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.
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 →
Forecast Accuracy ±12%
Instead of the simple stage-weighted math every CRM defaults to, Aeion computes an activity-velocity-adjusted forecast — factoring in engagement decay, sentiment, and deal momentum to produce a real per-deal close confidence. The board stops getting surprised at quarter-end.
Per-Call AI Coaching
Aeion Connect records and transcribes every sales call, then AeionClaw reads the transcript for objections, missed discovery questions, competitive mentions, and talk-track adherence — turning it into a per-AE coaching report for managers, without anyone re-listening to the recording. Replaces Gong-style intelligence.
Pre-Call Briefings
Before every call, AI auto-generates a prep document from deal history, contact engagement, product usage, and competitive context — cutting AE prep time from 15 minutes to about 2.
AE Productivity Analytics
Per-AE activity metrics, per-stage conversion rates, deal-health distribution, and time-to-close trends roll up automatically, and AI flags both your high performers and the reps who need coaching — before it shows up in a missed quota.
Native Outbound Sequencing
Email, LinkedIn, and phone cadences run as one sequence, personalized per prospect by AI, with A/B-tested subject lines built in. Replaces Outreach / SalesLoft. Read Marketing →
Real-Time CRO Dashboard
Pipeline value by stage, AE, and segment; forecast-accuracy tracking; conversion funnel by source; CAC and LTV by source — all live, with no per-analyst Looker license to budget for.
Per-Deal Audit Trail
Every interaction — email, call, meeting, doc share, demo attendance — is captured automatically, so when a deal slips you get a full forensic timeline instead of a guess, and when it closes you get the same timeline as a case study.
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)
- Forecast accuracy ±30% → ±12% (CRO + CFO planning)
- Worked example: $20M ARR, 20-AE team → ~$2.5M/year revenue impact
See the Sales Pipeline Analyst Use Case for the full deployment walkthrough with day-by-day economics.
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.
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).