COMPUTED
Follow-on signal built from prior Hiring evidence
COMPUTED
Stack motion from hiring, not a separate technographics feed.
When Hiring keeps showing the same catalog tool over 30 days, VeilStrat creates or refreshes a Tools signal from that prior evidence — one live row per tool per company, stronger when JDs use usage language.
- Computed from previous Hiring signals (and linked JD text)
- One live row per company + tool, updated in place
- Mention-only 60 · usage-hint language 75
- Dropout from the window leaves the historical row
Sample signalCOMPUTED
Tools · LangChain
Computed from Hiring · 30-day window · sample
Hiring · ML Engineer
“Experience with LangChain and OpenAI API”
Hiring · AI Platform
“Building agents using LangChain in production”
30-day window →
Tools · LangChain
computedUsage-hint language · confidence 75 · 1 live row
COMPUTED
Role
30 days
Window
60 / 75
Score
At a glance
How this signal shows up in the store
30d
Rolling window of Hiring mentions per tool
1 row
Live row per company + catalog tool
60/75
Mention-only vs usage-hint language
What it is
A follow-on signal born from hiring evidence
Tool adoption is a COMPUTED signal: when Hiring keeps showing the same tool from VeilStrat’s catalog for a company over time, VeilStrat creates another signal type — Tools — using those previous Hiring signals as evidence. Optionally strengthens when JDs use usage language (“using X”, “experience with X”).
This is not a separate technographics crawl. Mentions over the last 30 days roll up to one live row per tool per company. Snapshots link back to the Hiring evidence and relevant slice ids so GTM can cite the stack with proof.
- Triggered in the hiring process chain after new Hiring writes
- Create if missing, or refresh label, confidence, and snapshot in place
- If a tool drops out of the 30-day window, the historical row is left in place
Signature story
From Hiring JD evidence to one Tools row
Catalog tools mentioned across recent hiring create or refresh a live Tools signal — mention-only at 60, usage language at 75.
Hiring in → Tools out
Evidence inheritance
Hiring · ML Engineer
“Experience with LangChain and OpenAI API”
Hiring · AI Platform
“Building agents using LangChain in production”
30-day window →
Tools · LangChain
computedUsage-hint language · confidence 75 · 1 live row
Rolling window rules
- Create if the tool row is missing
- Refresh label, confidence, and snapshot in place
- Link back to Hiring signal / job ids
- If the tool drops out of 30 days, keep the historical row
How it forms
Previous signals in → Tools out
After Hiring is processed for a company, Tools recompute from Hiring in the last 30 days and their linked job text.
- 1
Hiring lands
New Hiring signals trigger the post-hiring recompute chain — the same step that also refreshes Velocity.
- 2
Scan 30-day evidence
Detect catalog tools in recent Hiring signals and linked job descriptions across the rolling window.
- 3
Create or refresh
For each tool: create the Tools signal if missing, or refresh label, confidence, snapshot, and links back to Hiring / job ids. One live row per company + tool.
- 4
Keep history on dropout
If a tool leaves the 30-day window, the historical Tools row stays — it is not auto-deleted — so you retain past stack motion context.
Confidence
Mention vs usage language
Write-time confidence depends on how the tool appears in the JD evidence behind the Hiring rows.
BandConfidenceNote
- Name mention only60—
- Usage-hint language75e.g. using X, experience with X
Stock vs flow
Tools vs bolted-on technographics
Third-party stack databases lag hiring reality. Tool adoption is computed from Hiring you already trust on the timeline.
Layer
Traditional
VeilStrat
Source
Evidence
Separate technographics vendor
Another contract, another lag
Prior Hiring signals + JD text
Same store, computed follow-on
Freshness
When it updates
Batch refresh cycles
Stack looks frozen for months
After each Hiring write
30-day window create/refresh
Proof
What reps can cite
Vendor logo with no JD context
Hard to personalize
Links back to Hiring / job ids
Snapshot carries the evidence
Unit
How it stores
Noisy multi-row dumps
Duplicate tools everywhere
One live row per tool
Updated in place while in window
Source
Evidence
Traditional
Separate technographics vendor
Another contract, another lag
VeilStrat
Prior Hiring signals + JD text
Same store, computed follow-on
Freshness
When it updates
Traditional
Batch refresh cycles
Stack looks frozen for months
VeilStrat
After each Hiring write
30-day window create/refresh
Proof
What reps can cite
Traditional
Vendor logo with no JD context
Hard to personalize
VeilStrat
Links back to Hiring / job ids
Snapshot carries the evidence
Unit
How it stores
Traditional
Noisy multi-row dumps
Duplicate tools everywhere
VeilStrat
One live row per tool
Updated in place while in window
Example snapshots
What a Tools row looks like
Illustrative computed rows — real snapshots link to the Hiring evidence that produced them.
Usage language
Tools · LangChain
Multiple Hiring JDs mention building with LangChain in production over 30 days.
Confidence 75 · Live row · Linked hiring ids
Mention only
Tools · Cursor
Catalog tool named in openings without strong usage phrasing.
Confidence 60 · Still one live row per tool
Window exit
Historical Tools · Gong
Tool left the 30-day window; historical row retained, not auto-deleted.
Out of window · History kept
GTM moments
See stack motion without another data vendor
Lead with the tools buyers are staffing for — grounded in hiring evidence.
Stack
Prioritize accounts adopting your category
Filter and personalize around catalog tools that appear in live Hiring windows.
Proof
Cite the JD, not a logo list
Snapshots link back to Hiring so outreach can quote how the tool showed up.
Timing
Catch stack motion as seats open
Tools refresh after Hiring writes — closer to the build moment than quarterly scrapes.
Focus
One row per tool keeps lists clean
Create/refresh in place so reps are not flooded with duplicate tool pings.
Computed window
One live row per tool, rolling 30 days
Mentions over the last 30 days roll up to one row per tool per company. Tools is not a vendor technographics feed — it is derived from Hiring you already trust on the timeline. Dropout leaves history; it does not invent a new source of truth.
Related signals
How this connects on the timeline
See stack motion without another data vendor — explore the signals that feed or follow this type.
FAQ
Tool adoption, answered
How computed Tools rows inherit evidence from Hiring.
It is a COMPUTED timeline row created or refreshed when catalog tools appear in a company’s Hiring evidence over the last 30 days — one live row per company and tool.
Track the stackbuyers are hiring for.
Tool adoption signals refresh from Hiring — one live row per tool, scored for mention vs usage language.
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