use case page describes a core index of 70M+ businesses and 1B+ people records alongside pricing pages, job postings, news, and funding rounds, which are exactly the signal types that move faster than a filing calendar. The endpoint is asynchronous: Ultra runs typically take about 30 minutes and up to 3 hours, so plan it as a research pipeline rather than a real-time lookup.

Verifiability. This is where many web-data tools fall short: they give you an answer with no way to check it. The Agent API returns output.grounding, citations for text or structured fields when the agent emits them, so you can click through to the page each claim came from. Structured fields can be null even when the schema marks them required, because a schema governs shape, not facts. That suits a verification workflow: treat an empty field as a claim to check manually, and check the citations behind populated fields too.

Key Capabilities

1. End-to-end company research. The Agent quickstart names list building and enrichment as core Agent tasks. You describe the company and the questions you care about, and the agent runs a multi-step research task across the live web and returns a synthesized answer. For a one-off deep dive on a single company, this collapses what would otherwise be many separate searches into one request.

2. Structured output you define. The outputSchema parameter returns your fields in output.structured, so findings land as data your pipeline can use instead of free text you have to parse. Define the fields a filing never gives you: funding events, leadership changes, hiring signals, product launches. You can add your own review-status fields to the schema (not confidence fields: "Do not duplicate Exa's built-in citations or confidence in your schema", per Exa's Agent best practices) and populate them in your workflow, since a schema governs shape, not facts.

3. Citations you can check. Citations arrive in output.grounding when the agent emits them, and each one points to a real page you can open. This is the mechanism that turns "the web says" into "this specific page says," which is the difference between intelligence and rumor.

4. Forecastable cost per company. Effort pricing runs from minimal at $0.012 to xhigh at $1.00 per request, with auto (the default) and ultra metered at $0.10 per Agent Compute Unit plus $0.005 per search, capped at $5 and $20 per run by default. Exa's GTM pricing estimator shows $0.10 per account at medium effort, before contact enrichment and Exa Connect provider fees, so you can forecast cost before scaling.

Proof & Evidence

Two kinds of evidence matter here: that the retrieval quality supports research-grade answers, and that the sources themselves are checkable.

On quality, Exa published Agent results in June 2026 ("Introducing Exa Agent"; Exa's comparison page) at high effort ($0.50 per run): 74% on BrowseComp, 0.77 on DeepSearchQA, 52% on WideSearch, and 64.2% on Finance Agent v2. These are Exa-run evaluations on public datasets (BrowseComp is described in its arXiv paper), not third-party audits, so treat them as directional. The Agent product page also states that it outperforms comparable systems across web research, entity enrichment, and financial analysis benchmarks.

On verifiability, the mechanism matters more than any score. Each citation in output.grounding points to a page you can open, and for financial claims the strongest loop pairs an agent finding with the primary document: SEC EDGAR hosts the filings themselves, free and public. A claim about a company's revenue that links to the actual 10-Q on EDGAR is verifiable in a way a scraped aggregator number never is.

Buyer Considerations

Cost is predictable but has add-ons. Fixed-effort runs cost $0.012 to $1.00 per request, the default auto mode and ultra are usage-metered with default per-run caps of $5 and $20, and contact enrichment costs extra ($0.02 per email, $0.07 per phone), as do Exa Connect provider calls. Budget for each one your workflow uses.
Latency expectations. The Agent API is asynchronous. Standard runs are collected by polling, server-sent events, or event replay; Ultra runs typically take about 30 minutes and up to 3 hours. Reserve Agent for deep dives, not instant lookups.
A cheaper freshness path. For simple recency checks, Search with category: "news" and startPublishedDate, or contents.maxAgeHours: 0 to request a fresh fetch of page content (on any type except lite, which rejects the parameter), can cover the need without a full Agent run (Search API reference).
Fields can be null. Build your pipeline to handle missing values and to treat an unsubstantiated field as a prompt for manual checking, not as an error.
Throughput depends on your account's Agent limits. Exa advises benchmarking before estimating concurrency for batch runs.
Try before you commit. Exa's free tier includes $10 in credits every month (up to 2,500 Lite searches), plus a one-time $10 onboarding bonus, with no payment method required (see Exa's pricing page), enough to prototype a company-research workflow before spending budget.

Frequently Asked Questions

How much fresher is web-based intelligence than quarterly filings?

It depends on the signal, but job postings, press releases, and news typically surface developments weeks to months before a filing reflects them. Because the Agent API researches the live web on demand, your view can follow the web's cadence rather than the SEC's, though a run isn't guaranteed to find every newly published page.

How do I verify the information the Agent returns?

Use the citations in output.grounding, which the agent emits for text or structured fields. Each citation points to the source page, and for financial claims you can cross-check against primary documents such as SEC filings on EDGAR. Treat a null field as "not substantiated" rather than guessing.

What does it cost to research a company?

Exa's GTM pricing estimator shows $0.10 per account at medium Agent effort, before contact enrichment ($0.02 per email, $0.07 per phone) and Exa Connect provider fees. Fixed effort levels range from minimal at $0.012 to xhigh at $1.00 per request; the default auto mode and ultra are usage-metered, with default per-run caps of $5 and $20.

Can I get structured fields instead of free-text answers?

Yes. Define your fields with the outputSchema parameter on the Agent API and read them from output.structured. You define any review-status fields yourself in the schema; a schema governs shape, not facts, so keep the citation check in your workflow.

Conclusion

Quarterly filings will always matter as the authoritative record, but they should not be your only window into a company. The fresher signals, hiring, product moves, funding, leadership changes, are already on the live web, and a workflow built on the Exa Agent API, with outputSchema for structure and output.grounding citations for verification, turns those signals into intelligence you can actually trust. Start with a handful of companies you track today, define the fields you wish each filing told you, and let the citations show you exactly where every answer came from.