A Pipeline That Talks Back.

VPs Sales and CROs spend 30-40% of their week reconciling pipeline truth across Salesforce + Gong + Clari + Outreach + Slack + custom forecasting spreadsheets — paying per-seat at every layer for visibility they could get natively. Aeion's revenue stack — CRM + Claw + Connect + Marketing + BI — unifies pipeline, AI sales coaching, call recording, deal-health scoring, forecast accuracy, and AE productivity tracking. Forecast within ±12% (vs ±30% industry-typical). $2.5M+/year revenue impact on a 20-AE team.

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What Aeion Delivers for Sales Leaders

AI Pipeline Analyst

AeionClaw's pipeline-analyst skill watches your pipeline around the clock — sending every AE a morning briefing, flagging stalled deals 30-60 days before they'd otherwise die quietly, scoring each deal's health, and surfacing coaching opportunities for managers. Replaces Clari + Gong AI. Read Claw Pipeline Use Case →

The 20-AE Team Math

Typical sales-stack costs (20 AEs, 3 Sales Managers, 1 VP Sales):

Salesforce Sales Cloud Enterprise: $150-200/AE/month × 24 seats = $43-57K/year
Salesforce CPQ + Engage + extras: $50-100/seat/month = $14-29K/year
Gong: $135-189/AE/month × 24 = $39-54K/year
Clari (Connect Plan): $30-50/seat/month × 24 = $9-15K/year
Outreach: $100-130/AE/month × 20 = $24-31K/year
ZoomInfo Sales: $14K-30K/year platform fee
Chorus: $115/AE/month × 20 = $28K/year (if separate from Gong)
Slack Business+ (sales orgs): $5K-10K/year
Total: $176-258K/year for a mid-market 20-AE team

Aeion replacement:

Aeion platform tier: $40-100K/year for mid-market
LLM inference for sales AI: $3-15K/year
Total: $43-115K/year

Year-1 savings: $61-215K

But the bigger number is revenue impact:

Time-to-hire compression on quota-carrying reps: +1-3 reps' worth of capacity
Close rate +5-10 points (better pre-call prep + objection handling)
Stalled-deal recovery 22% → 41% (early identification)

The Forecast Accuracy Story

Why traditional CRM forecasts are ±30%: Pipeline weighted by stage probability + close date. AEs commit, managers roll up, CRO commits to board. The problem: stage-probability is generic (e.g., "Proposal stage = 60% likely") and close-date is wishful (everyone slips a quarter). The actual deal velocity isn't factored in.

Aeion's activity-velocity-adjusted forecast:

``` Per-deal forecast signals: - Stage age (vs expected stage age for this segment) - Activity velocity (emails, calls, meetings per week) - Engagement sentiment (decay, response latency) - Decision-maker confirmation - Champion engagement strength - Competitor mentions (lost-to-competitor risk) - Pricing-conversation depth (vs deal-stage expectation) - Procurement-cycle progress

AI synthesizes → per-deal close confidence + close-date range Per-AE roll-up → AE-specific forecast bias correction Per-segment roll-up → segment-specific cycle-time correction Final CRO forecast: pipeline-weighted + activity-adjusted + bias-corrected ```

Per-customer accuracy reports:

30-day horizon: typically ±8-12%
60-day horizon: ±12-18%
90-day horizon: ±15-22%
Quarter-end commitment: ±10% (within $1M on $10M-quarter commitments)

vs Salesforce + Clari alternative typical:

30-day: ±20-30%
Quarter-end: ±25-40%

The board planning conversation goes from "we hope to hit $X" to "we'll be within $Y of $X with 80% confidence." That's a categorically different conversation with the board.

What Sales Leaders Stop Doing

Reconciling Pipeline Truth Across 5 Tools

One source of truth replaces the Friday ritual of cross-checking Salesforce against Gong against a forecasting spreadsheet.

Per-AE Weekly 1:1 Prep

Auto-generated coaching reports mean managers walk in with the data already pulled together, not scrambling the night before.

Quarter-End Forecast Spreadsheet Rituals

An AI-narrated forecast replaces the manual roll-up, so the CRO's number is ready the moment the quarter closes.

Cherry-Picking Call Recordings for QBRs

Calls are AI-summarized and categorized automatically, so the best (and worst) examples surface themselves instead of someone hunting for them.

Per-Deal Forensics After Losses

The full timeline and signal history are captured as they happen, so a post-mortem is a lookup, not a reconstruction.

AE Territory + Quota Carving in Spreadsheets

Per-AE optimization AI handles the balancing, replacing the spreadsheet gymnastics of territory-planning season.

Manual Stalled-Deal Review

Deals at risk are auto-surfaced 30-60 days early, instead of being noticed only when they're already dead.

Competitive Intel Collection

Competitor mentions are auto-extracted straight from call transcripts, instead of relying on an AE remembering to log them.

The CRO Dashboard

Real-time pipeline rollup at CRO/VP-Sales level:

``` Top-of-funnel: Leads: 1,247 (vs 1,180 prior month, +5.7%) MQL: 432 (35% lead→MQL conversion) SQL: 187 (43% MQL→SQL conversion) Opps: 89 (48% SQL→Opp conversion)

Pipeline value: By stage: $4.2M weighted ($18.3M unweighted) By segment: Enterprise $7.8M / Mid-market $6.1M / SMB $4.4M By AE rank: Top-3 AEs hold 62% of weighted value By close period: Q3 $1.8M / Q4 $2.4M

Forecast confidence: Q3 commit: $2.1M ± $260K (80% confidence interval) Q4 commit: $2.8M ± $410K H2 commit: $4.9M ± $580K

Pipeline health flags: Stalled deals: 12 ($1.4M at risk) Champion changes: 4 ($820K at risk) Competitive losses-trending: 2 ($340K at risk) Aging deals (>180 days in pipeline): 8 ($1.1M at risk)

AE productivity: Top performer: 142% to quota, 14 calls/day, 35% close rate Median: 87% to quota, 8 calls/day, 22% close rate Bottom 3: 41% to quota, 4 calls/day, 11% close rate (coaching focus)

Per-stage conversion velocity: Discovery → Qualified: 14 days (vs 12 prior month, slowing) Qualified → Proposal: 21 days (stable) Proposal → Close: 38 days (vs 42 prior, faster) ```

All real-time from live CRM data. Updates as activities happen, not on a 4hr ETL refresh.

A pipeline that consolidates instead of just adding another dashboard.

Aeion's pipeline analyst runs continuously — flagging stalled deals weeks before they'd die quietly, scoring deal health, and turning call transcripts into per-AE coaching reports without anyone re-listening to the recording. The activity-velocity-adjusted forecast model targets ±12% accuracy at a 30-day horizon, versus the ±30% typical of stage-weighted CRM forecasting. For a 20-AE team, that consolidates a stack that typically runs $176-258K/year (Salesforce + Gong + Clari + Outreach + ZoomInfo) into a single platform.

AI pipeline analyst running 24/7

Forecast accuracy ±12% at 30-day horizon

Per-call AI coaching + objection detection

Pre-call AI briefings

Per-AE productivity analytics

Native outbound sequencing

Per-deal audit trail (forensics + celebration)

Real-time CRO dashboard

Sales Leader Questions

Gong is best-in-class for call recording + conversation intelligence on the call layer. Aeion is broader — pipeline analysis, deal scoring, briefings, post-call updates, leadership forecasting. Some sales orgs keep Gong for the deep conversational analytics + use Aeion for everything else (pipeline + forecasting + AE productivity). Most replace Gong entirely + accept slightly less-deep call analytics in exchange for unified data.

Singularity Salesforce connector handles standard migration in 14-26 weeks for 200-seat team. AppExchange dependencies are the biggest variable. See Replace Salesforce solution for the full playbook.

Aeion Commerce ships CPQ functionality for SaaS + product configuration. For deeply customized Salesforce CPQ deployments (manufacturing, complex bundling), keep Salesforce CPQ + integrate via Singularity. Most B2B SaaS teams find Aeion CPQ sufficient.

Aeion Marketing handles email + LinkedIn + phone cadences with AI personalization. Outreach has deeper engagement-tracking + integrations; for orgs heavily invested in Outreach (200+ users), keep it + integrate. Most mid-market teams find Aeion Marketing sufficient + simpler.

Native — per-AE territory + quota tracking, per-segment quota allocation, AI surfaces under-quota + over-quota AEs for territory rebalancing. Comp-plan modeling supported.

Aeion CRM supports partner pipeline as first-class. Per-partner deal registration, per-deal partner split, per-partner performance dashboards.

SE / SC pre-sales workflow native. Per-deal SE allocation, demo scheduling, technical-validation tracking, SE utilization analytics. Per-deal SE-to-AE handoff captured.

Per-target-account playbooks via Aeion Marketing + CRM. Per-account buying-committee mapping + multi-touch attribution. Replaces Demandbase / 6sense entry-level features.

Most orgs reduce RevOps headcount by 30-50% — Aeion's AI does much of what RevOps analysts did manually (dashboard maintenance, pipeline cleanup, AE coaching reports). Reassigned to strategic projects (territory planning, comp-plan design, sales-tech evaluation).