PredictLeads alternatives for company signals

PredictLeads and Fokals are close in kind: both work from public sources and return what they find as dated, structured signals about companies. This guide is for the engineer or product lead who is choosing between them, or who plans to run both. It compares the two dataset by dataset on what each vendor documents, says what each is built for, explains three differences of method that change the numbers you will see, and builds the same alert on each.

The short version: PredictLeads is built for events delivered to an endpoint and for relationships between companies. Fokals is built for scored, point-in-time company data: evidence-backed intent scores, technographic and hiring datasets that are written once and never revised, and the security identifiers of every listed company on one company index.

What PredictLeads offers, as documented

The PredictLeads website describes structured company intelligence delivered through an MCP server, APIs, webhooks and flat files. It says the data is sourced by its own crawling of company websites and their subpages, such as career, product and case-study pages, and of news outlets, press sites, blogs, review sites and SEC filings.

Its documentation lists the datasets: companies, job openings, technology detections, news events, financing events, connections, similar companies, products, SEC filings, website subpages and GitHub repositories among them. The pricing page describes two routes. The API is paid by credit as you go, with a free monthly allowance. Flat files and webhooks across all datasets come under what the page calls Enterprise Access.

What each is built for

The first six lines restate the PredictLeads pages linked in this guide. The last four follow the Fokals methodology.

• Events pushed to an endpoint. PredictLeads delivers by webhook.
• Files in your storage. It exports flat files to AWS S3, Google Cloud Storage or SFTP.
• AI agents. It runs an MCP server for AI agents.
• Company relationships. It has datasets for key customers, similar companies and products.
• Media coverage and description text. Its news events are drawn from news outlets, press sites and blogs, and its job records include the full description.
• A long record. Its website dates its job and news history to 2016.
• Scores with evidence. Fokals Intent Scores give a weekly score from 0 to 100 for each company and topic, across a curated taxonomy of buying topics in nine groups, and every score carries its five strongest dated signals.
• Point-in-time research. Fokals daily and weekly datasets are written once after the period closes and never revised, under named, frozen label versions, so a model reads the same record tomorrow that it read today.
• Security identifiers. A listed company carries its ticker, MIC, ISIN, LEI and share-class FIGI, and a brand carries the identifiers of its listed parent.
• Market-level series. Market Series gives weekly series by industry, country and company size band on same-store cohorts.

Dataset by dataset

The PredictLeads column is drawn from its home page and its pages for job openings, technologies, news events and key customers. The Fokals column follows the data dictionary.

Three differences of method that change your numbers

Website evidence and posting evidence. PredictLeads says its technologies are collected from script tags, DNS records, IP ranges, cookies and job descriptions that name them as required skills. Fokals delivers the two kinds of technographic data as separate evidence. Technology Stack, in the marketing stack dataset, delivers the technologies detected on a company's website, from page content, scripts, network requests, response headers and DNS records. The tools named in a company's job postings are delivered with each role in Job Postings. A count of companies that run a tool can therefore differ between the two feeds, and on Fokals you decide whether a mention in a posting counts as use.

Media coverage and company statements. PredictLeads news events come from news outlets, press sites and blogs. The Fokals announcements dataset is first-party: company announcements and regulatory disclosures, classified by event type, so every event is the company's own statement and can be cited as such. Counts of events per company will differ between a media feed and a first-party feed, and the two answer different questions: what was reported about a company, and what the company itself put on the record.

What counts as new. On Fokals a first observation sets a baseline and is never counted as a change. A technology already in place appears in Technology Stack with its first-seen date and writes no event, and a posting already open is never counted as new, so every event in Technology Changes and every new posting in Hiring Activity is a true change. When you run two feeds in parallel, compare changes from the day after each feed first observed each company.

The same alert on each feed

Take one alert: tell an account owner when a target company adds a technology in your category. The PredictLeads documentation lists webhooks for the companies a client follows, covering job openings, technology detections, news events and connections, so the event is pushed to your endpoint.

On Fokals the alert runs on a cursor feed. The REST API has 25 endpoints and cursor pagination, and the delivery page describes it beside the bulk files. The feed of website changes runs oldest first from a time you set, so the last cursor you hold is the bookmark for an incremental sync, and a scheduled job loads what is new into your copy of Technology Changes. The alert is then a query.

select e.company_id, e.observed_at, e.technology_name, e.after
from company_tech_events e
join watched_accounts w
  on w.company_id = e.company_id
where e.category = 'technology'
  and e.change = 'added'
  and e.technology_category = :your_category
  and e.observed_at >= :last_run;

The table of watched accounts is your own. Three properties of the record shape what the query returns. A removal is confirmed before it is written, so a tag that comes and goes with a consent banner or a test stays out of the change record. Every event carries its observation time, so the bookmark neither skips nor repeats an event, and a rerun of the job returns the same rows. The marketing stack datasets are refreshed daily to weekly, and the observation time on each event states exactly when the change was recorded.

An illustrative case: Acme Robotics, an invented company, adopts an analytics tool on a Tuesday. The adoption is observed on Wednesday, the event lands in Technology Changes with Wednesday's observation time, and your job reports it on its next run, with the tool, its category and the date as the account owner's reason to call. A push feed is built for an event that must reach an endpoint as it is detected. A cursor feed is built for a scheduled job that must account for every event exactly once. The guide to building company change alerts covers deduplication and wording.

A parallel run before you choose

Two weeks of both feeds on your own accounts settle more than a table can. Four measures are worth taking.

1. Match rate. Take a few hundred accounts by website domain and count how many each feed resolves. On Fokals each domain resolves to one stable company ID, which every dataset carries.
2. Open roles. Both feeds take postings at the employer, so counts per company can be compared directly. Hiring Activity gives the Fokals count for each company and closed day, written once, so the comparison is repeatable.
3. Technologies. For twenty companies, list what each feed reports and check every entry against the live website. On the Fokals side, read Technology Stack for website detections and Job Postings for the tools named in roles, so that you compare like with like.
4. First sight. PredictLeads job records carry a URL and the time a job was first seen, and so does Job Postings. Join the two on the posting URL and compare.

-- predictleads_jobs is the other feed as you loaded it: url, first_seen_at
select
  count(*) as shared_postings,
  percentile_cont(0.5) within group (
    order by extract(epoch from (f.first_seen_at - p.first_seen_at)) / 3600
  ) as median_hours_between_first_sightings
from job_postings f
join predictleads_jobs p on p.url = f.url
where not f.found_on_first_read;

Normalise the URLs before the join. Leave out the postings that formed each feed's baseline, which Job Postings flags on the Fokals side, so that only true openings are compared. A gap of a day or so follows from refresh cadence: PredictLeads says it refreshes each opening once every 36 hours, and the Fokals hiring dataset is refreshed daily.

Other alternatives to PredictLeads

• TheirStack infers the technologies a company uses from its job postings, which it gathers from company websites, job boards and other hiring platforms. The guide to TheirStack alternatives sets that method beside website detection.
• Coresignal sells company, employee and jobs data as flat-file datasets and through APIs.
• BuiltWith reports the technologies websites are built with, and its API page offers current and historical technology data for a domain and lists of sites by technology.
• Crunchbase presents itself as private company data with predictive intelligence on funding rounds and acquisitions, with an API and data licensing among its products.

Frequently asked questions

The queries and code on this page are examples to adapt. Test them in your own environment before you rely on them.

What this page says about the products it names was checked against their public documentation on 4 October 2026. Product and company names are trademarks of their owners. Fokals is not affiliated with them or endorsed by them.