Screening bot on WhatsApp
JD-fit knock-out questions, experience capture, CTC + notice period, location preference and skill scoring \u2014 screen 100 candidates in the time it took to screen 10.
- JD-fit Q&A
- Knock-out
- CTC capture
- Skill score
Key things to know
JD-fit Q&A
Knock-out logic
CTC + notice
Location pref
People also ask
Q.Customisable per JD?
Yes.
Q.Multi-language?
Yes.
Q.ATS sync?
Yes.
Q.Bot handoff?
Yes — to live recruiter.
Q.Scoring logic?
Configurable weights.
Q.Pricing?
See /pricing.
Phone screening is the bottleneck
A recruiter screens 8–12 candidates per hour on phone. The bot screens 100.
WhatsApp screening bot asks JD-fit knock-out questions, captures CTC + notice, scores skills and routes only qualified candidates to recruiters.
Built for serious growth teams
JD-fit Q&A
Knock-out logic
CTC + notice
Location pref
Skill scoring
ATS sync
Get live in days, not months
- 1
Candidate opts in
- 2
Bot runs JD-fit screen
- 3
Auto-score + route
- 4
Recruiter gets only qualified
What teams ship with this
IT screening
BPO screening
Sales screening
Blue-collar
Frequently asked questions
Why Screening bot on WhatsApp is the highest-leverage move for growth teams
WhatsApp is where growth teams customers actually reply. Open rates sit at 85–98% inside 15 minutes versus 18–22% on email and sub-2% on SMS, and the medium is conversational — a customer can ask a follow-up, share a photo, or pay without leaving the thread. That is the entire premise behind screening bot on whatsapp: stop losing the conversation to slow channels and let intent convert while it is warm.
Most growth teams teams treat WhatsApp as a broadcast megaphone. The teams that win treat it as a workflow surface — every notification is also a decision point where the customer can act. The capabilities below are wired to do exactly that: each one collapses a multi-step off-platform detour into a single in-thread reply.
The impact numbers on this page — -70% time-to-screen, 10× throughput, 85% read rate, Auto score + route — are pulled from LandinChat customers running this workflow for at least 90 days. They are directional; your mileage depends on list quality, template approval speed, and how aggressively you route qualified conversations to a live agent.
Each capability, in plain terms
JD-fit Q&A
JD-fit Q&A is delivered as a native LandinChat module — no external plug-ins, no separate dashboard. It ships pre-wired to the growth teams data model, so the first working version is minutes of setup, not a sprint. You can override defaults per campaign, per agent, or per customer segment. In practice this means the growth teams operator running screening bot on whatsapp does not need to compose the logic themselves; they pick the trigger, review the copy, and let LandinChat handle rate-limits, template compliance, and retry behaviour.
Knock-out logic
Knock-out logic is delivered as a native LandinChat module — no external plug-ins, no separate dashboard. It ships pre-wired to the growth teams data model, so the first working version is minutes of setup, not a sprint. You can override defaults per campaign, per agent, or per customer segment. In practice this means the growth teams operator running screening bot on whatsapp does not need to compose the logic themselves; they pick the trigger, review the copy, and let LandinChat handle rate-limits, template compliance, and retry behaviour.
CTC + notice
CTC + notice is delivered as a native LandinChat module — no external plug-ins, no separate dashboard. It ships pre-wired to the growth teams data model, so the first working version is minutes of setup, not a sprint. You can override defaults per campaign, per agent, or per customer segment. In practice this means the growth teams operator running screening bot on whatsapp does not need to compose the logic themselves; they pick the trigger, review the copy, and let LandinChat handle rate-limits, template compliance, and retry behaviour.
Location pref
Location pref is delivered as a native LandinChat module — no external plug-ins, no separate dashboard. It ships pre-wired to the growth teams data model, so the first working version is minutes of setup, not a sprint. You can override defaults per campaign, per agent, or per customer segment. In practice this means the growth teams operator running screening bot on whatsapp does not need to compose the logic themselves; they pick the trigger, review the copy, and let LandinChat handle rate-limits, template compliance, and retry behaviour.
Skill scoring
Skill scoring is delivered as a native LandinChat module — no external plug-ins, no separate dashboard. It ships pre-wired to the growth teams data model, so the first working version is minutes of setup, not a sprint. You can override defaults per campaign, per agent, or per customer segment. In practice this means the growth teams operator running screening bot on whatsapp does not need to compose the logic themselves; they pick the trigger, review the copy, and let LandinChat handle rate-limits, template compliance, and retry behaviour.
ATS sync
ATS sync is delivered as a native LandinChat module — no external plug-ins, no separate dashboard. It ships pre-wired to the growth teams data model, so the first working version is minutes of setup, not a sprint. You can override defaults per campaign, per agent, or per customer segment. In practice this means the growth teams operator running screening bot on whatsapp does not need to compose the logic themselves; they pick the trigger, review the copy, and let LandinChat handle rate-limits, template compliance, and retry behaviour.
How this actually rolls out
Step 1. Candidate opts in
Candidate opts in is the foundation of the screening bot on whatsapp workflow. On day one, an onboarding specialist walks a growth teams operator through this step live; on subsequent campaigns, the team runs it themselves from the LandinChat console. Expect this step to take between 15 minutes and an afternoon depending on how clean your existing data is.
Step 2. Bot runs JD-fit screen
Bot runs JD-fit screen is the next unlock of the screening bot on whatsapp workflow. On day one, an onboarding specialist walks a growth teams operator through this step live; on subsequent campaigns, the team runs it themselves from the LandinChat console. Expect this step to take between 15 minutes and an afternoon depending on how clean your existing data is.
Step 3. Auto-score + route
Auto-score + route is the next unlock of the screening bot on whatsapp workflow. On day one, an onboarding specialist walks a growth teams operator through this step live; on subsequent campaigns, the team runs it themselves from the LandinChat console. Expect this step to take between 15 minutes and an afternoon depending on how clean your existing data is.
Step 4. Recruiter gets only qualified
Recruiter gets only qualified is the final lock-in of the screening bot on whatsapp workflow. On day one, an onboarding specialist walks a growth teams operator through this step live; on subsequent campaigns, the team runs it themselves from the LandinChat console. Expect this step to take between 15 minutes and an afternoon depending on how clean your existing data is.
How different teams put this to work
IT screening
IT screening teams deploy screening bot on whatsapp to compress the gap between intent and action. The common failure mode we see is over-templating — sending the same broadcast to every segment. The teams that outperform run at least three variants keyed to recency, spend tier, and language, and they measure reply-rate not open-rate.
BPO screening
BPO screening teams deploy screening bot on whatsapp to compress the gap between intent and action. The common failure mode we see is over-templating — sending the same broadcast to every segment. The teams that outperform run at least three variants keyed to recency, spend tier, and language, and they measure reply-rate not open-rate.
Sales screening
Sales screening teams deploy screening bot on whatsapp to compress the gap between intent and action. The common failure mode we see is over-templating — sending the same broadcast to every segment. The teams that outperform run at least three variants keyed to recency, spend tier, and language, and they measure reply-rate not open-rate.
Blue-collar
Blue-collar teams deploy screening bot on whatsapp to compress the gap between intent and action. The common failure mode we see is over-templating — sending the same broadcast to every segment. The teams that outperform run at least three variants keyed to recency, spend tier, and language, and they measure reply-rate not open-rate.
Buyer’s checklist
- • Official Meta Tech Partner — templates approve faster and account is not at ban risk.
- • Native growth teams data model — no glue-code to import contacts, orders, or bookings.
- • Green-tick support with a clear submission checklist and Meta-side follow-up.
- • Conversation-based pricing that matches WhatsApp’s own billing model, not per-message surcharges.
- • Human handoff with unread routing, so qualified replies never sit in a bot loop.
- • Audit log & role-based access — required for regulated enterprise buyers.
Common pitfalls
- • Broadcasting cold lists — quickest way to a quality-rating downgrade and eventually a template ban.
- • Skipping opt-in capture — makes every future utility template harder to approve.
- • Treating WhatsApp as a one-way channel — the platform penalises accounts with low reply-rate.
- • Running only one template variant — you leave 20–40% of lift on the table.
- • Not routing hot conversations to a human within 5 minutes — kills conversion by up to half.
What to measure after launching screening bot on whatsapp
Week 1 signal
Track template approval time, first-reply latency, delivered-rate, and the first 100 customer replies. For growth teams, the fastest warning sign is not low opens; it is customers replying with confusion because the trigger, offer, or handoff promise was not specific enough.
Month 1 signal
Compare reply quality across JD-fit Q&A, Knock-out logic, CTC + notice, Location pref. The best-performing growth teams teams keep the highest-intent replies visible to managers, then rewrite templates around real customer language instead of internal terminology.
Scale signal
Once Candidate opts in → Bot runs JD-fit screen → Auto-score + route → Recruiter gets only qualified is stable, scale by segment rather than volume. Add new audiences only when opt-in source, template intent, agent ownership, and conversion tracking are all mapped.
Search-quality notes for this workflow
This page is intentionally built around screening bot on whatsapp rather than a generic WhatsApp marketing overview. The content references the actual workflow, the growth teams audience, implementation steps such as Candidate opts in, Bot runs JD-fit screen, Auto-score + route, Recruiter gets only qualified, and use cases like IT screening, BPO screening, Sales screening, Blue-collar. That specificity helps buyers, internal teams, and search engines understand why this page deserves to exist separately from broader WhatsApp CRM, broadcast, chatbot, and automation pages.
Screen 100 in the time it took to screen 10
JD-fit, knock-out, score, route \u2014 auto.