Use Cases
Compound AI workflows we build for GTM teams. Not single features — full systems that run autonomously.
AI Pipeline Monitoring
Know a deal is dying before your rep does.
AI Pipeline Monitoring
Know a deal is dying before your rep does.
Deal Risk Alert — Acme Corp ($85K)
The Problem
Pipeline reviews happen weekly — by then, deals have already stalled. Reps sit on aging opportunities, managers find out too late, and forecasts are built on gut feel instead of signals.
What We Build
We build an AI monitoring layer that scans your pipeline continuously. It flags risk the moment patterns emerge and routes alerts with context so reps act in hours, not weeks.
How It Works
- 1 AI agent scans your pipeline on a schedule — hourly, daily, or triggered by deal changes
- 2 Flags deals crossing risk thresholds: stalled engagement, slipping close dates, single-threaded contacts
- 3 Sends Slack alerts with deal context, risk reason, and recommended next step
- 4 Generates pipeline digests with movement summary, risk distribution, and forecast signals
Before
Found stale deals in weekly pipeline review. Manager discovers risk 5–7 days late. Forecast accuracy under 60%.
After
Risk flagged same-day. Rep acts within hours. Pipeline digest replaces the meeting. Forecast grounded in real signals.
AI Rep Prep
Every rep walks in at 100%. Every time.
AI Rep Prep
Every rep walks in at 100%. Every time.
The Problem
Reps spend 20 minutes before each call pulling context from the CRM, LinkedIn, and past notes. After the call, they spend another 15 minutes logging notes — if they do it at all. MEDDIC fields sit empty.
What We Build
We build an AI prep-and-process system. Before every call, a brief is auto-generated with deal context and stakeholder intel. After, transcripts are processed into CRM updates and MEDDIC fields automatically.
How It Works
- 1 Calendar trigger fires 30 minutes before each meeting
- 2 AI pulls CRM data, past emails, call transcripts, and LinkedIn signals into a structured brief
- 3 Brief delivered to Slack or email with deal context, stakeholder map, and open questions
- 4 Post-call: transcript processed into CRM notes, MEDDIC fields, and follow-up tasks automatically
Before
Reps wing calls with minimal prep. Post-call notes are inconsistent. MEDDIC coverage under 30%.
After
Every rep gets a brief 30 min before the call. CRM updated automatically post-call. MEDDIC coverage hits 90%+.
AI Lead Operations
Form fill to the right rep in under 60 seconds.
AI Lead Operations
Form fill to the right rep in under 60 seconds.
The Problem
Leads sit in a queue for hours. Round-robin ignores fit. Reps waste time on unqualified leads while hot ones cool off. Enrichment happens manually — if it happens at all.
What We Build
We build an AI lead operations pipeline. The moment a lead hits your CRM, it's enriched, scored on real signals, and routed to the best-fit rep — not the next rep in line.
How It Works
- 1 Lead enters CRM via form, import, or API
- 2 AI enriches with firmographic, technographic, and intent data in real time
- 3 Scoring model evaluates fit + intent signals and assigns a score with reasoning
- 4 Smart routing matches lead to best-fit rep based on territory, expertise, and capacity
Before
Leads wait 4+ hours for assignment. Round-robin ignores fit. No enrichment until a rep manually researches.
After
Leads enriched, scored, and routed in under 60 seconds. Best-fit rep gets full context on arrival.
AI CRM Hygiene
Clean CRM without changing rep behavior.
AI CRM Hygiene
Clean CRM without changing rep behavior.
The Problem
CRM data decays 30% per year. Reps don't update fields. Duplicates multiply. Marketing segments break. Reports become unreliable — and nobody trusts the data enough to act on it.
What We Build
We build an AI hygiene layer that runs continuously. It enriches missing fields, flags stale records, merges duplicates, and monitors data quality — all without requiring reps to change a single habit.
How It Works
- 1 AI scans CRM records on a schedule to detect gaps, stale data, and duplicates
- 2 Missing fields auto-enriched from external data sources — firmographics, job titles, company info
- 3 Duplicate detection runs fuzzy matching across contacts, companies, and deals
- 4 Hygiene dashboard surfaces data quality score and trends over time
Before
30% of CRM records are stale or incomplete. Duplicates cause routing errors. Reports can't be trusted.
After
CRM fields 95%+ complete. Duplicates caught on entry. Data quality score tracked weekly.
AI Revenue Forecasting
A forecast that updates itself and actually predicts outcomes.
AI Revenue Forecasting
A forecast that updates itself and actually predicts outcomes.
Weekly Forecast Update — Q1 2026
The Problem
Forecasts are built in spreadsheets from rep self-reports. They're stale by the time they're shared. Stage-based models don't account for deal health. Leadership makes decisions on data that's a week old.
What We Build
We build an AI forecasting engine that scores every deal in real time. It analyzes engagement velocity, stakeholder activity, and historical patterns to generate forecasts that actually predict outcomes.
How It Works
- 1 Every deal gets a health score based on engagement patterns, not just stage
- 2 Stage velocity analysis flags deals moving faster or slower than benchmark
- 3 Rep calibration layer adjusts for individual forecasting tendencies
- 4 Forecast updates in real time as deal signals change — delivered to Slack and dashboards
Before
Forecast built on rep gut feel. Updated weekly in spreadsheets. Accuracy under 60%. Leadership flying blind.
After
Forecast updates in real time. Deal health scored automatically. Accuracy exceeds 85%. Leadership trusts the number.
AI Competitive Intelligence
Extract intel from every conversation. Route it to the right people.
AI Competitive Intelligence
Extract intel from every conversation. Route it to the right people.
Competitor Mention — Gong vs. NeuroGTM (Dataflow deal)
The Problem
Competitive intel lives in rep heads and scattered Slack messages. Nobody systematically captures mentions from calls. Product hears about competitor moves weeks late. Battle cards are always outdated.
What We Build
We build an AI intelligence layer that monitors every conversation for competitive signals. Mentions are extracted, categorized, and routed to product, marketing, and enablement — automatically.
How It Works
- 1 AI processes call transcripts and emails for competitor mentions and buying signals
- 2 Mentions categorized by competitor, theme (pricing, features, positioning), and deal stage
- 3 Alerts routed to relevant teams — product gets feature gaps, enablement gets objection patterns
- 4 Champion monitoring tracks stakeholder changes and engagement shifts across active deals
Before
Competitive intel is anecdotal. Product hears about competitor moves weeks late. Battle cards are stale.
After
Every competitor mention captured and categorized. Product gets real-time intel. Battle cards update from real data.