The Problem
The client came to us with a frustrating reality: their sales team was spending more time managing their CRM than actually selling. Lead data was fragmented across 4 tools, qualification was manual and inconsistent, and there was no intelligence layer to tell reps which leads to prioritise.
The outcome was predictable. High-quality leads went cold because nobody got to them fast enough, reps chased dead ends based on gut feel, and the sales cycle stretched to 18+ days.
“We knew AI could help. We just didn’t know how to build it into something we’d actually use every day.”
Our Approach
We started with a 3-day discovery sprint to map the existing sales workflow and identify the highest-leverage integration points for AI.
Discovery findings:
- 40% of rep time went to manual data entry and CRM updates
- Lead scoring was entirely manual, based on rep intuition
- No automated follow-up triggered by lead behaviour
- Zero visibility into which communication patterns correlated with closed deals
From these findings, we designed NeuroCRM around three AI-powered capabilities:
1. Intelligent Lead Scoring
We built a scoring model that analyses:
- Company size, industry, and tech stack signals (enriched via Clearbit API)
- Engagement behaviour (email opens, link clicks, page visits)
- ICP match score against historical closed-won data
- Recency and frequency of interactions
Leads are automatically ranked 1 to 10. Reps open their dashboard every morning and see exactly who to call first.
2. AI Communication Intelligence
We fine-tuned a model on the client’s historical email data (anonymised) to identify:
- Which subject lines generated the highest response rates by industry
- Which email lengths and structures correlated with booked calls
- The optimal follow-up cadence by lead type
The CRM now suggests the next best action for every lead, with a one-click draft.
3. Automated Workflow Triggers
We replaced the manual “remember to follow up” system with intelligent triggers:
- Lead visits pricing page → immediate high-priority alert to assigned rep
- No response after 72 hours → automated soft-touch email sent
- Demo booked → prep brief auto-generated from CRM data
- Deal stuck in stage for 5+ days → manager alert with AI-suggested unblocking actions
The Stack
- Frontend: Next.js + Tailwind CSS
- Backend: Node.js + PostgreSQL
- AI layer: OpenAI GPT-4o for drafts + fine-tuned scoring model
- Data enrichment: Clearbit API
- Email integration: Gmail + Outlook via OAuth
- Hosting: Vercel (frontend) + Railway (API)
Results
| Metric | Before | After |
|---|---|---|
| Lead qualification time | 45 min/lead | 8 min/lead |
| Sales cycle length | 18 days | 6 days |
| Follow-up consistency | ~60% | 100% (automated) |
| Rep data entry time | 2 hrs/day | 20 min/day |
The pilot teams reported 3× faster time from first contact to qualified opportunity. The AI scoring model’s top-10% leads converted at 4× the rate of the manual process.
What We Learned
The most important lesson: AI in B2B tools works best when it removes friction from existing workflows rather than introducing new ones. We deliberately made every AI suggestion one-click-to-act. Reps don’t have to think about the AI. They just see better information and make faster decisions.
Build time: 5 weeks from kickoff to live pilot.