External signal detection and prioritization for Novarc Technologies
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.
Three weeks of stakeholder interviews, system analysis, and workflow documentation.
| Stakeholder | Role | Key Pain Point |
|---|---|---|
| Jordan Crick | Deal Management / RevOps | External signal detection gap; internal reporting covered |
| Jackie Nolan | Account Executive | Territory planning, regional project intelligence |
| Kyle Parker | BDR Team Lead | Data export leakage; Sales Navigator workflow gaps |
| Kabir | BDR — NovAI | Multi-tab workflow friction; manual prospecting |
| Peter | Digital Revenue Analyst | HubSpot-LinkedIn sync; manual report reconciliation |
| Melissa Bayanzadeh | VP Marketing | Attribution tracking across 12+ touchpoints |
Findings confirmed by multiple stakeholders carry higher confidence.
| Finding | Jordan | Jackie | Kyle | Kabir | Peter | Melissa |
|---|---|---|---|---|---|---|
| External signal detection is primary gap | ✓✓ | ✓ | ✓ | — | — | — |
| LinkedIn audience saturated | — | — | ✓ | — | ✓ | ✓ |
| HubSpot data quality issues (401 properties) | ✓ | — | — | — | ✓ | — |
| Gong underutilized / inconsistent usage | ✓ | — | — | ✓ | ✓ | — |
| People won't go to dashboards | ✓✓ | — | — | — | — | — |
| Regional intelligence valuable | — | ✓✓ | — | — | — | — |
Technical capability alone doesn't guarantee success. These factors shape implementation approach.
Same destination, different entry points based on priorities. All options lead to the full system.
Regardless of which option is selected, the first two weeks are dedicated to a HubSpot data quality audit. HubSpot has 401 properties, but discovery revealed only ~20 are reliably populated. Attribution models, signal scoring, and CRM automation all depend on clean, consistent inputs. We don't build on assumptions — we audit first, identify the reliable subset, normalize inconsistent formats, and establish the data foundation everything else depends on.
This includes property mapping with population rates, cross-system drift analysis (HubSpot vs. NetSuite vs. Gong), and a go/no-go checkpoint at Week 3 before committing to the full build. If data quality is worse than expected, we adjust scope before burning budget on features that won't work.
Monitor external signals, match against historical patterns, deliver prioritized leads to Slack.
The system generates actionable intelligence reports on-demand or scheduled. Here's what a regional market brief looks like:
| # | Account | Score | Signals | Key Intelligence |
|---|---|---|---|---|
| 1 | RK Industries | 46% | 5 | Indeed: 5 welders @ $90-109k. HubSpot: Proposal stage, $549k deal |
| 2 | DPR Construction | 44% | 4 | Confirmed $38M warehouse. Gong: "evaluating three vendors..." |
| 3 | Samsung Taylor | 38% | 2 | LinkedIn: 7 PM roles open. Gong: "leaning toward fastest start" |
| Signal Type | Source | Why It Matters |
|---|---|---|
| Hiring welders | Adzuna, LinkedIn Jobs | "They just won a project" — validated signal |
| Facility expansion | News, PR, Permits | Capital availability, capacity needs |
| Project awards | Gov contracts, industry news | Downstream demand (CHIPS Act, LNG) |
| Executive changes | LinkedIn, News | New decision-makers, budget cycles |
| Competitor mentions | News, Social | Displacement opportunities |
When Q3 underperforms Q2, Novarc currently has no diagnostic playbook. This option builds one.
Peter manually reconciles data across HubSpot, LinkedIn Ads, and Google Ads every month. Melissa tracks attribution across 12+ touchpoints with no unified view. Kyle's BDR team receives e-book MQLs but has no way to distinguish a genuine prospect from someone who just wanted a PDF. The result: marketing budget allocation is based on intuition, not attribution math.
Option B connects these systems into a single reporting layer with automated cost reconciliation, multi-touch attribution, and engagement-weighted MQL scoring. But first, we audit — because attribution built on inconsistent data tells you the wrong story.
What Peter would see every Monday morning, instead of spending hours building it manually:
| Phase | Duration | Investment |
|---|---|---|
| HubSpot data audit — property mapping, population rates, normalization, cross-system drift analysis | Weeks 1-2 | $7,000 |
| Data integration (HubSpot + LinkedIn Ads + Google Ads) — Go/no-go checkpoint at Week 3 | Weeks 3-4 | $8,000 |
| Reporting engine + multi-touch attribution model | Weeks 5-6 | $7,000 |
| MQL scoring + Slack delivery + onboarding + 30-day post-launch support | Weeks 7-8 | $6,000 |
$25,000–$31,000 · 8 weeks · Fixed price · Milestone billing
Leadership prioritizes easily measurable ROI (hours saved, cost clarity). Peter's reporting pain is more urgent than Jordan's signal gap. Marketing budget attribution is a near-term strategic priority. The organization needs to answer "what's working?" before investing in new signal sources.
The work is already happening. The output is leaking. 75% of prospects found in Sales Navigator never make it to HubSpot.
Kabir prospects across five tabs simultaneously: LinkedIn for context, Cognism for contact data, the company website for recent news, Gemini for email drafting, and HubSpot for CRM entry. Each prospect takes 10+ minutes of manual assembly. He's already built custom Gemini "Gems" with specific prompts to speed this up — the most advanced AI user on the team — but the workflow is still fundamentally manual.
Kyle's team faces a different version of the same problem. They find prospects in Sales Navigator, but the export-to-CRM process is so friction-heavy that 75% of contacts leak out of the pipeline before they ever reach HubSpot. That's not a lead generation problem. It's a plumbing problem.
But before we automate the push into HubSpot, we need to know what we're pushing into. With 401 properties and inconsistent usage, automating CRM entry without first auditing the data model means scaling bad data faster. The data audit ensures new contacts land in the right fields with the right formats from day one.
The tools are supposed to help, but they create their own friction. Cognism's direct dials are unreliable. Sales Navigator exports are lossy. HubSpot entry is manual. Option C doesn't add another tool to the stack — it wraps the existing tools into a single workflow that actually captures the output.
Kabir is the sole BDR for NovAI — an almost entirely outbound motion in a large TAM. His throughput is a direct bottleneck on NovAI's growth. If NovAI is a strategic bet, accelerating the one person responsible for its outbound pipeline has outsized impact. At 10+ minutes per prospect today, he contacts roughly 30-40 prospects per day. At under 2 minutes, that number triples.
What Kabir sees when he clicks on a LinkedIn profile:
| Phase | Duration | Investment |
|---|---|---|
| HubSpot data audit — property mapping, field normalization, duplicate identification, CRM data model cleanup | Weeks 1-2 | $7,000 |
| Browser agent core + LinkedIn integration — Go/no-go checkpoint at Week 3 | Weeks 3-4 | $8,000 |
| HubSpot + Cognism integration + duplicate resolution + AI email generation | Weeks 5-6 | $8,000 |
| Activity logging + onboarding + 30-day post-launch support | Weeks 7-8 | $6,000 |
$26,000–$32,000 · 8 weeks · Fixed price · Milestone billing
Kabir has already built his own Gemini automations. He's demonstrated he'll adopt tooling that works. This is the lowest adoption-risk option — you're building for a user who's already proved the behavior. Kyle's team has the same need at scale. The risk isn't "will they use it?" — it's "how fast can we ship it?"
NovAI outbound is the immediate growth priority. BDR team throughput is the binding constraint on pipeline growth. Leadership wants the most tangible daily-use tool with highest adoption certainty. The organization values operational efficiency gains that compound across the entire BDR team.
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.
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:
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.
| Component | Technology | Purpose |
|---|---|---|
| Signal Scanner | Serper.dev + NewsAPI + Adzuna | Daily monitoring of target accounts |
| Pattern Engine | Gemini 1.5 Pro / Claude API | Learn from historical wins, score signals |
| Data Store | PostgreSQL → AWS Data Lake (Phase 2) | Signal history and learned patterns |
| CRM Integration | HubSpot MCP Server | Read context, write enrichment back |
| Delivery Layer | Slack Webhooks | Push insights where teams work |
| Orchestration | AWS Lambda | Scheduling and coordination |
| Service | Endpoint | Rate Limit | Est. Cost |
|---|---|---|---|
| Serper.dev | POST /search | 2,500/month (paid) | $50/month |
| NewsAPI | GET /everything | 1,000/day (paid) | $449/month |
| Adzuna | GET /jobs | 250/day (free) | Free |
| Gemini 1.5 Pro | generateContent | 60 RPM | ~$100/month |
| HubSpot | MCP Server (read/write) | 100/10sec | Existing license |
| Slack | Incoming Webhooks | 1/sec | Existing license |
Read access: contacts, companies, deals, engagements, properties. Write access: contact/company properties, notes, tasks. No delete permissions. API key stored in AWS Secrets Manager.
| Failure Mode | Response |
|---|---|
| Serper.dev returns no results | Log, skip company for this cycle, retry next day |
| HubSpot rate limit hit | Exponential backoff, queue remaining, alert if persistent |
| Slack webhook fails | Retry 3x with backoff, fallback to email digest |
| LLM returns malformed response | Retry with simplified prompt, log for review |
| Duplicate signal detected | Merge with existing, update score, don't re-notify |
Phase 1: ~$300–$600/month (APIs + hosting). Phase 2 adds Data Lake at $300–$500/month. All other components use existing Novarc licenses.
Phase 1 delivers a complete, working system. Future phases expand capability based on demonstrated value.
| Risk | Mitigation |
|---|---|
| HubSpot API access delays | Early API validation in Week 1; parallel development tracks if blocked |
| Signal quality (false positives) | Two-week pilot with feedback loop before scaling; human review threshold |
| Adoption stalls | Push to Slack (not new dashboard); structured onboarding |
| Data quality constraints | Build on validated ~20 HubSpot properties; don't depend on full 401 |
| Gong partial data | Use activity counts (reliable) before transcript analysis (partial) |
| Stakeholder availability | Weekly 30-min feedback slots; async Slack channel |
$28,000–$34,000 · 8 weeks · Fixed price · Milestone billing
50% at project kickoff, 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.