Sales Intelligence
Fabric

Which fifty contacts
do you call?

Novarc has 30,000+ contacts in HubSpot. The BDR team can make roughly 50 meaningful calls per week. Current hit rate hovers around 10%.

The question isn't whether you have enough leads. It's whether you're calling the right ones at the right time. Jordan's internal reporting infrastructure is strong—PowerBI dashboards, custom deal scoring, weekly pipeline snapshots. What's missing is visibility into external signals that indicate buying intent.

What we learned

Three weeks of stakeholder interviews, system analysis, and workflow documentation.

Cross-interview validation

Findings confirmed by multiple stakeholders carry higher confidence.

Six critical insights

SWR vs NovAI comparison

Beyond the technology

Technical capability alone doesn't guarantee success. These factors shape implementation approach.

Sales Signal Engine

Monitor external signals, match against historical patterns, deliver prioritized leads to Slack.

Sample output: Market Intelligence Report

The system generates actionable intelligence reports on-demand or scheduled. Here's what a regional market brief looks like:

Signal types monitored

Example output

Three starting points

Same destination, different entry points based on priorities. All options lead to the full system.

Before we build, we audit

Signal detection is only as good as the underlying data. HubSpot has 401 properties, but discovery revealed only ~20 are reliably populated. Phase 1 starts with analysis, not assumptions.

Why data quality matters

Pattern learning requires consistent, reliable inputs. If we train on fields that are only 30% populated, the model learns noise instead of signal. The first two weeks focus on identifying which data we can trust:

• Property audit: Map all 401 HubSpot properties—population rates, data types, last-updated timestamps
• Reliable subset: Identify 15-30 properties with >80% population that pattern learning can depend on
• Normalization gaps: Document inconsistent formats (dates, phone numbers, company names) requiring parsing
• Cross-system drift: Compare HubSpot ↔ NetSuite ↔ Gong records for the same accounts and quantify discrepancies

This audit becomes the foundation for all pattern learning. We don't train on garbage. If data quality is worse than expected, we adjust scope before burning budget on signal detection that won't work.

System components

API specifications

HubSpot MCP server scope

Read access: contacts, companies, deals, engagements, properties. Write access: contact/company properties, notes, tasks. No delete permissions. API key stored in AWS Secrets Manager.

Error handling

Monthly operating costs

Phase 1: ~$300–$600/month (APIs + hosting). Phase 2 adds Data Lake at $300–$500/month. All other components use existing Novarc licenses.

Phased delivery

Phase 1 delivers a complete, working system. Future phases expand capability based on demonstrated value.

How we measure

Phase 1 complete (Week 8)

• 50+ accounts under daily monitoring
• Pattern library from 15-20 closed-won deals
• 10+ actionable signals per week delivered to Slack
• 70%+ signals rated "useful" by pilot team
• 3+ deals influenced in first 8 weeks

90-day post-launch targets

• 60%+ prediction accuracy (signal → opportunity)
• 10%+ win rate improvement on monitored accounts
• Account prep time: 45 min → 15 min (further reduction in Phase 2)
• Regional market briefs generated on-demand for Jackie's territory

What could slow us down

Critical dependencies

• HubSpot admin access for MCP server setup
• Slack workspace permissions for webhook delivery
• Jordan's time for weekly feedback (30 min/week)
• Export of 15-20 closed-won deal records for pattern learning

What's included

• Complete Phase 1 build: Core Signal Engine (8 weeks)
• Data quality audit and reliable property identification
• HubSpot MCP integration (read/write)
• Signal APIs: job boards, news monitoring, web search
• Pattern learning from 15-20 closed-won deals
• Slack delivery system (#sales-signals channel)
• 50 accounts under daily monitoring
• Pilot onboarding: Jordan + 2 AEs + Kyle
• 30-day post-launch support

Payment terms

50% at project kickoff ($14,000–$16,000), 50% at successful Phase 1 deployment. Go/no-go checkpoint at Week 3 with first signals reviewed. Phases 2–3 scoped and quoted separately upon Phase 1 completion.