Swordfish vs SalesIntel: a cynical buyer’s audit (records vs connects)

Q: Where can I compare SalesIntel to other tools?

See the SalesIntel vs ZoomInfo comparison and the SalesIntel alternative page.

Byline: Ben Argeband, Founder & CEO of Swordfish.AI

Disclosure: I’m the CEO of Swordfish. Treat this page as a buying framework and validate everything with your own test list and dialer logs.

Who this is for

International teams (especially EU-heavy) deciding whether swordfish vs salesintel fits their geography and compliance needs. If your reps are dialing, the purchase isn’t “contact records.” It’s connect rate, rep adoption, and how much operational cleanup you inherit when data decays.

Author note (geography-fit first): Region affects reachability. A provider can look fine in one market and fail in another because coverage and refresh behavior vary. Validate fit with a representative test list from your real territories, then measure connects, not just filled fields.

Quick verdict

Core answer: Records vs connects is the real decision. Choose Swordfish when you need fast callable numbers at scale, prioritized direct dials (ranked mobile numbers), and predictable spend under high usage (“unlimited” with fair use). Choose SalesIntel when your org is willing to run a verification workflow and can operationally own the queue time and handoffs.
Key stat: Ignore generic “accuracy” claims. Run a territory-matched test list and compare connect rate plus time-to-first-dial. Those expose data decay and workflow friction faster than any vendor dashboard.
Ideal user: EU-heavy or multi-region teams that need verification tied to outbound outcomes, not record volume, and that want to avoid integration debt and surprise usage costs.

What Swordfish does differently

In a real audit, the only definition of verification that matters is whether it improves connect rate in your territories. Everything else is a spreadsheet exercise that doesn’t pay for itself.

Prioritized direct dials (ranked mobile numbers): When multiple numbers exist, the only question that matters is which one gets tried first. Prioritization reduces wasted attempts per connect, which shows up as higher rep throughput and fewer “this data is trash” complaints that kill adoption.

True unlimited + fair use: For high-activity outbound, the contract should not punish usage with per-contact penalties. Confirm fair use guardrails in writing so spend stays predictable when seat count grows or outbound volume spikes.

Automation-first enrichment: If your process depends on a human verification loop, you’re adding queue time and operational ownership. Automation-first enrichment reduces handoffs so time-to-first-dial stays low, which is what keeps sequences moving and reps using the system instead of bypassing it.

If you want a more automated alternative to a verification workflow that can turn into a ticket queue, see Prospector.

Decision guide

Framework to include: Evaluating “records” instead of “connects”. Common mistake: buyers compare record counts and field completeness, then wonder why the dialer still can’t reach anyone. That’s the records vs connects trap.

Force the evaluation to answer two operational questions: (1) does it increase connects in your territories, and (2) what ongoing work does it create for RevOps and engineering.

How to test with your own list (5–8 steps)

Build a representative test list from your real territories (include EU-heavy segments if that’s your business). Keep titles, industries, and seniority similar to your pipeline.
Normalize the input before testing: remove duplicates, standardize company names/domains, and freeze the list so both tools see the same starting point. This prevents “list quality” from masking vendor differences.
Define connect rate and dispositions with your team before dialing, and enforce consistent disposition hygiene in the dialer so results are comparable.
Run enrichment the same way you’ll run it in production: same CRM writeback rules, same dialer workflow, same user permissions. A demo flow is not a production flow.
Measure time-to-first-dial from lead creation/import to a callable number available in the dialer. Workflow friction is a hidden cost that shows up as low adoption.
Dial in comparable windows (same days/times) and track outcomes by region, industry, and seniority. Geography variance is real; averages hide failures.
Audit exceptions: missing numbers, conflicting fields, duplicates created, and manual steps required. These become recurring operational work.
Map cost model to usage reality: seat count, API usage, enrichment volume, and any fair use terms. If finance can’t forecast it, you’ll pay for it later.

Checklist: Feature Gap Table

Buying criterion (what breaks in real life)	Swordfish (what to verify)	SalesIntel (what to verify)	Hidden cost if you get it wrong
Records vs connects (filled fields that don’t reach people)	Run a territory-matched test and compare connect rate; confirm prioritized direct dials reduce attempts per connect	Run the same test; confirm whether any verification workflow changes time-to-first-dial	Rep time wasted dialing dead numbers; adoption drops when reps stop trusting the data
Verification definition (what “verified” means operationally)	Ask what signals drive verification and how refresh behaves by region	Ask what verification covers (phone/title/employment) and how turnaround time affects outbound	False confidence: “verified” data still decays; you pay twice (subscription + cleanup)
Direct dial data coverage by geography (EU-heavy reality)	Break results out by country and segment; confirm mobile availability where direct dials are scarce	Break results out the same way; confirm whether verification improves reachability in your countries	Coverage gaps force vendor stacking; integration and reconciliation become permanent work
Cost model under peak outbound (unlimited credits vs throttles)	Confirm “unlimited” terms, fair use boundaries, and what triggers restrictions	Confirm credit rules, overages, and whether verification requests consume quota	Budget variance: spend rises when outbound activity rises
Integration ownership (CRM/dialer/API)	Confirm API limits, enrichment latency expectations, and error handling; define field precedence rules	Confirm API access and how verification workflow integrates; define who owns exceptions	Engineering time + RevOps time; “it integrates” becomes “you maintain it”
Implementation/onboarding ownership (RevOps vs Eng)	Confirm who configures dedupe, writeback, and permissions; document the ongoing admin tasks	Confirm the same, plus who runs verification requests and manages turnaround expectations	Tool becomes shelfware when no one owns the workflow
Contact data validation in production (data decay management)	Define re-validation cadence and measure drift by segment	Define how re-verification works and whether it’s request-based or automatic	Decay becomes a recurring cleanup project; your CRM becomes less reliable over time

Decision Tree: Weighted Checklist

Highest weight: Connect-oriented outcomes (standard failure point: buying “accuracy” instead of reachability). Require a territory-matched test list and compare connect rate and time-to-first-dial.
Highest weight: Verification and decay behavior (standard failure point: stale “verified” data). Ask how verification is defined, how refresh works by region, and what happens when records drift.
High weight: Cost model predictability (standard failure point: usage surprises). Evaluate spend drivers using the variance explainer: seat count, API usage, list quality, and industry. Confirm how unlimited credits and fair use are enforced.
High weight: Adoption risk (standard failure point: reps bypass the tool). Measure workflow steps and exceptions. More steps means lower adoption, even if the dataset looks good in a spreadsheet.
Medium weight: Integration debt (standard failure point: “it integrates” without ownership). Confirm dedupe rules, field precedence, writeback conflicts, and who maintains the connector.
Medium weight: Geographic variance reporting (standard failure point: blended averages). Require results broken out by country/region and segment so you can forecast performance where you actually sell.

Troubleshooting Table: Conditional Decision Tree

If your outbound motion is dialer-first and speed matters, then pick the tool that reduces time-to-first-dial and improves connect rate on your territory-matched list.
If your org wants a verification workflow for governance, then confirm the workflow details in writing and treat it as an operating process: define ownership, turnaround expectations, and what happens when volume spikes.
If your team is EU-heavy, then require country-by-country results and confirm your compliance posture with counsel before rollout.
If your usage spikes (new SDR class, new territory, end-of-quarter), then stress-test the cost model using seat count and API usage assumptions, not a demo environment.
Stop condition: If the vendor won’t support a territory-matched test that reports connects and won’t provide an order form plus usage policy you can map to seat count and API usage, you can’t forecast outcomes or spend. Don’t sign.

Limitations and edge cases

Variance is expected. Results change with seat count, API usage patterns, list quality, and industry. A team enriching inbound leads will see different value than a team building outbound calling lists.

Geography can dominate outcomes. Some regions have lower availability and faster decay for direct dials. That’s why you should report results by country, not as a blended average.

Integration details decide adoption. If dedupe rules and field precedence aren’t defined, you’ll create duplicates, overwrite good data with worse data, and spend months arguing about “which source is right.” That’s integration debt, not a one-time setup task.

Evidence and trust notes

This page is written from a buyer/auditor perspective: assume data decays, assume workflows create hidden labor, and assume “unlimited” has terms. Validate everything with your own list.

What would falsify the claims here: if a territory-matched test shows no improvement in connect rate or time-to-first-dial, or if the cost model can’t be forecast from seat count and API usage, the tool isn’t doing the job you’re paying for.

Audit trail to keep during your test:

Original frozen test list and the enriched outputs from each tool
Dialer logs with dispositions and timestamps (to compute connect rate and time-to-first-dial)
Exception log: missing numbers, duplicates created, writeback conflicts, manual steps
Order form and usage policy notes mapping charges to seat count, API usage, and enrichment volume

Cost-model sanity check: Request the order form and usage policy from each vendor, then map charges to your seat count, API usage, list quality, and industry. Ask each vendor to confirm in writing what triggers throttles or overages and whether API usage is treated differently than UI usage.

For deeper evaluation criteria, see data quality and unlimited contact credits.

FAQs

Is SalesIntel accuracy better than Swordfish?

“Accuracy” depends on your definition. If you mean “a field matches a reference,” you can get one answer. If you mean “a rep reached a human,” you need a connect-based test. Use your own list, then compare connect rate and time-to-first-dial by region and segment.

What does records vs connects mean when buying contact data?

Records are rows with populated fields. Connects are reachability outcomes that move pipeline. Data decay makes record completeness a weak proxy for performance, especially for phone-based outbound.

How do I evaluate direct dial data for EU-heavy outbound?

Don’t accept blended results. Break your test by country and segment, then measure connects. If coverage is uneven, you’ll end up stacking vendors and paying integration costs to reconcile conflicts.

What’s the real risk behind unlimited credits?

The risk is enforcement details: fair use boundaries, throttles, exclusions, and how API usage is treated. If you can’t map spend to seat count and usage patterns, finance will get surprised after rollout.

Where can I compare SalesIntel to other tools?

See salesintel vs zoominfo and salesintel alternative.

Next steps

Timeline:

Day 1: Build and freeze a representative test list; define connect rate and disposition rules.
Days 2–3: Enrich the same list through both tools using your real CRM/dialer workflow; log exceptions.
Days 4–5: Dial in comparable windows; report results by country/segment; review time-to-first-dial and adoption friction.
Week 2: Audit the cost model using seat count and API usage assumptions; decide based on connects and operational overhead; set a re-validation cadence to manage data decay.

About the Author

Ben Argeband is the Founder and CEO of Swordfish.ai and Heartbeat.ai. With deep expertise in data and SaaS, he has built two successful platforms trusted by over 50,000 sales and recruitment professionals. Ben’s mission is to help teams find direct contact information for hard-to-reach professionals and decision-makers, providing the shortest route to their next win. Connect with Ben on LinkedIn.