A customer-led workflow
Transform your sales pipeline with AI-powered insights that predict deal outcomes, surface hidden risks, and provide recommendations that help you close more deals faster — replacing gut-feel forecasting with data you can trust.
Customer evidence
“Looking back, we were lucky to go with BuildBetter. A lot of other tools were extremely rigid. You've genuinely delivered on the tailoring — it's never been easier to work with any company we've bought from, ever.”
Start with the account history and original conversation. Use that context to prepare the next step, then share what was agreed with the team delivering it.
01
AI scores every deal across four dimensions — engagement, momentum, fit, and risk — updated in real time after every interaction. See at a glance which deals are on track and which need immediate attention, with specific recommended actions for each.
02
Get contextual alerts when deal health drops, champions go dark, close dates push, or competitors are mentioned multiple times. Risk detection happens continuously, not just when someone updates the CRM.
03
Identify which stages are creating bottlenecks, how long deals spend in each phase, and what activities consistently accelerate deals through the funnel. Stop guessing at root causes and see the data.
04
Map every engaged stakeholder per deal, score their relationship strength, and surface gaps — missing economic buyers, unengaged executive sponsors, single-threaded dependencies — before they become deal killers.
05
Replace painful weekly forecast calls with AI-generated predictions that show confidence levels by deal, highlight where rep commits diverge from AI predictions, and surface which deals need conversation before the quarter ends.
06
For every deal in the pipeline, get specific AI recommendations — which stakeholder to engage, what content to share, which activity historically accelerates similar deals — grounded in your own won deal history.
Integrate your CRM (Salesforce or HubSpot), connect email and calendar for communication tracking, and enable call recording. Import 6 months of historical deal data so AI predictions are accurate from day one.
Define your winning deal characteristics and set up risk signals — no activity in 14+ days, competitor mentioned 3+ times, decision maker not engaged, budget concerns raised, or timeline pushing repeatedly.
Create filtered views for deals needing attention (score below 60), fast movers, at-risk deals closing this quarter, and ready-to-close opportunities. Set up Slack and email notifications for priority risk alerts.
Refine your scoring model across engagement (30%), momentum (25%), fit (25%), and risk (20%) dimensions. Calibrate weights based on which factors best predict your historical win patterns.
Analyze average time in each stage, identify the biggest bottlenecks, and map which activities — executive briefings, ROI workshops, reference calls, site visits — consistently reduce cycle length.
Set coverage standards per deal stage and use stakeholder maps to identify which deals are dangerously single-threaded. Deals with 3+ engaged contacts close 67% more often.
Set up high-priority alert rules for critical risk events — score drops over 15% in 48 hours, no contact in 10+ days in late stages, champion gone dark, new unknown stakeholder appears — with context-rich notifications and recommended actions.
Pre-populate deal reviews with AI confidence scores, risk mitigation plans, and upside opportunities. When AI and rep forecasts differ by more than 20%, the AI is right 78% of the time — use that data.
Analyze won deals from the last 6 months to build an ICP scorecard, score your current pipeline against it, and reallocate resources toward high-fit deals while deprioritizing poor-fit opportunities early.
Bring a real question. We’ll walk through the context and the next step together.
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