Clay and Bettercontact both do the same core job well, in different ways: take a record with gaps and route it through multiple data providers until the missing fields come back verified. That is genuinely useful, unglamorous infrastructure work. It is also a different job from deciding whether the record was worth enriching in the first place, which is a fit question a general-purpose enrichment tool has no way to answer.
What each enrichment tool is actually good at
See the dedicated comparison at Arcova vs. Clay for the full breakdown against Clay specifically.
Free tier, then usage-based plans scaling from roughly the low hundreds to several hundred dollars a month, with custom enterprise pricing above that
A spreadsheet-like canvas that waterfalls over a hundred data providers together, runs AI research agents on each row, and pushes results anywhere, flexible enough to build almost any enrichment or research workflow.
Best for: Technical RevOps teams that want a fully custom, provider-agnostic enrichment and orchestration workflow across many use cases, not just one.
Roughly $15 to $50 a month for most teams, scaling to enterprise tiers around $800/month for high volume, charging only for verified results
A dedicated waterfall enrichment tool routing records through 20-plus data providers, with high reported email and phone find rates and a pay-only-for-verified-results credit model.
Best for: Teams that already have contact lists and need to fill in missing, verified emails and phone numbers at scale, without building a custom workflow.
Enrichment fills fields. Scoring decides who matters.
A waterfall tool answers "what is this company's size, and what is this person's verified email," regardless of industry, because that question looks the same everywhere. For a life science team, the more useful question is different: does this company's therapeutic area, modality, and development stage actually resemble our best customers, and is anything happening right now that means this is a good time to reach out. See building an ICP as a CRO or CDMO for how that model works. Neither Clay nor Bettercontact ships that model; Clay can be configured to approximate it, and Bettercontact does not attempt to at all, by design, since it is scoped specifically to contact-field verification.
The stitching burden vs. one score written back to the CRM
A realistic life science data-orchestration stack often runs a waterfall tool to keep contact fields fresh, a separate spreadsheet or set of CRM views to approximate an ICP filter by hand, and someone who periodically re-checks that filter as therapeutic focus or ideal customer profile shifts. Every piece of that is real, recurring work: mapping enrichment output into CRM fields, maintaining the manual fit rules, and re-running the filter whenever the list goes stale.
Arcova unifies a narrower, specific piece of that motion end to end: it sits behind your CRM, scores every incoming record against a life science ICP, layers in fit and readiness signals, and writes the result back as fields your team can filter and sort on. That is the one motion Arcova owns end to end, life science scoring and CRM sync, not a claim to replace the general enrichment-waterfall category, which solves a genuinely different, industry-agnostic problem well.
Choosing between them
If your CRM has real gaps in contact data, verified emails and phone numbers missing on records you already care about, a dedicated waterfall tool like Bettercontact is the fastest, cheapest fix, and it is a narrower, more affordable tool than Clay if enrichment is the only job you need done. If you need a broader, fully custom orchestration workflow across many use cases beyond contact enrichment, Clay's flexibility is worth the setup time. Either way, neither tool answers the fit and readiness question a life science team actually needs answered once the data is clean, which is the gap a purpose-built layer like Arcova is built to close.
For the upstream data-sourcing side of this same stack, see the best sales intelligence tools for life science.