Your sales reps spend 72% of their time on everything except selling. That is a structural problem, not a motivation problem.
We build AI systems that score leads automatically, forecast pipeline with 85%+ accuracy, eliminate CRM busywork, and pull coaching insights from every sales call. Deployed on your existing stack in 3-4 weeks.
Your sales team has a systems problem disguised as a performance problem.
Every lead gets the same treatment, and most of them are worthless
Your sales team of 15 reps is treating every inbound lead the same. The marketing-qualified lead from a fintech company with 200 employees who downloaded three whitepapers gets the same follow-up cadence as the student who filled out a form for a free resource. Your reps are spending 40% of their prospecting time on leads that were never going to convert. Meanwhile, the high-intent leads are waiting 36-48 hours for a response because nobody can tell, at a glance, which ones matter. By the time your rep calls back, the prospect has already spoken to two competitors.
Your pipeline forecast is fiction
Every Monday, your sales manager asks the team for pipeline updates. Every rep inflates their numbers slightly because optimism is easier than accuracy. The VP reports a $480K pipeline to the board. By quarter end, 35% of those deals have slipped or gone dark, and nobody saw it coming because the forecast was built on gut feel and self-reported stage progression. AI-powered forecasting closes that gap. The difference between reliable and unreliable forecasting is the difference between hiring ahead of demand and scrambling to explain a revenue miss.
Your CRM is a graveyard of incomplete data
Your reps spend 5-6 hours per week on manual CRM data entry. They hate it, so they do it poorly. Most of your CRM data is incomplete or inaccurate. Fields are blank, notes are cryptic two-word entries, and deal stages have not been updated in weeks. Your sales ops team spends another 10 hours per week trying to clean this data for reporting. The result: a CRM that costs you $25,000 a year in licensing and labor but tells you almost nothing useful about your pipeline.
Coaching is based on outcomes, not behaviour
Your sales managers listen to maybe 2-3 calls per rep per month. They coach based on whether deals closed, not on what actually happened in the conversation. They have no systematic way to know that one rep talks 75% of the time and never asks discovery questions, or that another rep consistently fails to handle the pricing objection. Without conversation intelligence, coaching is anecdotal, inconsistent, and always too late.
Diagnose. Build. Deploy. Measure. 3-4 weeks.
We work on top of your existing CRM and sales stack. No rip-and-replace. No six-month implementation. We identify the highest-impact AI interventions, build them, and deploy them with your team trained and the system producing measurable results.
Sales Process Audit
Days 1-3
We map your entire sales process end to end: lead sources, qualification criteria, handoff points, pipeline stages, forecasting methods, and coaching workflows. We pull data from your CRM to identify where deals stall, which lead sources actually convert, how long reps take to follow up, and where data quality breaks down. We interview reps, managers, and sales ops to document what actually happens, not what people think happens. The output is a gap analysis showing exactly where AI will make a difference and where it will not.
Deliverable: Sales process audit with data-backed gap analysis, lead-to-close funnel metrics, and prioritized AI opportunity map
Model Design & Integration Planning
Days 4-7
For each AI intervention we are deploying, we design the model architecture, define data inputs, set scoring thresholds, and plan the integration with your existing systems. For lead scoring, we identify the 15-25 signals that predict conversion in your specific business. These are behavioural signals from your CRM, website, and communication history, not generic firmographic data. For forecasting, we map the deal velocity patterns in your historical data. You review and approve everything before we build.
Deliverable: Model design document with data requirements, scoring logic, integration architecture, and CRM workflow specifications
Build, Train & Test
Days 8-18
We build and train each AI system using your historical data. Lead scoring models are backtested against 6-12 months of closed-won and closed-lost deals to validate accuracy before going live. Forecasting models are tested against known outcomes. CRM automation workflows are built and tested with real data. Conversation intelligence is configured, calibrated against sample calls, and tested for accuracy on your team's actual selling patterns. Nothing goes live until it proves it works on your data.
Deliverable: Trained and validated AI models, CRM automations deployed in staging, conversation intelligence configured and calibrated
Deployment & Enablement
Days 19-25
We deploy to production, run parallel testing for one week, and train your entire sales team, managers included. Reps learn how to read lead scores and prioritize their day. Managers learn how to use forecast dashboards and conversation analytics for coaching. Sales ops learns how to monitor model performance and flag when retraining is needed. You get documentation, runbooks, and a 30-day measurement plan so you can track the impact from day one.
Deliverable: Production deployment, team training sessions (recorded), documentation, monitoring dashboards, and 30-day measurement plan
Sales Process Audit
Days 1-3
We map your entire sales process end to end: lead sources, qualification criteria, handoff points, pipeline stages, forecasting methods, and coaching workflows. We pull data from your CRM to identify where deals stall, which lead sources actually convert, how long reps take to follow up, and where data quality breaks down. We interview reps, managers, and sales ops to document what actually happens, not what people think happens. The output is a gap analysis showing exactly where AI will make a difference and where it will not.
Deliverable: Sales process audit with data-backed gap analysis, lead-to-close funnel metrics, and prioritized AI opportunity map
Model Design & Integration Planning
Days 4-7
For each AI intervention we are deploying, we design the model architecture, define data inputs, set scoring thresholds, and plan the integration with your existing systems. For lead scoring, we identify the 15-25 signals that predict conversion in your specific business. These are behavioural signals from your CRM, website, and communication history, not generic firmographic data. For forecasting, we map the deal velocity patterns in your historical data. You review and approve everything before we build.
Deliverable: Model design document with data requirements, scoring logic, integration architecture, and CRM workflow specifications
Build, Train & Test
Days 8-18
We build and train each AI system using your historical data. Lead scoring models are backtested against 6-12 months of closed-won and closed-lost deals to validate accuracy before going live. Forecasting models are tested against known outcomes. CRM automation workflows are built and tested with real data. Conversation intelligence is configured, calibrated against sample calls, and tested for accuracy on your team's actual selling patterns. Nothing goes live until it proves it works on your data.
Deliverable: Trained and validated AI models, CRM automations deployed in staging, conversation intelligence configured and calibrated
Deployment & Enablement
Days 19-25
We deploy to production, run parallel testing for one week, and train your entire sales team, managers included. Reps learn how to read lead scores and prioritize their day. Managers learn how to use forecast dashboards and conversation analytics for coaching. Sales ops learns how to monitor model performance and flag when retraining is needed. You get documentation, runbooks, and a 30-day measurement plan so you can track the impact from day one.
Deliverable: Production deployment, team training sessions (recorded), documentation, monitoring dashboards, and 30-day measurement plan
AI systems that sell. Not slide decks about AI in sales.
Audit & Design (Days 1-7)
- Sales process audit with lead-to-close funnel analysis and conversion bottleneck identification
- AI opportunity map ranked by revenue impact and implementation feasibility
- Model design with scoring logic, data requirements, and integration architecture
Build & Test (Days 8-18)
- Predictive lead scoring model trained on your historical CRM data
- Pipeline forecasting system with deal-level probability scoring
- CRM automation: auto-logging, activity capture, deal stage progression, and follow-up triggers
- Conversation intelligence setup with call analysis, talk-ratio tracking, and objection detection
Deploy & Enable (Days 19-25)
- Production deployment integrated with your CRM and sales tools
- Rep-level and manager-level training sessions (recorded)
- Sales ops runbook for model monitoring, threshold adjustment, and retraining triggers
- 30-day measurement plan with baseline metrics and target KPIs
We build AI for your sales team. These are separate engagements.
We scope tightly so timelines stay honest and results stay measurable. Each of these is available as a standalone service.
Revenue operations redesign and sales-marketing alignment
If your problem is structural (territories, comp plans, handoff processes between marketing and sales, or GTM architecture) that's a RevOps engagement. AI for sales assumes your sales process works but needs to be faster, smarter, and more consistent.
Revenue OperationsData infrastructure, warehouse setup, or analytics platform build
If your CRM data is fundamentally broken (duplicates everywhere, no single source of truth, multiple disconnected systems) you need a data foundation before AI can add value. We can build that, but it's a different project with a different timeline.
Data AnalyticsWorkflow automation beyond sales (HR, finance, operations)
If you want to automate processes outside of sales (invoice processing, employee onboarding, reporting workflows) that falls under our broader automation service. Same methodology, different scope.
Business AutomationIs this right for you?
Right for you if
- You have a sales team of 10-50 reps, a CRM with at least 6 months of historical data, and you know your team is leaving revenue on the table because they cannot tell good leads from noise.
- Your pipeline forecasting is manual, your conversion rates are inconsistent across reps, and your sales managers are coaching based on instinct rather than data.
- You want measurable results in weeks, not a multi-quarter transformation programme. You need AI that works inside your current CRM, not a new platform to adopt.
- You are spending $60,000 or more annually on your sales team and suspect that better tooling would improve output by 20-30% without adding headcount.
Not right if
- You have fewer than 5 sales reps or less than 6 months of CRM data. AI models need a baseline of historical patterns to be accurate. Below that threshold, the investment does not justify the return.
- You do not have a CRM, or your sales process is not yet defined. AI sharpens an existing process. It does not create one from scratch. Start with our RevOps service to build the foundation first.
What AI for sales looks like in practice.
Problem
A SaaS company with 30 sales reps was processing 2,000+ inbound leads per month with no scoring system. Reps cherry-picked leads based on company name recognition, leaving 60% of leads untouched for 72+ hours. Conversion rate from MQL to SQL was 8%, well below the 15% industry benchmark.
What we did
Built a predictive lead scoring model using 14 months of CRM data, integrating website behaviour, email engagement, firmographic data, and product usage signals. Deployed automated lead routing that assigned high-score leads to reps within 5 minutes and triggered nurture sequences for medium-score leads.
Outcome
MQL-to-SQL conversion rate improved from 8% to 19%. Average lead response time dropped from 48 hours to 23 minutes. Sales cycle shortened by 22%. The team closed 31% more revenue in Q2 without adding a single rep.
Problem
A lending platform with a 25-person sales team had a pipeline forecasting accuracy of 55%. The VP of Sales was consistently off by $180-$250K per quarter, making resource planning and cash flow management unreliable. Deal slippage was the primary issue. Reps marked deals as 'likely to close' based on verbal confirmations that meant nothing.
What we did
Deployed a pipeline forecasting model that scored every deal based on 22 behavioural signals: email response velocity, number of stakeholders engaged, document requests, pricing discussion patterns, and historical close rates by segment. Replaced subjective stage progression with data-driven probability scores.
Outcome
Forecast accuracy improved from 55% to 88%. Deal slippage reduced by 40%. The VP now identifies at-risk deals 3 weeks earlier and can intervene before they go dark. Quarterly revenue variance dropped to under 5%.
Problem
A D2C brand selling through a B2B wholesale channel had 12 field sales reps managing 800+ retailer accounts. Reps logged call notes manually, and the sales manager had no visibility into conversation quality. Top-performing reps were closing 3x more than bottom performers, but nobody could explain why because the data did not exist.
What we did
Deployed conversation intelligence across all sales calls. Built automated CRM logging from call transcripts: notes, next steps, and deal stage updates written directly into the CRM without rep intervention. Created a coaching dashboard showing talk-to-listen ratios, discovery question frequency, objection handling patterns, and competitive mention tracking.
Outcome
CRM data completeness went from 35% to 94%. Bottom-quartile reps improved close rates by 28% within 60 days using targeted coaching from conversation analytics. The sales manager now reviews 100% of calls algorithmically instead of 5% manually. Rep onboarding time dropped from 90 days to 45 days.