Swordfish vs Clearbit: company enrichment vs reachability (what breaks after the demo)

Q: Is Clearbit a direct competitor to Swordfish?

Not cleanly. Clearbit is commonly evaluated as an enrichment provider (especially company data), while Swordfish is commonly evaluated for person-level data that improves reachability. Many teams run both when they need complete profiles and working contact paths.

Q: Why do results vary so much between teams?

Because the biggest drivers are operational: seat count, API usage, list quality, and industry. If you don’t control for those, you’ll argue about vendor performance without learning anything.

Clearbit typically enriches profiles. Swordfish typically adds a reachability layer via phones. Most failures show up as procurement and integration risk.

By Ben Argeband, Founder & CEO of Swordfish.AI

Who this is for

Buyers starting vendor research who want a shortlist method they can defend after rollout. If you’ve been burned by credit models, data decay, and “simple integrations” that turn into RevOps tickets, this is for you.

Quick verdict

Core answer: swordfish vs clearbit is mainly company enrichment vs reachability: Clearbit is typically used for enrichment (company/person attributes for routing, scoring, and personalization). Swordfish is typically used for person-level data that improves reachability (prioritized direct dials and mobile numbers) so reps can connect. Many teams run both: enrichment plus a phone reachability layer.
Key stat: Ignore vendor-wide averages. Your results vary most by seat count, API usage, list quality, and industry.
Ideal user: Teams that already have acceptable enrichment (or can live with basic firmographics) but are losing time and pipeline to low connect rates and stale phone coverage.

Choose Clearbit if your bottleneck is missing or inconsistent attributes that break routing, scoring, segmentation, or personalization.
Choose Swordfish if your bottleneck is contactability and you need a reachability layer (phones) that holds up on your ICP.
Choose both if you need clean company profiles and working contact paths: keep Clearbit for enrichment and add Swordfish for phones.
If you can’t define field ownership (which tool writes which fields), don’t deploy either. You’ll create silent overwrites and spend your quarter debugging CRM history.

What Swordfish does differently

Clearbit is commonly bought for enrichment: filling in missing company data and person attributes so downstream systems don’t guess. That helps ops workflows, but it doesn’t guarantee a human is reachable.

Swordfish is built around person-level data for reachability, with an emphasis on prioritized direct dials and mobile numbers (the numbers you try first in a dial workflow, not just “a phone field” in the CRM). If your business outcome is more conversations per rep-hour, this is the layer that usually decides whether sequences produce connects or just activity.

Commercial models are where buyers get quietly taxed. Swordfish sells true unlimited access with a fair use policy, which can reduce internal rationing and “who burned the credits?” arguments. Audit it like you would any other contract term: ask for the written fair-use boundaries, what triggers throttling or review, and how API usage is measured.

If you already use Clearbit for enrichment, the operationally boring approach is best: keep Clearbit as the company enrichment layer and add Swordfish as the reachability layer. Use File Upload to append phones to existing lists so you don’t rebuild your enrichment pipeline just to add direct dials.

Decision guide

Use the framework you can explain to Finance and RevOps: company enrichment vs reachability. They fail differently, and they create different hidden costs.

Enrichment failure: routing/scoring/personalization breaks because attributes are missing, inconsistent, or mapped wrong.
Reachability failure: sequences run, but connects don’t happen because phone coverage is weak, stale, or not aligned to your ICP.

Most teams eventually need both layers. The practical question is which failure is costing you more this quarter.

Example field ownership that avoids self-inflicted damage: your enrichment tool writes firmographics and routing fields, your phone tool writes phone fields, and your CRM keeps a change log so you can trace overwrites.

Production reality: the “integration” isn’t the API call. It’s the month-two mess—field overwrites, dedupe keys that don’t match, enrichment triggers that fire too often, and a CRM full of conflicting updates. If two tools write to the same field, you’ll spend weeks chasing “why did this record change?” tickets.

Checklist: Feature Gap Table

Buyer requirement (what breaks)	Clearbit (typical fit)	Swordfish (typical fit)	Hidden cost / integration headache to audit
Company enrichment for routing, scoring, segmentation	Strong fit when you need structured company data and enrichment workflows	Not the primary use case	Schema drift: fields don’t map cleanly to your CRM; you’ll keep re-normalizing as your schema and scoring rules change
Person-level reachability (phones that lead to connects)	Not typically the core buying reason	Primary fit: direct dials and mobile reachability focus	Coverage variance by ICP: what works in one industry can fail in another; test on your own list quality and regions
Prospecting workflows that don’t get throttled by credits	Often usage-metered depending on plan and workflow	Unlimited credits positioning with fair use	Budget predictability: metered models create overage risk or internal rationing that kills adoption
Integration surface area (CRM/SEP workflows)	Works best when you standardize on enrichment endpoints and define field ownership	Works best when you standardize on phone append workflows and define field ownership	Field overwrite risk: if both tools write to the same phone/company fields, “last write wins” creates silent data corruption
Data governance (audit trail and ownership)	Depends on how you implement logging and change tracking	Depends on how you implement logging and change tracking	Without change logs, you can’t debug decay vs overwrite vs bad inputs; you’ll blame the vendor and still not fix the system
Change logging / audit trail	Usually your responsibility to implement in CRM/data warehouse	Usually your responsibility to implement in CRM/data warehouse	If you can’t trace who wrote what and when, you can’t prove ROI or diagnose failures; you’ll end up rolling back fields by hand
Source-of-truth policy (field-level)	Works when you restrict writes to agreed enrichment fields	Works when you restrict writes to agreed phone fields	If you don’t set this policy, you’ll get duplicate fields, conflicting values, and “which one is real?” debates that stall rollout
Contact data quality you can monitor	Quality depends on match logic and inputs	Quality depends on ICP and validation approach	Decay management: if you don’t sample and re-test, quality drops and nobody notices until pipeline does

Decision Tree: Weighted Checklist

This rubric uses weights based on standard contact-data failure points: coverage mismatch to ICP, unpredictable consumption models, and integration drift that creates rework. Use it to score swordfish vs clearbit in your environment.

Highest weight: ICP fit (coverage where you sell) — Evidence: run a trial on your own ICP list and review results by industry and region. If the vendor won’t support this, stop.
Highest weight: Outcome alignment — Evidence: if you need meetings, test whether person-level phone data increases reachable contacts on the same sample and channel you use today. If you need routing/scoring, test enrichment completeness for the exact fields your rules use.
High weight: Pricing model predictability — Evidence: model cost under your expected seat count and API usage. If the model forces rationing or creates overage surprises, adoption will drop.
High weight: Integration and field ownership — Evidence: a written field-ownership map (which tool writes which fields) plus a plan for conflict resolution. If you can’t define ownership, you’re buying future tickets.
Medium weight: Data decay controls — Evidence: a sampling cadence and a re-append/re-enrich plan. If you treat this as a one-time project, you’ll be back here in six months.
Medium weight: Compliance and auditability — Evidence: documentation on permitted use, opt-out handling, and data processing terms. If Legal can’t review it quickly, the tool becomes shelfware.

Variance explainer: teams disagree on vendor performance mostly because of list quality (dedupe and domain hygiene), industry coverage differences, API usage patterns, and seat count behavior.

Troubleshooting Table: Conditional Decision Tree

If routing/scoring is failing because firmographics and attributes are missing, then prioritize Clearbit-style enrichment first.
If reps are running sequences but connects are low, then prioritize Swordfish-style reachability first (phones).
If you already have enrichment but meetings are flat, then keep Clearbit for enrichment and append phones via File Upload to add a reachability layer without rebuilding workflows.
If Finance is pushing back on unpredictable usage, then favor the model you can forecast under your seat count and API usage assumptions, and get “fair use” in writing.
Stop condition: If neither vendor produces acceptable results (better than your current baseline on the same ICP sample, using the same success definition and channel), stop the purchase and fix inputs first (dedupe, normalize domains, remove junk titles). Buying more data won’t repair bad list hygiene.

Limitations and edge cases

Enrichment does not equal reachability: enrichment can improve targeting and personalization, but it doesn’t guarantee a working phone number.
Industry variance is real: coverage can swing by vertical and region. That’s why vendor-wide claims don’t transfer cleanly.
International complexity: phone formats and availability vary by geography. If your ICP is outside a vendor’s strong regions, expect more cleanup and lower yield.
Two-vendor stacks need governance: if you run both, define source-of-truth per field and log changes, or you’ll create silent conflicts.
Seat count changes behavior: more seats means more ad-hoc lookups and inconsistent usage. Without guardrails, ROI attribution becomes guesswork.

Evidence and trust notes

This page avoids invented accuracy rates, coverage claims, or competitor pricing details. The only honest promise is process: test on your own ICP and control for the variables that usually explain variance—seat count, API usage, list quality, and industry.

Bias note: I’m the CEO of Swordfish. If you want to keep this fair, set up the test so it can disprove the tool. If Swordfish doesn’t improve reachability on your ICP sample, don’t buy it.

Compliance requirements vary by jurisdiction and internal policy. Require written documentation and route it through Legal before you wire anything into production workflows.

Audit questions I’d ask either vendor before signing: who owns which fields in the CRM, what change logging exists (or what you must build), what “fair use” means in writing, and how opt-outs/requests are handled operationally.

How to test with your own list (7 steps)

Define the layer you’re testing: enrichment (attributes) vs reachability (phones). Don’t mix success criteria.
Predefine your join key and dedupe rules: decide what counts as the same company/person before you run anything, or your “match rate” becomes a spreadsheet argument.
Pull a blinded ICP sample: include the titles, regions, and industries you actually sell to. Keep it representative.
Clean inputs first: dedupe, normalize domains, and remove obvious junk records. This reduces “list quality” noise.
Run the same list through both approaches: for enrichment, measure completeness of the fields your routing/scoring uses; for reachability, measure presence of dialable phone outputs on the same people.
Segment results: break outcomes down by industry and region so you can see where the tool fails.
Log conflicts and overwrites: if you’re testing in a sandbox CRM, track which fields changed and why. If you can’t trace changes, you can’t deploy safely.

If you want deeper cost-model scrutiny for Clearbit, see clearbit cost. If you’re building a shortlist beyond Clearbit, see clearbit alternative. For how to monitor decay and quality over time, see data quality. For how “unlimited” models typically behave in procurement and rollout, see unlimited contact credits.

FAQs

Is Clearbit a direct competitor to Swordfish?

Not cleanly. Clearbit is commonly evaluated as an enrichment provider (especially company data), while Swordfish is commonly evaluated for person-level data that improves reachability. Many teams run both when they need complete profiles and working contact paths.

What should I test to compare swordfish vs clearbit fairly?

Run two tests on the same ICP sample: enrichment completeness for the fields your routing/scoring uses, and reachability yield for phones on the same people. Keep inputs constant so you’re not confusing vendor performance with list quality.

Why do results vary so much between teams?

Because the biggest drivers are operational: seat count, API usage, list quality, and industry. If you don’t control for those, you’ll argue about vendor performance without learning anything.

Can I run Clearbit and Swordfish without field conflicts?

Yes, if you treat it like a data governance problem, not a tooling problem. Define field-level source-of-truth (enrichment fields vs phone fields), restrict writes accordingly, and keep change logs so you can trace overwrites.

Can I use Swordfish to add phones to a Clearbit-enriched list?

Yes. If Clearbit is your enrichment layer and you want a reachability layer, append phones to your existing list via File Upload.

What’s the hidden cost that usually shows up after rollout?

Field conflicts and decay. If you don’t define field ownership and monitor changes, you’ll get silent overwrites and a slow drop in effectiveness that looks like “reps stopped using it.”

Next steps

Timeline (7–10 business days if you keep scope tight):

Day 1: Decide whether you’re solving enrichment, reachability, or both. Write success criteria that match the layer.
Days 2–3: Build a blinded ICP sample list and clean it (dedupe, normalize domains, remove junk titles). Predefine join keys and dedupe rules.
Days 4–6: Run tests with identical inputs. Segment results by industry and region.
Days 7–8: Review commercial risk under your seat count and API usage assumptions. Get “fair use” boundaries in writing if you’re buying unlimited.
Days 9–10: Decide deployment: define field ownership, logging, and where updates land in your CRM/SEP. If you’re adding reachability to an existing enrichment stack, start with File Upload.

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.