10 Ways Companies Can Use PredictLeads Similar Companies Dataset

November 20, 2025 / Robert Fon

PredictLeads Similar Companies Dataset identifies companies that closely resemble another company. This is done based on domain patterns, digital presence, and behavioral signals. This makes it a powerful engine for targeting, prioritization, and market mapping. Using the Similar Companies Dataset for targeting and market mapping ensures precise alignment with business goals.

Below are ten practical applications of the Similar Companies Dataset for targeting and market mapping.

1. Build High-Precision Lookalike Lists

Teams can generate lookalike targets based on actual similarity, not guesswork, effectively utilizing the dataset to conduct similar companies targeting and market mapping. This improves targeting accuracy and reduces time spent on low-fit accounts.

2. Strengthen Ideal Customer Profile (ICP) Development

By examining similarity clusters around top customers, teams can better define what a strong-fit company actually looks like, backed by data rather than opinion, aiding similar companies targeting and market mapping efforts.

3. Improve Lead Scoring and Prioritization

A high similarity score can be used as a ranking factor. Leads that resemble existing best customers automatically surface to the top, leveraging a similar companies dataset approach for market mapping and effective targeting.

4. Identify New Market Segments

Similarity patterns often reveal adjacent groups of companies teams may not be monitoring. This helps GTM teams explore new verticals or sub-segments with lower risk, thanks to the insights from a similar companies dataset.

5. Fuel Account-Based Marketing Campaigns

ABM works when target lists are highly focused. Lookalike companies help build Tier 1, Tier 2, or Tier 3 account lists that mirror winning customer profiles, benefiting directly from a similar companies dataset used in market mapping.

6. Accelerate TAM and Market Mapping

Similarity clusters provide a quick way to understand how a market is structured. Companies that group together often share needs, tools, and workflows. This enhances targeting strategies with similar companies datasets applied for effective market mapping.

7. Improve Competitive Research

Teams can analyze which companies resemble a key competitor and identify emerging entrants or indirect competitors that share similar characteristics, utilizing a similar companies dataset for better mapping of the market landscape.

8. Support Product-Led Growth Targeting

PLG teams can identify companies similar to their most active or highest-converting users; as a result, they can more effectively target those accounts for activation, onboarding, or upsell campaigns. Additionally, the Similar Companies Dataset becomes a crucial element in this broader market strategy.

9. Surface High-Potential Accounts Overlooked in CRM

Many strong prospects never get touched simply because they do not fit old filters. Lookalike analysis can reveal accounts hiding in plain sight, which can be targeted effectively using a similar companies dataset for precise market mapping.

10. Help Investors Find Deals Faster

VCs and investors can generate lookalikes for any strong-performing portfolio company. This produces instant deal pipelines of companies with similar traits and traction signals through the innovative use of a similar companies dataset targeting market dynamics.

Get started with the PredictLeads Similar Companies Dataset and unlock powerful insights for sales, marketing, GTM, and investment teams.
The dataset curently covers 18.5+ million companies and draws from all PredictLeads datasets, giving you a unified view of similarity based on real digital and behavioral signals.

Explore the full documentation here:
https://docs.predictleads.com/guide/similar_companies_dataset

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Real-Time Data Personalization & How it Improves Cold Outreach

November 18, 2025 / Robert Fon

Real-Time Data Personalization isn’t a buzzword but the foundation of truly relevant cold outreach. Most sales emails today pretend to be personal, but the timing is off. The message doesn’t match what the company is doing right now, which is why responses are low even when messaging is “customized.”

This article explains how real-time job openings and real-time news events create the context that makes outbound feel natural instead of random. When outreach reflects what’s actually happening inside a company, the message doesn’t just stand out but also benefits from effective personalization based on real-time data.

To go deeper into how PredictLeads structures this data, you can explore our documentation.
PredictLeads Docs

*News event data powering real-time outreach personalization*

Jobs Reveal What Companies Are Building Right Now

New job openings are one of the strongest real-time signals in B2B. When a company posts a role, it tells you exactly where they’re investing:

A team they’re scaling
A capability they lack
A bottleneck they’re preparing to solve
A geography they’re entering
A project they’re kicking off

Instead of generic outreach (“We help companies like yours…”), Real-Time Data Personalization lets you write outreach that reflects this immediate shift.

Example:
If a company suddenly opens several engineering or ops roles in one week, you know they’re getting ready up for a buildout (even before they say anything publicly.)

News Events Explain Why Those Roles Exist

Job data shows the what while News data shows the why.

Expansion announcements, new partnerships, funding rounds, layoffs, product launches. All these events offer context for the operational changes seen in job openings, allowing for real-time data personalization.

A company expanding into a new market?
You’ll see hiring in that region.

A company signing a large enterprise customer?
Support or onboarding roles usually appear.

A company restructuring?
Reductions in one function may be paired with increased hiring in another.

News events transform cold outreach from “I hope this resonates” into “I saw what’s happening, and here’s how I can help.”

For additional context categories, see this external guide.
News Events Categories

The Advantage Comes From Combining Both Signals

Real-time data personalization gets its power from aligning both signals:

Jobs → operational direction
News → strategic explanation

Together, they give you a timeline of what’s happening inside the company, enabling a seamless connection through data-driven personalization.

Expansion → hiring spike → operational strain → perfect outreach moment.
Funding → engineering growth → new product sprints → perfect outreach moment.
Layoffs → efficiency focus → consolidation → perfect outreach moment.

This context isn’t guesswork. It’s watching a story unfold in real time.

What Outreach Sounds Like When It’s Truly Contextual

Instead of generic lines like:

“Wanted to reach out because we help companies like yours…”

You write:

Expansion + hiring
“Saw you’re expanding into Ghana and opening several Ops and Support roles. Teams usually run into X during the first 90 days… & here’s how others manage it.”

Funding + engineering growth
“With the recent funding announcement and backend hiring spike, it looks like the engineering team is preparing for new product cycles. Here’s how others speed up Y during this stage.”

Layoffs + targeted hiring
“Saw the reductions in X but continued hiring in Y. That typically signals a shift toward efficiency. Here’s what’s working in similar transitions.”

This is how personalization in real-time data works in practice.

Automating the Workflow

Implementing this doesn’t require a complex stack:

Fetch new jobs daily
Fetch relevant news events daily
Link them by company
Trigger outreach based on time proximity or categories
Push dynamic messaging into your outbound tool

PredictLeads’ schema is built in a relational way, so combining these signals is straightforward.

Why It Works

Personalization isn’t about writing someone’s name twice.
It’s about reflecting a company’s real-world situation with accurate data in real time.

Real-Time Data Personalization creates relevance, and relevance is what makes outreach convert.

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5 AI Agents you can connect with PredictLeads to automate smarter (and skip the boring stuff)

November 6, 2025 / Robert Fon

Most automation tools are only as good as the data you feed them. PredictLeads focuses on providing that missing piece – clean, structured company data that can actually make automations useful. The integration with AI automation tools offered by PredictLeads allows you to surface things like job openings, tech stacks, funding events, and company news, so your workflows can react to what’s happening in real-time. Whether you’re using APIs or no-code integrations, PredictLeads helps you gain valuable insights.

You can connect PredictLeads to your favorite AI agents and automation tools such as Activepieces, n8n, Make.com, Zapier, and Bardeen.ai to make your workflows actually smart, not just automated.

Example of an automated workflow combining PredictLeads data with OpenAI and Google Sheets through Activepieces.

1. Activepieces

If you haven’t tried Activepieces, think of it as open-source Zapier that’s simple and powerful.

The new PredictLeads integration lets you pull company insights and trigger actions across hundreds of apps. You can:

Enrich CRM records when a new company domain shows up.
Post in Slack when one of your tracked companies adds several new job openings.
Notify your sales team when PredictLeads detects a new funding event using PredictLeads integration with AI automation tools.

Available PredictLeads actions:

List Companies
List Job Openings
Get Company by Domain
Retrieve Companies by Technology
Get News Event
List Company News Events
List Technologies by Domain
List Connections
Make Custom API Calls

You can start experimenting with it directly on Activepieces. No code, no setup pain.

2. n8n

n8n is great when you want more logic and control in your automations.

This tool allows for PredictLeads integration with AI automation features to blend seamlessly with CRMs, Slack, Google Sheets, or your custom systems.

Example ideas:

Automatically find companies hiring for “AI Engineers” and send them to your CRM.
Get alerts when portfolio startups start scaling their teams.
Filter PredictLeads data to show only companies that match your target tech stack.

n8n is for those who like to see the inner workings of their automation instead of just hitting “run.”

3. Make.com

Make.com (formerly Integromat) is perfect if you prefer visual workflows.

By connecting PredictLeads, you can:

Pull new job openings, check if they fit your ICP, and push them into your CRM.
Watch for technology changes like new marketing tools detected on company websites.
Create a live dashboard that tracks companies hiring for data roles in your target region through PredictLeads integration with AI automation tools.

Make.com turns PredictLeads data into visual, flowing automations that are easy to understand.

4. Zapier

Zapier might be the old classic, but it’s still the easiest starting point for most.

You can set up simple PredictLeads automations such as:

Adding new job openings to Google Sheets.
Sending outbound leads to Notion when they meet specific filters.
Getting Slack notifications when a company is mentioned in PredictLeads News Events with the advantages of PredictLeads integration with AI automation tools.

Zapier works great when you want to get started quickly and don’t need complex logic.

5. Bardeen.ai

Bardeen.ai is an AI agent that automates your browser.

Combine it with PredictLeads data and you can:

Scrape company lists from the web and enrich them instantly.
Build prospect lists based on who’s hiring and send them into your CRM.
Write personalized outreach messages using PredictLeads company data integrated with AI automation tools.

It’s the easiest way to use PredictLeads data directly from your browser while staying in flow.

TL;DR

PredictLeads gives you the data.
Activepieces, n8n, Make, Zapier, and Bardeen give you the automation.

Put them together and you can:

Build lead lists automatically.
Track hiring trends across your ICP.
Get alerts before competitors do.
Automate the parts of prospecting that nobody enjoys.

If you want to test it out, check the PredictLeads integration on Activepieces or dive into the full API docs at docs.predictleads.com/v3

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How Marketing Teams Can Use Technology Data to Spend Less and Convert More

October 29, 2025 / Robert Fon

Most marketing budgets are spent on the wrong companies.
Teams define their audience by industry, size, or location, but those filters don’t tell you much about whether a company is actually a fit.

Two businesses can look identical on paper and still be worlds apart in how they operate.
One might have a modern stack built around HubSpot and Stripe.
The other could be using outdated tools that don’t connect with anything.
Both will appear as “software companies,” yet only one can realistically buy what you’re selling.

This is where data about a company’s technology usage becomes useful. It helps you see what’s underneath the surface.

Understanding Technology Stack Insights

Every company leaves small digital traces of the software it uses.
These traces appear on websites, subdomains, job descriptions, and DNS records.
When you combine those signals, you can build a reliable picture of a company’s technology stack.

PredictLeads tracks a billion of these detections across more than sixty million companies.
Each detection shows which tool a company uses, when it was first seen, and when it last appeared.
Over time, this data forms a clear timeline of how that company’s tools change and evolve.

For marketers, that view is valuable because it lets you stop guessing.
You don’t need to assume who your product fits.
You can filter for companies that already use related technologies or competitors.

Technology data showing patterns across company stacks.

Better Targeting Starts with Simple Filters

Say you’re marketing software that integrates with Salesforce.
With technology data, you can instantly filter for companies that use Salesforce, HubSpot, or Pipedrive.
Now every company you contact is technically ready to use your product.

If you’re running paid campaigns, you can exclude everyone else.
That means less wasted budget and a smaller but more accurate audience.

Instead of spending $10,000 on 10,000 random clicks, you might spend the same amount reaching 2,000 companies that actually have a chance to convert with adopting a data-driven marketing approach.

Making Segmentation Practical

Technology data can improve more than ad targeting.
You can use it to refine email lists, prioritize leads, or adapt your messaging.

If your data shows that a company recently added a tool your product connects to, you can reach out with something relevant to that setup.
If another company is using an older competitor, you can adjust the message toward migration.

These are small shifts, but they make communication feel informed rather than generic.
Instead of another “we help SaaS teams grow faster” email, you can send a message that clearly fits the company’s environment.

Reducing Spend and Improving Conversion

When campaigns reach the right people, costs naturally go down.
With data-driven marketing you spend less per qualified lead, and the leads you do attract are more likely to move forward.

Marketing metrics improve not because of better creative or higher budgets, but because the audience is better defined.
Sales teams waste less time chasing mismatched prospects.
Both departments work with cleaner data and clearer signals.

What This Looks Like in Real Life

A small team used PredictLeads’ Technology Detections dataset to focus on companies already using Stripe and Segment.
Their product connected directly with both tools, but before this change, most of their leads came from companies using completely different systems.

After applying the filters, the number of leads dropped by more than half.
However, their conversion rate tripled, and the average deal size increased.
They didn’t expand reach — they focused it.

A Simpler Kind of Data-Driven Marketing

There’s a lot of talk about data-driven marketing and technology stack insights, but in practice it often means adding more dashboards and complexity.
Technology usage data is the opposite.
It’s simple context since it’s a way to understand who can actually benefit from what you sell.

The best part is that you don’t need to change your entire marketing system.
You can enrich your existing CRM or lead lists with technology data and start filtering immediately.
It works quietly in the background, supporting the tools you already use.

Final Thoughts

Marketing becomes more effective when you stop treating every company as a potential customer.
Technology stack insights help narrow the focus to businesses that already have the systems, integrations, and maturity level to use your product.

You don’t need to guess who’s a fit anymore.
You can see it.

And once you see it, everything from ad spend to conversion rate starts to improve — not through growth hacks or new tools, but through better understanding of the companies you’re trying to reach.

Got a question? Our team at PredictLeads will be happy to help.

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How to Choose a Historical Data Provider?

October 21, 2025 / Robert Fon

Choosing a historical data provider comes down to coverage, timestamp fidelity, lifecycle tracking, provenance, and licensing fit. PredictLeads provides time-stamped company signals such as Job Openings, Technology Detections, News Events, Financing Events, and Vendor/Partner/Investor Connections. Each record includes granular first_seen, last_seen, found_at, and published_at fields, along with rich categories. The data is delivered through APIs, FlatFiles and webhooks, which makes it easy to build reproducible backtests, ICP models, and RevOps playbooks.

Why a “historical” view matters (and what it is not)

If you’re evaluating historical data for B2B go‑to‑market, investing, or partnerships, your goal isn’t tick‑by‑tick market feeds. It’s who did what, when, and for how long. E.g., when a company started hiring for a role, when a technology first appeared on their site, when a partnership was announced, or when a funding round was published. That requires:

Event‑level timestamps that support causal analysis (e.g., jobs spike → outreach → meeting → opportunity).
Lifecycle states so you can see what’s active now and what existed in the past (avoid survivorship bias).
Provenance so every signal is explainable and defensible (source URLs, categories, and context).

For GTM decisions, event recency and duration usually matter more than intraday speed. If you can align a first_seen_at with an action you took, you can attribute lift.

The evaluation framework

1) Coverage & provenance

Ask: Which signals and geographies are covered? Can I inspect source URLs and confidence? Are categories normalized?

PredictLeads coverage (examples):

Job Openings: titles, categories (incl. O*NET mapping), location, salary fields, first_seen_at/last_seen_at, active/closed flags.
Technology Detections: tech name, version where available, first_seen/last_seen, subpage context, optional behind‑firewall hints.
News Events: normalized categories (e.g., acquisitions, partnerships, launches, headcount, expansions, awards), found_at, linked article URL.
Financing Events: amounts, round types, investors, first_seen_at.
Connections: normalized relationship types (vendor, partner, integration, investor, parent, rebranding, published_in, badge, other).

2) Timestamp fidelity & auditability

History is useful only if you can trust when things happened. Prefer datasets with event‑level timestamps (e.g., first_seen_at, last_seen_at, found_at, published_at) and clear rules for “active,” “closed,” and “deleted.” Distinguish source publish time from discovery time for honest backtests.

3) Granularity & lifecycle tracking

Look for record lifecycle: created → updated → closed/deleted. For hiring, you’ll want active/closed and last_seen_at to infer fill times; for tech adoption, you want first_seen and last_seen to understand churn and stickiness.

4) Normalization & enrichment

Categories unlock use cases: job families (Sales vs Eng), O*NET for role families, news event categories, connection types, and financing round types. Normalization reduces your downstream modeling effort and boosts precision.

5) Delivery & operational fit

API, webhooks or flat files. Prefer JSON/REST with clear pagination, idempotent endpoints, rate‑limit headers, and meta.count. For batch, support for incremental windows (e.g., found_at_from), and stable IDs.

6) Legal & licensing

Clarify whether you can: use data in internal models, trigger outreach, share derived analytics, or redistribute subsets. Ensure the license reflects your actual workflows.

How PredictLeads maps to the checklist

Job Openings

Fields: title, categories, onet_code, location_city/country, salary_low_usd/salary_high_usd, first_seen_at, last_seen_at, active_only, not_closed.
Uses: hiring intent, geo expansion, seniority mix, comp banding, time‑to‑fill.

Technology Detections

Fields: technology_name, subpage, confidence_score, first_seen, last_seen.
Uses: tech adoption, competitive intel, ecosystem scoring.

News & Financing Events

Fields: category (partners_with, launches, acquires, increases_headcount_by, expands_offices_to/in, raises_funding), found_at, published_at, amount, round_type.
Uses: intent, timing outreach, portfolio scouting.

Connections (vendor/partner/investor)

Fields: relationship_type (vendor, partner, integration, investor, parent, rebranding, published_in, badge, other), source_url, first_seen_at.
Uses: partner ecosystem maps, channel strategy, integration‑led growth.

Why this matters: With continuous first_seen/last_seen and strong categories, you can write reproducible rules like: Companies with ≥3 new engineering roles in the last 14 days AND a newly detected HubSpot integration → high‑priority outreach.

Example playbooks

1) Hiring momentum filter

Pull last 90 days of engineering jobs for a domain list with active_only=true.
Aggregate by domain/week; keep domains with ≥5 new roles/week and salary_low_usd ≥ X.
Join with Technology Detections (e.g., Salesforce, HubSpot, Snowflake) for stack fit.

Outcome: A short‑list of fast‑growing, ICP‑fit accounts with concrete talking points.

2) Partner ecosystem map

Query Connections for relationship_type in [vendor, partner, integration].
Rank vendors by breadth and first_seen_at recency.
Enrich with News Events for fresh announcements to personalize outreach.

Outcome: Find co‑sell angles and integration‑led ABM plays.

3) Expansion alerts

Listen to News Events for expands_offices_to/in or increases_headcount_by.
Cross‑check Job Openings spikes in those geos.
Route accounts to reps by territory; trigger sequences with geo‑specific messaging.

Outcome: Time outreach to moments of budget and urgency.

Common traps (and how PredictLeads addresses them)

Survivorship bias: Only looking at what’s live today hides closed roles and churned tech. PredictLeads tracks historical states and last_seen timestamps.
Opaque provenance: Without source_url, confidence, and page context, you can’t justify a signal. PredictLeads links back to sources and captures context.
Schema drift & rework: Hand‑built normalizers break. PredictLeads ships normalized categories (job families, news types, relationship types) to cut integration time.

Implementation blueprint (90‑minute setup)

Pick signals: Start with Jobs + Tech + News for your ICP.
Define windows: e.g., found_at_from last 30/90 days; keep active_only where applicable.
Build joins: Domain key across signals; keep first_seen/last_seen fields in your warehouse.
Score rules: Combine recency (days since first_seen), volume (event count over 7 or 14 days), and context (technology stack fit or partner relevance).
Route & measure: Push scored accounts to CRM, track meetings/opps sourced.

Conclusion

Historical data that drives revenue must be explainable, time-stamped, and normalized. PredictLeads focuses on the company‑level events that matter. Look for who’s hiring, adopting tech, partnering, raising, launching, and changing their site. Such timestamps and lifecycle states you need to trust your models and take action.

Ready to see your history‑powered pipeline?
• Explore the API docs: https://docs.predictleads.com/guide
• Ask us for a sample: https://predictleads.com/#demo

About PredictLeads

PredictLeads indexes 98M+ companies and delivers normalized, time‑stamped signals to help GTM and investment teams find and act on buying windows. We provide APIs, webhooks, and flat files; therefore, you can wire signals directly into your workflows.

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How Consultants Can Use PredictLeads’ Key Customers Dataset for Competitive Advantage

October 14, 2025 / Robert Fon

In the consulting world, understanding a client’s ecosystem is everything. Whether you’re advising on growth strategy, market positioning, or partnership opportunities, your recommendations are only as good as the data behind them. That’s where PredictLeads’ Key Customers Dataset comes in – providing an unparalleled window into the companies your clients rely on and the ones relying on them.

What Is the Key Customers Dataset?

The Key Customers Dataset identifies which companies are customers of which. It surfaces business relationships – for example, which firms use HubSpot, AWS, or Snowflake – based on verified digital evidence such as logos, case studies, testimonials, job posts, or partner listings.

Each record helps you see:

Who buys from whom
The type and depth of the relationship
When the connection was first detected or last updated

This dataset connects millions of companies globally, revealing commercial dependencies that often go unnoticed in traditional research.

Logos of companies using PredictLeads data, including Dealroom, Clay, and FactSet.

Why It Matters for Consulting Firms

Consultants thrive on context. Understanding a client’s customer and partner landscape allows for sharper insights, faster audits, and more targeted recommendations. Here are some specific consulting use cases:

1. Market Mapping & Competitive Benchmarking

By analyzing the customers of your client and their competitors, you can identify:

Which verticals or regions your client underperforms in
The industries that competitors dominate
Emerging players gaining traction with shared customers

For instance, if your client competes with HubSpot, you could analyze thousands of its key customers to uncover underserved segments or new partnership opportunities.

2. M&A Target Screening

When evaluating acquisition targets, consultants can quickly assess:

Overlap or synergy between customer bases
Potential cross-sell opportunities
Concentration risk (e.g., 70% of revenue tied to one customer cluster)

This reduces manual research and brings data-driven precision to strategic decision-making.

3. Customer Retention & Expansion Planning

For growth-focused consulting, understanding a client’s current customers (and who else they buy from) enables tailored expansion strategies.
Example: If a SaaS client’s customers also use 3–4 competing platforms, that’s a signal to strengthen retention tactics or upsell integrations.

4. Partner & Ecosystem Strategy

Advisors helping clients build alliances or reseller programs can identify:

Which companies have overlapping customer ecosystems
Where indirect partnerships already exist through shared clients
Which verticals offer the strongest growth potential

Example: Turning Data into Strategy

Imagine a consulting firm advising a cybersecurity company. Using PredictLeads’ Key Customers Dataset, the consultant identifies that most of their top customers are also working with AWS and Snowflake … suggesting an opportunity to develop integrations or co-marketing campaigns within those ecosystems.

In another case, the dataset could reveal that a client’s competitor just signed multiple fintech customers in Southeast Asia, hinting at regional momentum worth investigating.

Why PredictLeads?

PredictLeads doesn’t just collect data but it maps business relationships across millions of verified signals.
The Key Customers Dataset integrates seamlessly with other PredictLeads datasets (e.g., Job Openings, Technologies, or News Events), allowing consultants to layer insights:

Who are a company’s biggest customers?
What technologies do they use?
Are they hiring for new markets or functions?

Together, these datasets paint a holistic picture of where your client stands – and where they can move next.

Final Thoughts

For consulting firms, the Key Customers Dataset transforms relationship intelligence into strategic foresight. Instead of relying on assumptions or fragmented public data, consultants can now map entire customer ecosystems, quantify competitive positions, and identify actionable growth paths – all with data that’s refreshed continuously.

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Hydrogen is hiring: what the PredictLeads Jobs dataset says about sector health in 2025

October 2, 2025 / Robert Fon

If you want to know whether a sector is actually moving, don’t start with hype – start with hiring. We used the PredictLeads Jobs dataset (last 3 months) across leading hydrogen names to “nowcast” sector health. The takeaway: deployment is real, and it shows up in job titles first.

TL;DR

The PredictLeads Jobs dataset shows strong and recent hiring activity at major hydrogen companies, particularly in roles connected to deployment such as field and service positions, manufacturing, and engineering.
External market signals are consistent with what the hiring data reveals. The Global X Hydrogen Exchange Traded Fund (ticker symbol HYDR) has risen in 2025, reflecting investor optimism in the hydrogen sector. The International Energy Agency reports that hydrogen demand continues to grow and that there has been a wave of projects reaching the stage of Final Investment Decision, where companies formally commit capital to build. In parallel, the European Union Hydrogen Bank is providing funding for additional renewable hydrogen production capacity.
Falling interest rates are providing a supportive backdrop for capital-expenditure-intensive technologies such as hydrogen. The European Central Bank reduced its benchmark interest rate by 25 basis points in both March 2025 and April 2025, and the United States Federal Reserve lowered its policy rate in September 2025.

From job ads to energy shifts: What hiring tells us about the future of hydrogen.

What the PredictLeads Jobs dataset shows (last 3 months)

Air Liquide, Bloom Energy, and Plug Power are the backbone of current hiring:

Air Liquide: Fresh postings spike into September – a classic “projects greenlit → staff up” seasonality you expect when deployments move.
Bloom Energy: Steady month-over-month momentum. Stack R&D + manufacturing roles show factories and product lines scaling.
Plug Power: Heavy field & service footprint (commissioning, technicians, sustaining). That’s boots-on-the-ground work (aka real deployments).

Across companies, the role mix skews toward:

Field & Service → signal of installs, commissioning, and uptime SLAs.
Manufacturing → signal of throughput and factory capacity.
R&D & Engineering → ongoing stack, electrolyzer, and balance-of-plant improvements.

Why this matters: when a sector shifts from “talk” to “deploy,” job titles change first. The PredictLeads Jobs dataset is the fastest way to catch that turning point.

External confirmation the sector is moving (beyond our dataset)

Market proxy — HYDR ETF. The Global X Hydrogen ETF is up in 2025 on common trackers. That doesn’t prove revenues company-by-company, but it’s a clean risk sentiment read that aligns with our hiring picture.

IEA’s 2025 view – The IEA Global Hydrogen Review 2025 reports demand rising to ~100 Mt in 2024 and highlights 200+ FIDs through end-2024, i.e., a pipeline that naturally pulls hiring in engineering, manufacturing, and service. Growth is uneven, but the trajectory and investment signals are there. (FID being Final Investment Decisions)

EU Hydrogen Bank funding. The second auction drew strong interest and awarded ~€1 billion to 15 projects across the EU – another “real money → real people” link that matches the roles we see in the Jobs dataset.

Why rate cuts matter (and help what we’re seeing in the jobs data)

Hydrogen projects are capital-intensive. Lower rates improve project IRRs and make financing/offtake less painful. In 2025:

ECB reduced interest rates by 25 basis points in March and again in April which shows support for EU project finance.
Fed delivered its first 2025 cut in September – a broader risk-on nudge that tends to help thematics like H₂.

How to use the PredictLeads Jobs dataset like a pro

Steal this mini-playbook:

Nowcast sector health
Build a simple monthly postings index for a curated “Hydrogen 20” basket. Watch the mix shift from R&D → Field/Service/Manufacturing to know when deployments ramp.
Commissioning heatmap
Filter titles for “field”, “service”, “commissioning”. Map locations to see where projects are turning on. Use it for partner targeting and on-the-ground ops.
Capacity & supply chain
Track manufacturing roles (operators, line leads, welders). That’s your proxy for throughput and vendor demand coming down the chain.
Talent & wage checks
When ranges are present, parse & annualize to benchmark pay (useful for staffing, contractors, and budgeting).
Bridge to markets (optional)
Overlay your postings index with HYDR monthly returns and test 0–3-month lags. Hiring responds slower than prices, but the direction should rhyme if you’ve got the basket right. (The widget above lets you keep an eye on HYDR in real time.)

Bottom line

Hiring is one of the cleanest early signal we have. In hydrogen, the PredictLeads Jobs dataset shows the shift from “talk” to deploy: more field/service, more manufacturing, steady engineering. That’s what real projects look like from the inside.

Who we are (and why this works)

PredictLeads is a data provider focused on commercial signals (Jobs, News, Technologies, and more) delivered via API, FlatFiles and webhooks so you can plug insight directly into your models, decks, or ops. No platform to learn, just the data you need.

If you’re exploring hydrogen (or any sector where deployment beats hype) use the PredictLeads Jobs dataset as your lead signal.
Docs: https://docs.predictleads.com/v3

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Why Companies Rely on PredictLeads Data for Accuracy Instead of LLMs

September 29, 2025 / Robert Fon

Large Language Models (LLMs) are great at generating text, but when it comes to sourcing accurate, complete, and scalable company data for sales, they fall short. That’s why leading sales teams, investment firms, and go-to-market platforms rely on PredictLeads for reliable company data. When comparing PredictLeads company data vs LLMs, PredictLeads clearly outperforms in accuracy and completeness. Therefore, the debate around PredictLeads company data vs LLMs tends to favor PredictLeads for its precision.

We provide verified, structured, and instantly available datasets that make LLMs more powerful — instead of trying (and often failing) to have them collect the raw data themselves.

Here’s why the choice of PredictLeads company data vs LLMs can impact your workflow’s effectiveness:

1. Accuracy You Can Trust

Companies choose PredictLeads because our data is factual and verified at the source. LLMs, when tasked with crawling and extracting data, can misinterpret, skip over important details, or even hallucinate results. PredictLeads ensures your workflows run on solid, reliable inputs by leveraging detailed PredictLeads company data.

2. Complete Data, Not Just a Subset

LLMs often capture only fragments of information. For example, Tesla is hiring for 4,100+ positions right now. An LLM may return just a few dozen roles — sometimes only 3% of the total. That means missing critical senior or C-level positions that reveal Tesla’s strategy.

With PredictLeads, you get the entire dataset upfront and can filter for the insights that matter most, emphasizing the advantage of company data vs LLMs.

3. Breadth of Sources Beyond the Obvious

LLMs are limited to surface-level results, typically pulling from a company’s own website. PredictLeads scans across 100+ million company websites, surfacing signals like:

Case studies companies publish with partners
Emerging hiring trends
Strategic announcements beyond the press releases

For instance, while an LLM might only capture what NVIDIA says about itself, PredictLeads uncovers what other companies are saying about working with NVIDIA — a much broader and more valuable picture, highlighting the advantage of choosing PredictLeads company data over LLMs.

4. Instant Results, No Waiting Around

When speed matters, PredictLeads delivers. LLMs can take over a minute to fetch and process all open roles or case studies for a company. That’s a non-starter for busy sales reps or analysts.

PredictLeads data is already structured and available via flat file exports or integrations. Queries return results in milliseconds — fueling workflows without delay, proving efficiency in the PredictLeads company data vs LLMs comparison.

5. Built to Fit LLM Workflows

Even the best LLMs struggle with large amounts of raw data. A single case study might run 15,000+ characters. Feeding an LLM dozens at once causes context window overload and hallucinations.

PredictLeads provides concise summaries (~300 characters) of case studies, partnerships, and events. This means your LLM agents can handle more inputs, connect dots faster, and produce more accurate insights, making the company data vs LLMs discussion lean towards PredictLeads.

6. Beyond Enrichment in Clay

Our data is available in Clay if you already know which company domains you want to enrich. But most companies rely on us directly because we provide:

The full dataset of some 100M+ companies (including ones you haven’t identified yet)
Historical exports to track changes over time
Additional fields like timestamps and confidence scores not included in Clay

This makes PredictLeads not just an enrichment tool — but a data foundation for growth and investment strategies, illustrating the importance of selecting PredictLeads company data vs LLMs.

Why Companies Rely on PredictLeads company data

At the end of the day, companies don’t want their LLMs wasting time and compute on incomplete or unreliable data gathering. They want their LLMs focused on analysis, insights, and execution.

That’s why they rely on PredictLeads — to provide structured, factual, and scalable datasets that make LLMs (and the teams using them) perform at their best. Thus, the effectiveness of PredictLeads company data vs LLMs is evident in their performance.

Interested in exploring how PredictLeads can fit your workflow? Let’s set up a quick call.

Interested in our docs? Here they are:)!

Large Language Models (LLMs): Powerful for generating insights, but not built for sourcing accurate and complete company data.

Introducing Key Customer Data

Product

Unlock Business Insights with the Clay + PredictLeads Integration

How AI Agents Use the News Events Dataset to Power Smarter Sales

September 11, 2025 / Robert Fon

There’s a lot of talk about AI agents right now. Some see AI agents powered by News Events dataset as futuristic assistants, others as overhyped chatbots in disguise. The truth lies somewhere in between: AI agents are becoming practical tools for sales teams, and what makes them useful isn’t just the AI itself — it’s the data feeding them.

AI agents powered by News Events dataset are utilizing the News Events dataset effectively. One dataset that’s proving especially powerful here is the News Events dataset.

Every headline hides an opportunity — the key is knowing which ones matter.

Why AI Agents Need Real-Time Signals

An AI agent without fresh data is basically a parrot. It can mimic patterns, but it won’t know when your prospect just raised a Series B, or when your competitor opened a new office in London. That’s where the PredictLeads News Events dataset steps in.

Since 2016, it has processed millions of blogs, press releases, and articles, surfacing structured signals like:

A company receives financing
A new executive hire or departure
A competitor launches a product
A business expands into a new region

Instead of raw news headlines, the dataset gives AI agents clean, categorized events they can instantly understand and act on. This makes them excellent AI agents powered by News Events dataset.

Turning Events Into Action

Here’s how it looks in practice:

Prospecting agent: While scanning a target account list, the agent notices that “Company X just signed a new client in your industry.” Instead of sending a generic email, it drafts a message that congratulates them and positions your product as the next logical step.
Account monitoring agent: Your AI checks daily for news about top accounts. It flags that a CEO has stepped down at one company, suggesting you re-engage before new leadership sets a different direction.
Competitive intelligence agent: While tracking your market, it picks up that a competitor “is developing” a new feature. That becomes part of your next strategy meeting, long before it makes it into glossy press releases.

The dataset doesn’t just enrich records in your CRM — it gives AI agents powered by News Events dataset the awareness they need to behave less like scripts and more like actual teammates.

Why Structure Matters

The power here isn’t only in freshness, it’s in structure. AI agents thrive on clarity. If a news article says, “Rumors suggest the company might launch a new product later this year,” the dataset captures that nuance as planning = true, rather than treating it as a confirmed launch.

That kind of detail is the difference between an AI agent that spams prospects with irrelevant updates and one that reaches out with credibility.

The Bigger Picture

AI agents powered by News Events dataset are quickly moving from novelty to necessity in sales. But what separates the helpful ones from the noise is data quality. The News Events dataset acts like a stream of real-time situational awareness, allowing AI to spot openings humans might miss — and do it at scale.

In a sense, it gives AI agents something they usually lack: context. And in sales, context is everything.

Final Thought

If the last decade was about building bigger CRMs and larger lead lists, this one will be about equipping AI agents with the right signals. The News Events dataset is one of those signals — turning headlines into structured intelligence that AI can understand, prioritize, and act on. Therefore, AI agents powered by News Events dataset are becoming indispensable tools in modern sales strategies.

Because at the end of the day, the future of sales isn’t just AI for the sake of AI. It’s AI that knows when the moment is right.

Interested in our API Docs? Feel free to find them “here“.

How AI Sales Agents Are Transforming B2B Prospecting and How PredictLeads Steps In

How PredictLeads Company Data powers modern Sales Intelligence & Data Enrichment

Company Intelligence Data in Your Sales Enablement Platform

How PredictLeads Company Data powers modern Sales Intelligence & Data Enrichment

September 8, 2025 / Robert Fon

In today’s markets, having the right data at the right time can make or break a sales, marketing, or investment strategy. PredictLeads is a company data provider specializing in fresh, structured, and highly targeted company intelligence. Instead of offering another platform with a limited interface, PredictLeads delivers APIs, FlatFiles, and webhooks that plug directly into your existing systems offering top notch data enrichment services.

With some 100 million company profiles indexed and datasets covering everything from hiring signals to funding events, PredictLeads empowers teams to enrich their CRM, identify opportunities earlier, and personalize outreach with precision.

Why Data Enrichment Matters in 2025

Sales and marketing teams face an overload of static data that quickly becomes outdated. Investors, revenue teams, and growth leaders need real-time insights that signal change. That’s where data enrichment becomes critical.

Instead of relying only on traditional firmographics, modern teams use dynamic signals such as:

Job Openings for hiring for new roles signals company growth.
Technology Adoption used for monitoring tech stacks reveals buying intent and churn risks.
Financing Events showcasing funding rounds highlight momentum and expansion.
News Events such as acquisitions, partnerships, or product launches used to trigger new opportunities.

PredictLeads captures these signals at scale, allowing businesses to focus on accounts that are actually moving.

Turning Signals Into Opportunities

1. Companies Dataset

A global index of over 100 million companies, including firmographics, domain data, and organizational details. This forms the backbone for data enrichment and targeting.

2. Job Openings Dataset

Hiring trends reveal where companies are investing resources. Whether a SaaS company expanding its sales team or a fintech startup hiring engineers, job ads are a leading growth indicator.

3. News Events Dataset

Structured data on press releases, announcements, and media coverage – including M&A, partnerships, IPOs, and product launches. Perfect for timely outreach and market tracking.

4. Financing Events Dataset

Information on venture rounds, seed investments, and growth funding to help VCs and sales teams spot emerging opportunities before they hit mainstream databases.

5. Technologies Dataset

Understand which tools a company is adopting or replacing. Tech stack data is invaluable for competitive positioning and outbound targeting.

6. Website Evolution & Github Dataset

Track how websites evolve and which companies are actively pushing code. These niche signals are particularly useful for technical sales and product intelligence.

How PredictLeads Company Data Fits Into Your Stack

PredictLeads doesn’t lock users into a rigid interface. Instead, it integrates seamlessly with:

HubSpot & Salesforce – enrich leads and accounts with dynamic signals.
n8n, Zapier, Make.com, Polytomic – automate data flows without writing custom code.
Google Sheets & CRMs – Provides tools to convert exports into CSVs for quick experimentation and reporting.

Example Workflows using Company Data

Sales Prospecting: Find companies hiring for “Head of Marketing” roles → feed into CRM → trigger personalized outreach.
VC Scouting: Identify startups that just raised a Series A and are expanding their engineering team.
Competitive Monitoring: Get alerts when a competitor’s customer adds or drops a specific technology.

Case Examples with Data Enrichment

A SaaS company used the Job Openings dataset to find prospects expanding their marketing teams. By aligning outreach with hiring signals, they almost doubled response rates.
A venture capital firm leveraged Financing Events and News Events to track AI startups raising early-stage rounds for identifying opportunities before competitors.
A data marketplace partner integrated PredictLeads’ APIs to resell enriched company data profiles to their client base, generating recurring revenue.

Frequently Asked Questions

What is PredictLeads?
PredictLeads is a sales intelligence data provider offering APIs and datasets on companies, job openings, news events, funding, and technologies.

How does PredictLeads enrich company data?
By layering fresh signals (hiring, news, funding, technologies) on top of firmographics, PredictLeads helps teams prioritize the right accounts.

What makes PredictLeads different from Clearbit, Apollo, or ZoomInfo?
Unlike platforms that lock data behind a UI, PredictLeads provides direct APIs, FlatFiles and Webhooks making it easy to integrate into any workflow.

Can PredictLeads integrate with HubSpot or Salesforce?
Yes. PredictLeads data can be enriched directly into CRMs via APIs, n8n, Zapier, or reverse ETL tools.

Who uses PredictLeads Data Enrichment Services?
Sales teams, venture capital firms, marketing leaders, data marketplaces, and anyone needing up-to-date company intelligence.

Conclusion

The future of GTM and investment workflows is signal-driven. Static databases no longer cut it and companies need real-time enrichment that reflects actual market movements.

PredictLeads delivers exactly that: fresh datasets, flexible APIs, and seamless integrations. Whether you’re a sales leader targeting enterprise accounts, a VC scouting your next investment, or a marketplace reselling enriched company data, PredictLeads gives you the edge.

Feel free to let us know if you have any questions! We’re here to help.

Want to know how BBQ and company data are related – find out “here“.

Want to learn how to leverage PredictLeads via Polytomic?