Your sales team is guessing. AI fixes that.
AI for sales teams at mid-market companies. Lead scoring, pipeline forecasting, CRM automation, and conversation intelligence. Deployed in 3-4 weeks.
The Problem
This looks like a performance problem. It is a systems problem.
Every lead gets the same treatment, and most are worthless: Your 15 reps treat every inbound lead the same. The fintech MQL who downloaded three whitepapers gets the same cadence as the student who grabbed a free resource. 40% of prospecting time goes to leads that will never convert. The high-intent leads wait 36-48 hours because nobody can tell which ones matter. By the time your rep calls back, the prospect has talked to two competitors.
Your pipeline forecast is fiction: Every Monday, your sales manager asks for pipeline updates. Every rep inflates their numbers because optimism is easier than accuracy. The VP reports $480K to the board. By quarter end, 35% of those deals have slipped or gone dark. Nobody saw it coming because the forecast was gut feel and self-reported stage progression. That gap is the difference between hiring ahead of demand and scrambling to explain a revenue miss.
Your CRM is a graveyard of incomplete data: Reps spend 5-6 hours per week on manual CRM entry. They hate it, so they do it poorly. Fields are blank, notes are cryptic two-word entries, deal stages have not been updated in weeks. Sales ops spends another 10 hours per week cleaning this data for reporting. You pay $25,000 a year in CRM licensing and labor for a system that tells you almost nothing useful.
Coaching is based on outcomes, not behaviour: Sales managers listen to 2-3 calls per rep per month. They coach based on whether deals closed, not what happened in the conversation. They have no way to know that one rep talks 75% of the time and never asks discovery questions, or that another consistently fails to handle the pricing objection. Coaching ends up anecdotal and always too late.
Our Approach
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 find the highest-impact AI interventions, build them, and deploy them with your team trained and results measurable from day one.
Phase 1 — Sales process audit (Days 1-3): Full sales process mapping: lead sources, qualification criteria, handoff points, pipeline stages, forecasting methods, coaching workflows. We pull CRM data to find where deals stall, which sources convert, how fast reps follow up, and where data quality falls apart. Interviews with reps, managers, and sales ops capture what happens versus what people assume. Output: a gap analysis showing exactly where AI makes a difference. Deliverable: Sales process audit with data-backed gap analysis, lead-to-close funnel metrics, and prioritised AI opportunity map
Phase 2 — Model design & integration planning (Days 4-7): For each AI intervention, we design model architecture, define data inputs, set scoring thresholds, and plan integration with your current systems. For lead scoring, we identify 15-25 signals that predict conversion in your business: behavioural signals from CRM, website, and communication history. For forecasting, we map deal velocity patterns in historical data. You review and approve everything before we build. Deliverable: Model design document with data requirements, scoring logic, integration architecture, and CRM workflow specs
Phase 3 — Build, train & test (Days 8-18): Each AI system gets built and trained on your historical data. Lead scoring models are backtested against 6-12 months of closed-won and closed-lost deals. Forecasting models run against known outcomes. CRM automations use your data. Conversation intelligence is calibrated against sample calls and your team's actual selling patterns. Nothing goes live until it works on your data. Deliverable: Trained and validated AI models, CRM automations in staging, conversation intelligence configured and calibrated
Phase 4 — Deployment & training (Days 19-25): Production deployment with one week of parallel testing. Reps learn to read lead scores and prioritise their day. Managers learn forecast dashboards and conversation analytics for coaching. Sales ops learns model monitoring and retraining triggers. You get documentation, runbooks, and a 30-day measurement plan. Deliverable: Production deployment, team training sessions (recorded), documentation, monitoring dashboards, 30-day measurement plan
Deliverables
Audit & Design (Days 1-7)
- Sales process audit with lead-to-close funnel analysis and conversion bottlenecks
- AI opportunity map ranked by revenue impact and 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 with deal-level probability scores
- CRM automation: auto-logging, activity capture, deal stage progression, follow-up triggers
- Conversation intelligence with call analysis, talk-ratio tracking, objection detection
Deploy & Enable (Days 19-25)
- Production deployment integrated with your CRM and sales tools
- Rep and manager training sessions (recorded)
- Sales ops runbook for model monitoring, threshold adjustment, retraining triggers
- 30-day measurement plan with baselines and target KPIs
Who This Is For
Right for you if: You have 10-50 reps, a CRM with at least 6 months of data, and you know your team is leaving revenue on the table because they cannot tell good leads from noise.. Pipeline forecasting is manual, conversion rates vary wildly across reps, and managers coach on instinct rather than data.. You care about speed: results in weeks, inside your current CRM. No new platform, no multi-quarter programme.. You spend $60,000+ annually on your sales team and suspect better tooling would improve output by 20-30% without adding headcount..
Not right if: You have fewer than 5 reps or less than 6 months of CRM data. AI models need historical patterns to be accurate. Below that threshold, the investment does not pay off.. You do not have a CRM, or your sales process is not yet defined. AI sharpens an existing process; it does not create one. Start with our RevOps service..
Use Cases
B2B SaaS: 30 reps, 2,000+ inbound leads per month, no scoring system. Reps cherry-picked leads based on company name, leaving 60% untouched for 72+ hours. MQL-to-SQL conversion was 8%, well below the 15% benchmark. — Built a predictive lead scoring model on 14 months of CRM data: website behaviour, email engagement, firmographics, product usage signals. Deployed automated routing that assigned high-score leads within 5 minutes and triggered nurture sequences for medium-score leads.. Outcome: MQL-to-SQL conversion went from 8% to 19%. 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 rep.
Fintech / Lending: 25-person sales team, 55% forecast accuracy. The VP was off by $180-$250K per quarter, making resource planning unreliable. Reps marked deals as 'likely to close' based on verbal confirmations that meant nothing. — Deployed a forecasting model scoring every deal on 22 behavioural signals: email response velocity, stakeholder engagement, document requests, pricing discussion patterns, historical close rates by segment. Replaced subjective stage progression with data-driven probability scores.. Outcome: Forecast accuracy went from 55% to 88%. Deal slippage dropped 40%. The VP now spots at-risk deals 3 weeks earlier. Quarterly revenue variance fell to under 5%.
D2C / E-Commerce (B2B Channel): 12 field reps managing 800+ retailer accounts. Reps logged call notes manually; the manager had zero visibility into conversation quality. Top performers closed 3x more than bottom performers, but nobody could explain why. The data did not exist. — Deployed conversation intelligence across all calls. Built automated CRM logging from transcripts: notes, next steps, deal stage updates written directly into the CRM without rep input. Created a coaching dashboard with talk-to-listen ratios, discovery question frequency, objection handling, and competitive mentions.. Outcome: CRM data completeness went from 35% to 94%. Bottom-quartile reps improved close rates by 28% within 60 days. The manager now reviews 100% of calls algorithmically instead of 5% manually. Onboarding time dropped from 90 to 45 days.
Results
How a typical project runs
B2B SaaS, lead scoring & pipeline intelligence: 31% revenue increase in one quarter, zero additional headcount. A mid-market SaaS company doing $5M ARR came to us with a common problem: 30 reps, 2,000+ inbound leads per month, no way to tell which were worth pursuing. Reps scrolled through lead lists each morning, picking ones that looked promising based on company name and job title. 60% of leads went untouched for three days or more. The process audit took three days. We pulled 14 months of CRM data and found that actual conversion predictors had almost nothing to do with what reps assumed. Company size and industry were weak signals. The strongest predictors were product page visits in the last 7 days, number of team members who had interacted with content, and whether the lead had viewed the pricing page more than once. We built a scoring model on these behavioural signals plus firmographic data, backtested against 800 historical closed-won and closed-lost deals, and hit 82% prediction accuracy before going live. Deployed inside HubSpot with automated lead routing: high-score leads assigned to reps within 5 minutes via Slack and CRM task creation. Medium-score leads entered a nurture sequence. Low-score leads were deprioritised. Within 30 days, lead response time dropped from 48 hours to 23 minutes. MQL-to-SQL conversion jumped from 8% to 19%. By quarter end, the team closed 31% more revenue than the previous quarter with the same headcount and marketing spend. Engagement cost: $8,000. Incremental revenue in Q2: $475K.
Frequently Asked Questions
How long does an AI for sales engagement take?
3-4 weeks for lead scoring, pipeline forecasting, and CRM automation. Add conversation intelligence and it goes to 4-5 weeks because of calibration on your team's actual calls. Every engagement follows the same structure: audit, design, build, deploy.
What CRM do we need?
Salesforce, HubSpot, Zoho CRM, Freshsales, Leadsquared, Pipedrive. Different CRM? We evaluate API capabilities during the audit. The requirement is 6+ months of historical deal data and an API we can integrate with.
How accurate is the lead scoring model?
75-85% on backtested data, depending on data quality and volume. We validate against your historical closed-won and closed-lost deals before deployment. If your data cannot support a reliable model, we tell you during the audit.
Will our reps actually use this?
Number one concern we hear. We do not add dashboards reps have to check. Insights go into the tools they already use: lead scores inside CRM records, prioritised task lists, Slack notifications for high-intent leads. Less work for reps, not more. Adoption across our engagements exceeds 85% in the first month.
What if our CRM data is messy?
Most CRM data is messy. That is normal. Our models handle incomplete data. We figure out which fields are reliable, which are noise, and build scoring around what works. If your CRM is fundamentally broken (massive duplicates, no deal history, fields that mean different things to different reps) we flag that in the audit and recommend cleanup first.
How is this different from the lead scoring built into our CRM?
Native CRM scoring (HubSpot, Salesforce Einstein) uses basic rules or limited ML trained on generic datasets. Our models train on your data, your conversion patterns, your sales cycle. We also pull behavioural signals from outside the CRM (website activity, email engagement, product usage) that native scoring cannot access. Clients who switch typically see 30-40% better prediction accuracy.
What does it cost?
Standard engagement (lead scoring + pipeline forecasting + CRM automation) runs $6-$10K depending on complexity, integrations, and data volume. Conversation intelligence adds $3-$5K. We scope and price after the audit because the audit determines what is feasible. No recurring platform fees. You own everything we build.
Do we need to replace any of our current sales tools?
No. We build on top of your existing stack. The AI layer integrates with your CRM, email platform, calling tool, and website analytics. We do not sell software or require new platforms. If your current tools have API access, we can work with them.
