Use Cases

Built for how revenue teams actually work.

Four people, four jobs to be done, one platform that learns the shape of each.

The SDR

Volume targeted outbound

Lives inAction
Signature moment

9:00am — 40 drafts queued from the overnight Pipeline. Each one cites the actual signal that fired: Series B funding, VP Sales hire, EMEA expansion post. Edits 6, approves 34. Done before standup. Spends the rest of the morning on deal prep, not personalization.

Primary modules
SequencesRepliesProspect Research
Approval queue
40 drafts · overnight
AC
Alex Chen
VP Revenue Ops · ACME
92%
Cited: Series B · $40M
SK
Sarah Kim
VP Sales · Relay
88%
Cited: Hiring 4 SDRs
JR
James Reed
Head of Growth · Cloudmatrix
84%
Cited: New VP Sales
MP
Maya Patel
CRO · Northwind
81%
Cited: Series B · $24M
36 more below
The shift

Before and after

Five things change the day you switch. Each one compounds.

1

Daily research

Before

4–5 hours alt-tabbing across LinkedIn Sales Nav, Apollo, 6sense, the prospect's site, and ChatGPT.

After

25 minutes reviewing pre-researched drafts. The Personalization Sources are already cited per draft.

2

Personalization quality

Before

Generic intros that all blur together by the second sentence. Buyers spot them in under a second.

After

Per-prospect openers citing the firing signal — funding, hiring, executive change — with source links the prospect can verify.

3

Reply turnaround

Before

30 minutes per reply: read the thread, draft from scratch, second-guess, send, log.

After

AI draft already grounded in your past winning replies and your RAG library. Edit, approve, gone in 90 seconds.

4

CRM hygiene

Before

Manual logging after every touch, half the fields blank, status drift between Outreach and Salesforce.

After

Auto-logged with full conversation context. Status reflects reality, not yesterday's last manual update.

5

Voice consistency

Before

Drift between fresh-you on Monday morning and burnout-you on Thursday afternoon. Your best emails happen by accident.

After

Message Intelligence learns from your edits and tightens every subsequent draft. Edit rate is your training signal.

Reclaim your week

What you don't do anymore

  • LinkedIn → Apollo → Outreach → ChatGPT alt-tab dance
  • Forget what the original signal was when a reply lands six weeks later
  • Manually log activity in the CRM after every touch
  • Re-write AI drafts from scratch — you're editing, not authoring
Where you spend your day

The modules you live in

Sequences

Multi-channel native (email + LinkedIn) with [bracket] personalization the AI fills per-prospect. Smart routing gives high-score leads deep research and low-score leads template-only — within the same sequence.

Replies

AI drafts grounded in your RAG library — past winning replies, sequences that converted, similar lead patterns. Every draft cites which sources it pulled from.

Prospect Research

Continuous background research producing structured Personalization Sources the AI cites by name when drafting. Not enrichment — narrative facts with citations.

The payoff

Why this works for you

Volume up, quality up

AI does per-prospect research and synthesis. You do editing. Both throughput and reply rate improve.

Signals carry forward

The trigger stays attached through outreach, replies, and into the deal room. Every touch is contextual.

Voice gets sharper

Message Intelligence learns from every edit. Drafts converge on how you actually write.

See Orcha for sdrs

One platform for the entire prospecting workflow — tuned to how you actually work.

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