The build or buy question deserves a straight answer, because building is not a strawman: plenty of life science sales teams track signals in-house today, and some of that work is genuinely good. There are three common shapes, a spreadsheet with alerts, a RevOps workflow build, and a homegrown scraper stack, and each one solves part of the problem. What none of them gets for free is the part Arcova treats as the actual product: interpreting public activity against your ICP and keeping that interpretation current, week after week.
What does the spreadsheet and Google Alerts approach do well?
It starts today, costs nothing, and forces clarity. A rep who sets up alerts on fifty named accounts and logs what comes back learns quickly which events coincide with useful outreach, which is exactly the education signal-based GTM is built on. For a short, stable account list and a single owner, it can carry a team surprisingly far.
It strains on coverage and consistency. Keyword alerts only catch events that phrase themselves the way you guessed, and a spreadsheet is only as current as its last edit. The moment the list grows past what one person can re-check weekly, the misses become invisible: you do not see the funding round you did not catch.
What does a RevOps workflow build do well?
A skilled RevOps team using a general-purpose enrichment and workflow platform can automate real coverage: waterfall enrichment, scheduled checks on funding and hiring, AI prompts that approximate fit logic. This is the strongest in-house shape, and our Arcova vs. Clay comparison takes it seriously: yes, in principle a technical team can build a life science signal pipeline this way, and a team that already runs broad GTM automation across several use cases gets real leverage from owning the canvas.
The strain is that every life science-specific rule, what counts as therapeutic area match, which trial events matter at which stage, when a hiring pattern means budget, has to be designed, tested, and maintained by hand, and it decays as sources and edge cases change. The build is a project; the maintenance is a job.
What does a homegrown scraper stack do well?
For one well-defined source, a saved query on a public trial registry, a watcher on a federal database, scraping is cheap and exact, and an engineering team gets precisely the field it wants. If your entire signal need is one source and one question, a small script may honestly be the right answer.
The strain arrives with breadth and time. Life science readiness lives across many public sources, registries, regulatory calendars, funding announcements, conference exhibitor lists, hiring pages, each with its own format and its own habit of changing without notice. A stack that watches all of them is a standing engineering commitment, and the output is still raw events: someone must then decide what each one means for each account, which is the judgment half of the problem and the harder half to staff.
| Genuinely good at | Where it strains | |
|---|---|---|
| Spreadsheet + alerts | Free, immediate, teaches you your real signals | Coverage gaps and staleness as the list grows |
| RevOps workflow build | Real automation, full control of the logic | Life science rules built and maintained by hand |
| Homegrown scraper stack | Exact answers from a single known source | Breadth, source drift, and raw events without interpretation |
| Arcova | Fit and readiness scored out of the box, kept current | Not a general-purpose platform for non-life science work |
When is building in-house the better fit?
Honestly: when the need is narrow, the team is technical, or the learning is the point. A team whose entire motion hangs on one signal from one source is well served by a small script. A company with spare data engineering capacity and differentiated internal data, say, proprietary usage or lab data no vendor can see, may want an in-house layer because its edge lives in data nobody else has. And an early team with more time than budget learns its market faster by watching it manually for a quarter than by outsourcing the watching on day one. Arcova's case is strongest when the account list is long, the signal need spans many sources, and the team would rather spend its engineering on product than on GTM plumbing.
Related comparisons
See how Arcova compares to Clay, Zymewire, and ZoomInfo as well.