Every tenant in PQ Intel defines their own Ideal Customer Profile — industry, company size, keywords, tech stack, geography. Once configured, every discovered company and discussion is scored automatically against that profile.
81|Target by industry vertical (SaaS, fintech, healthcare, etc.) and company size band (1-10, 11-50, 51-200, 201-1000, 1000+). Filter out entire segments in one click.
89|Define trigger keywords your prospects use and the job titles you want to reach. The scoring engine weights signals higher when they hit your keywords and mention target roles.
94|Target companies using specific tools (Salesforce, HubSpot, Stripe, etc.) and limit by country, region, or city. Geography applies to both company HQ and signal origin.
99|ICP settings are stored per tenant in the tenants table. The scoring engine reads your ICP config and applies it to every signal that comes through the platform. Different teams within the same account can target different profiles — each sees only their own scored feed.
Configure ICP during onboarding (Step 2-4 of the 5-step wizard) or update it anytime from settings. Changes take effect on the next nightly scoring run. The configuration covers five core dimensions:
Industry & Company Size: Select from a curated list of verticals (SaaS, fintech, healthcare, e-commerce, manufacturing, etc.) and size bands (1-10, 11-50, 51-200, 201-1000, 1000+). These act as first-pass filters — companies outside your chosen industries are excluded from scoring entirely.
Keywords & Roles: Add trigger keywords that your prospects use and the job titles they hold. The scoring engine maintains a weighted keyword dictionary: industry terms score higher than generic keywords, and role mentions amplify the score further when they match your target titles.
Tech Stack: Target companies using specific tools and platforms — CRM systems (Salesforce, HubSpot), payment processors (Stripe, Braintree), analytics tools (Mixpanel, Amplitude), and more. Tech stack data comes from enrichment lookups and signal content analysis.
Geography: Limit by country, region, or city. Geography applies to both company HQ location and signal origin. A team targeting only UK-based companies sees signals from London but filters out identical posts from New York.
The composite_scorer.py algorithm runs against your ICP config every night via recalc_scores.py. Every company in your pipeline gets re-scored against the latest settings, so scoring stays aligned with your strategy. The recalculation processes all tenants in parallel — an account with 50 tenants completes the full pass in under two minutes.
Update your ICP config during the day, and the next morning your feed reflects the new criteria. No manual re-scoring needed. The system logs every config change so you can audit how your ICP has evolved over time.
New tenants complete a guided 5-step onboarding wizard that walks through each ICP dimension. Step 1 selects the target industry and company size range. Step 2 adds trigger keywords and job roles. Step 3 configures tech stack filters. Step 4 sets geography boundaries. Step 5 reviews the summary and launches monitoring.
The wizard includes smart defaults based on your industry selection, so most teams can complete setup in under 10 minutes. After onboarding, ICP settings are always accessible from the tenant configuration panel for refinement.
Your agency lands a new client in the enterprise healthcare space. You create a new tenant in PQ Intel, run through the 5-step onboarding wizard, and configure ICP for US-based hospitals with 500+ employees using Epic or Cerner EHR systems. Within 24 hours, the signal feed is populated with matched discussions from LinkedIn, Reddit, and HN — all scored against the client's exact ICP. No custom development, no manual list-building, no waiting.
Your product team decides to expand from serving mid-market SaaS to targeting enterprise fintech. You update the tenant's ICP: change industry from SaaS to fintech, bump company size from 51-200 to 201-1000, and add new keywords (compliance, reconciliation, payment orchestration). That night, recalc_scores.py re-evaluates every existing signal against the new config. Your feed transforms overnight — companies that were Cold under the old criteria are now scoring Hot, and you start outreach the next morning.
Your outbound team covers three verticals: healthcare, logistics, and proptech. Instead of building a single broad ICP that catches everything, you create three tenants — one per vertical — each with its own distinct industry, keywords, and geography settings. Each SDR team sees only their vertical's feed, scored specifically for their targets. No cross-contamination, no irrelevant signals, no time wasted filtering out other verticals.
Set up distinct ICP profiles for each outbound team — different industries, different geo focuses, different trigger keywords. Each team sees a feed tailored to their pipeline.
137|Run ICP configs for multiple clients under a single account. Each client gets their own scoring model, their own feed, and their own pipeline — isolated and independent.
142|Match ICP parameters to your team's territory splits. Scoring respects geography, so SDRs see only the prospects they can actually reach.
Use PQ Intel's ICP configuration to test whether your assumed ideal customer profile actually generates signal volume. If a vertical you thought was core produces few matched signals, the data tells you to refine your positioning or ICP filters before investing more marketing budget.
Define a master ICP template and apply it to all teams with minor modifications per territory or segment. Ensure every SDR works from the same targeting criteria, eliminating inconsistency in lead qualification across the sales org.
Many platforms offer lead scoring but tie it to a single company-wide model. PQ Intel gives you per-tenant, multi-ICP configuration — each team or client gets their own independent scoring criteria.
158|| Capability | PQ Intel | Apollo |
|---|---|---|
| Per-tenant ICP configuration | Yes | No |
| Multi-ICP per account | Yes | Single model |
| Industry + size + keywords + tech + geo | All five | Partial |
| Nightly auto-scoring against ICP | Yes | No |
| ICP drives signal monitoring | Yes | Enrichment-first |
Your ICP configuration is the root setting that feeds signal monitoring, scoring, enrichment, and outreach. A change in ICP ripples through every feature automatically.
The platform_signal_bridge.py scanners query against your ICP config to determine which posts to fetch. Update your ICP, and the next day's scan applies the new filters — no configuration changes needed in the scanner.
The composite scoring engine reads ICP config as its primary input. Industry weights, keyword dictionaries, and role multipliers are all derived from your tenant's ICP settings. Scoring is only as good as the ICP it runs against.
When signals are matched, the enrichment pipeline uses the company context from your ICP to prioritise which contacts to discover. A company matching your industry and geo filters gets full waterfall enrichment; a borderline company gets limited enrichment.
Campaign audiences can be built directly from ICP dimensions. Target only leads in specific industries or geo regions defined in your ICP. As ICP evolves, campaign audience composition updates automatically on the next nightly sync.
See all integrations on the integrations page.
ICP configuration powers every scoring and monitoring function. See what it connects to:
Scoring runs against your ICP config — update one, the other follows.
Platform scanners query against your ICP to find relevant signals.
Enrichment prioritisation is driven by ICP fit — high-fit accounts get full waterfall treatment.
Build campaign audiences using ICP dimensions as segmentation criteria.
Leads flow into the pipeline scored by ICP config — no manual qualification needed.
Find decision-makers at companies matching your ICP criteria from LinkedIn.
Industry verticals, company size ranges, job roles, keywords, tech stack filters, geography, and custom signal weights. All per-tenant.
Yes. PQ Intel is multi-tenant. Each team or client gets its own ICP profile feeding a separate signal feed.
Yes. The onboarding wizard includes a 5-step setup, and ICP configuration can be imported as structured lists.
Update at any time. The next nightly run re-evaluates all signals against the new criteria.
Yes. Each tenant has its own ICP definition, signal feed, and scoring weights.
Most teams complete the 5-step onboarding wizard in under 10 minutes. If you already have a clear ICP defined, you can import it as a structured list and be operational in minutes. The wizard includes smart defaults — selecting an industry auto-populates common keywords and roles for that vertical.
Yes. If you have a master ICP template that works across multiple teams or clients, you can duplicate an existing tenant's ICP configuration and modify it for the new tenant. This is especially useful for agencies managing multiple clients with similar profiles.
A broad ICP results in many Cold-scored signals and a noisy feed. PQ Intel's scoring engine helps here: even with a broad ICP, the Hot/Warm/Cold tiers highlight which signals are strongest. If you see too many Cold signals, tighten your industry selections, add more specific keywords, or narrow geography to improve signal quality.
Absolutely. Define a narrow ICP with specific company names, industries, and keywords to create a focused ABM feed. PQ Intel will monitor 13+ platforms for signals from your target account list, score them against your ABM criteria, and surface every mention — from LinkedIn posts to Reddit threads — in a single feed.