Unify deal intelligence, pipeline forecasting and quote-to-cash orchestration with AI-powered revenue operations that close gaps and accelerate growth.
AI-powered deal analysis combining CRM pipeline data, conversation intelligence, email engagement, product fit, and competitive signals.
B2B sales organizations generate massive volumes of deal activity across CRM, calls, emails, and product usage — but lack the ability to synthesize these signals into actionable deal intelligence:
Reps update CRM subjectively; managers lack objective signals of deal progression or stall
Thousands of sales calls happen weekly; insights (objections, competitor mentions, champion engagement) are lost
Critical buyer engagement signals in email threads (multi-threading, executive involvement, response latency) are invisible to leadership
Deals sold without understanding product fit result in churn; usage data never reaches sales
Competitor involvement is discovered late; no systematic tracking of competitive mentions across conversations and emails
Composite score combining CRM stage progression, conversation sentiment/momentum, email engagement velocity, stakeholder mapping completeness, and product-fit signals — updated continuously per deal.
NLP on call recordings: extract competitor mentions, objections raised, decision criteria (MEDDPICC), champion identification, next steps commitment, and deal risk signals.
Analyze email threads per deal: multi-threading depth, executive engagement, response time trends, attachment opens, and ghost-risk detection.
Correlate product usage data with deal context: prospect already using free tier → expansion signal; usage declining at renewal customer → churn risk.
Aggregate competitor mentions from calls, emails, and intent data; surface competitive battle cards and win/loss patterns per competitor.
Source systems mapped into the NexusAI ontology, reasoned over, and turned into the KPIs your teams track.
NexusAI ontology · reasoning · agents
| Ontology entity | Source system | Key fields |
|---|---|---|
| Deal / Opportunity | Salesforce CRM | Opportunity_ID, Account_ID, Amount, Stage, Close_Date, Owner, MEDDPICC_Score |
| Conversation Record | Gong / Call Platform | Call_ID, Opportunity_ID, Participants, Duration, Transcript, Sentiment_Score, Topics |
| Email Thread | Email Platform | Thread_ID, Opportunity_ID, Participants, Message_Count, Response_Time_Avg, Executive_Flag |
| Product Usage | Product Platform | Account_ID, Product, Feature_Adoption_Pct, MAU, Usage_Trend, License_Utilization |
| Competitive Signal | Market Intelligence | Signal_ID, Competitor, Source (Call/Email/Intent), Opportunity_ID, Detection_Date |
Pull Salesforce opportunity + all related calls from Gong + email threads + product usage + intent signals into unified deal context
Process call transcripts: extract MEDDPICC elements, competitor mentions, objections, next-step commitments
Calculate per-deal: multi-thread depth, executive involvement ratio, response velocity trend, attachment engagement, and ghost risk
Map current usage against proposed expansion for upsells; match prospect firmographics + intent signals against ICP for new logos
Aggregate all competitor signals; surface relevant battle card; calculate competitive threat score per deal
Weighted ensemble: CRM progression (25%) + conversation momentum (25%) + email engagement (20%) + product fit (15%) + competitive position (15%) → deal health score 0-100
Deal intelligence dashboard for sales leadership; deal-level risk cards for reps; conversation insights pushed to Salesforce activity; competitive alerts to enablement.
% of pipeline with deal health score above threshold
Closed-won / total qualified opportunities
Days from opportunity creation to close
% of qualified opps with complete MEDDPICC scoring
% of sales meetings with recorded and analyzed calls
Average unique buyer contacts engaged per deal
Opportunity stage unchanged >14 days with declining engagement
Primary champion no response >10 days on active deal
Competitor mention in call or email on deal >$100K
No meetings, calls, or emails on opportunity >21 days
Prospect use case does not match top 3 ICP segments
Annually from improved win rates, shorter sales cycles, better competitive positioning, and manager productivity — across a B2B SaaS company with $120M annual pipeline and 50+ AEs.
Gong API integration required for call transcript and metadata ingestion; ensure recording consent and compliance with regional regulations
Email integration via Exchange/Gmail API requires OAuth scoping limited to sales team mailboxes; PII filtering applied before ontology ingestion
MEDDPICC field mapping is customizable per sales methodology; initial configuration requires sales ops input on stage definitions and scoring weights
Product telemetry pipeline must expose account-level usage metrics; coordinate with product engineering on data availability and refresh cadence
CRM data hygiene is a prerequisite — deduplicate contacts, standardize opportunity stages, and enforce required fields before AI scoring is reliable
AI-driven pipeline risk identification, deal-level probability scoring, and bottoms-up revenue forecasting.
Revenue forecasting remains one of the most consequential and least reliable processes in B2B organizations. CRM-based forecasting relies on rep judgment and stage-based probabilities that consistently miss:
60%+ of B2B companies miss quarterly forecasts by >10%; CFOs and boards lose confidence
3x pipeline coverage looks healthy on paper but hides concentration risk, stage inflation, and dead deals
Deals slip in the final 2 weeks when it's too late to recover; no early warning system
Reps advance deals to later stages without objective evidence of buyer progression
Upcoming renewals at risk of churn or downsell are not surfaced until 30 days before expiry
CRM pipeline and ERP revenue recognition operate independently; no real-time revenue projection aligned to ASC 606
Score every deal on slip/loss probability using deal velocity, engagement signals, historical patterns, and competitive intelligence — enabling managers to focus on at-risk deals with highest recovery potential.
ML-driven forecast combining deal-level probabilities, historical conversion rates by stage/segment/rep, and seasonal patterns — producing a range-based forecast (best/likely/worst).
Diagnose pipeline quality: coverage ratio by segment, stage distribution vs. healthy benchmarks, velocity by cohort, concentration risk, and aging analysis.
Predict renewal probability, expansion likelihood, and downsell/churn risk using product usage, support tickets, NPS, and billing patterns — surfacing at-risk ARR 90+ days before renewal.
Project recognized revenue per ASC 606 rules: map pipeline to expected bookings → billing schedule → revenue recognition timing.
Source systems mapped into the NexusAI ontology, reasoned over, and turned into the KPIs your teams track.
NexusAI ontology · reasoning · agents
| Ontology entity | Source system | Key fields |
|---|---|---|
| Pipeline Snapshot | Salesforce CRM | Snapshot_Date, Opportunity_ID, Stage, Amount, Close_Date, Forecast_Category, Owner |
| Deal Velocity | Salesforce CRM + Gong/Email | Opportunity_ID, Days_In_Stage, Stage_Progression_Rate, Engagement_Score, Last_Activity_Date |
| Renewal Record | Oracle ERP / Finance | Contract_ID, Account_ID, ARR, Renewal_Date, Term_Length, Usage_Score, NPS |
| Revenue Schedule | Oracle ERP / Finance | Booking_ID, Billing_Schedule, Recognition_Start, Recognition_End, ASC606_Category, Amount |
| Forecast Submission | Salesforce CRM | Rep_ID, Period, Category (Commit/Best Case/Pipeline), Amount, Submission_Date |
Daily snapshot of full Salesforce pipeline: stage, amount, close date, forecast category, and owner — building longitudinal dataset for pattern detection
For each deal: calculate days-in-stage, stage progression velocity, engagement trend, and compare against historical cohort benchmarks
ML model classifies each deal as On Track / At Risk / Likely Slip / Likely Loss based on velocity, engagement, competitive signals, and historical win patterns
For upcoming renewals (90-day window): combine product usage trends, support ticket volume, NPS, payment history, and executive engagement into renewal probability score
Aggregate deal-level probabilities into segment/region/rep forecasts; apply historical conversion adjustment factors; generate best/likely/worst range
Map forecasted bookings to billing schedules and ASC 606 recognition timing; surface gaps between CRM pipeline and finance revenue projections
Forecast dashboard for CRO/CFO; weekly pipeline risk digest for managers; renewal risk alerts for customer success; finance-aligned revenue projection for FP&A.
Actual revenue vs. committed forecast at quarter end
Weighted pipeline / quota (adjusted for stage probability)
% of deals that push close date past current quarter
% of ARR retained at renewal (gross retention)
Projected vs. actual recognized revenue per ASC 606
Weighted pipeline drops below 2x of remaining quota mid-quarter
Deal in commit category scores >60% slip probability
Deal advanced to later stage without corresponding engagement increase
Renewal within 90 days with renewal probability <60%
Top 5 deals represent >40% of quarterly target
Annually from improved forecast accuracy, reduced pipeline surprise losses, higher gross retention, and manager productivity — across a B2B SaaS company with $100M ARR and 200+ person sales organization.
Pipeline snapshot infrastructure requires daily Salesforce extract with full opportunity history; configure change data capture or scheduled SOQL exports
Deal velocity scoring depends on Deal Intelligence app outputs (conversation and email engagement signals); deploy P0 app first for maximum accuracy
Renewal risk model requires Oracle ERP subscription and billing data; coordinate with finance on ARR calculation methodology and contract term mapping
ASC 606 revenue recognition rules must be codified with finance team; different booking types (new, expansion, renewal) follow distinct recognition schedules
Historical forecast accuracy calibration requires 4-6 quarters of pipeline snapshot data; initial deployment uses industry benchmarks until sufficient data accumulates
Real-time coordination across CRM, finance, billing, and contract systems — eliminating handoff gaps in quote-to-cash.
The quote-to-cash lifecycle spans multiple systems and teams — sales, deal desk, legal, finance, billing, and collections — with manual handoffs at every junction that create delays, errors, and revenue leakage:
Deals close in Salesforce but contracts, billing, and provisioning happen in separate systems with manual handoffs taking 5-15 days
Pricing discrepancies between CRM quotes and ERP billing, unapplied payments, and missed renewal billing erode 2-5% of revenue
Contract terms (ramp schedules, usage tiers, multi-year commits) are in PDFs that never sync to billing system
Sales compensation relies on manual CRM-to-finance matching; disputes consume 15-20% of finance team time each quarter
Collections follow static aging rules rather than customer-context-aware strategies
End-to-end orchestration from CPQ quote approval → contract generation → billing schedule creation → revenue recognition → commission calculation — reducing handoff from days to hours.
AI reconciliation across CRM (bookings), billing (invoiced), collections (payments), and recognition (ASC 606) to detect and quantify leakage with root cause classification.
NLP extraction of contract terms (ramp schedules, usage tiers, SLAs, renewal terms) into structured billing and recognition rules.
Rules-driven commission calculation from closed-won CRM data + ERP payment data; automated plan modeling, quota attainment tracking, and payout scheduling.
Account lead
Decision makerChampionFinance approver
Procurement
Buying group · 5 stakeholdersAI-scored A/R prioritization combining payment probability, customer value, relationship context, and optimal outreach timing — dynamic collection strategies.
Source systems mapped into the NexusAI ontology, reasoned over, and turned into the KPIs your teams track.
NexusAI ontology · reasoning · agents
| Ontology entity | Source system | Key fields |
|---|---|---|
| Quote | Salesforce CRM (CPQ) | Quote_ID, Opportunity_ID, Product_Lines, Total_Amount, Discount_Pct, Approval_Status, Ramp_Schedule |
| Contract Record | Document Management | Contract_ID, Account_ID, Effective_Date, Term_Months, Renewal_Type, Usage_Tiers, SLA_Terms |
| Billing Schedule | Oracle ERP / Finance | Billing_ID, Contract_ID, Billing_Frequency, Amount, Start_Date, End_Date, Usage_Component |
| Commission Record | Salesforce CRM + Oracle ERP | Rep_ID, Opportunity_ID, Booking_Amount, Payment_Status, Plan_Type, Rate, Attainment_Pct |
| AR Aging | Oracle ERP / Finance | Invoice_ID, Account_ID, Amount, Due_Date, Days_Outstanding, Payment_History, Dunning_Stage |
On CPQ quote approval: validate pricing against contract terms, discount authority matrix, and product configuration rules; flag discrepancies before booking
NLP processing of signed contracts: extract ramp schedules, usage tiers, payment terms, renewal clauses, and SLA commitments into structured data
Map extracted contract terms to Oracle ERP billing schedules: create invoice cadence, apply ramp adjustments, configure usage-based billing thresholds
Apply ASC 606 rules to each booking: determine performance obligations, allocate transaction price, set recognition schedule
Continuous reconciliation: CRM booking amount vs. billing schedule total vs. collected revenue vs. recognized revenue — surface and classify gaps
On payment confirmation: calculate commission per plan rules, apply accelerators/decelerators based on quota attainment, generate payout schedule
Quote-to-cash status dashboard for revenue ops; leakage alerts for finance; commission statements for reps; intelligent collection queue for AR team.
Days from quote approval to first invoice sent
% variance between CRM bookings and billed revenue
Average days to collect on invoices
% of commission payments disputed by reps
% of overdue AR collected within 30 days of due date
CRM booking amount differs from billing schedule by >1%
Monthly reconciliation surfaces >$50K unexplained variance
Rep disputes payout; CRM and ERP data disagree
Invoice >$100K past due >30 days on strategic account
Billing schedule does not match extracted contract terms
Annually from eliminated revenue leakage, faster cash collection, commission automation, and quote-to-cash acceleration — across a B2B SaaS company with $100M ARR and complex multi-product billing.
CPQ integration requires Salesforce CPQ or equivalent quote system with API access for real-time quote validation and booking triggers
Contract NLP accuracy depends on document standardization; prioritize standard templates first (>90% extraction accuracy), then address bespoke agreements
Oracle ERP billing configuration requires finance team involvement for ramp schedule logic, usage-based billing rules, and ASC 606 recognition categories
Commission plan modeling must accommodate multiple plan types (AE, SDR, overlay, management); initial deployment should cover top-of-house plans first
AR aging integration requires real-time payment feeds from Oracle ERP; customer value scoring uses firmographic data + lifetime revenue for intelligent prioritization
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Recorded in the live NexusAI product.