Industry: Email Marketing / Agency
Solution: Automated Lead Intelligence and Enrichment Pipeline
Stack: n8n + Supabase + CRM
AI Enablement Partner: Ovidius AI
The Problem: A Lead List That Was Too Big to Use
An email marketing agency came to us with a problem that sounds like a good problem on the surface.
They had a 300,000-domain lead list.
But in reality, it was not yet an asset. It was just raw data.
Their team of about 25 people and 120 clients relied on a lead generation process that ran once per year, completely manually.
That process included:
- Researching domains one by one
- Checking traffic and business viability
- Identifying e-commerce platforms
- Finding decision-makers and contacts
- Scoring leads for sales relevance
It worked until it did not.
By the time the team finished working through the list, the data was already stale.
Then the cycle started again.
They did not have a lead problem.
They had a pipeline problem.
The Real Constraint: Lead Qualification Was the Bottleneck
The issue was not finding leads. It was qualifying them.
Every domain required multiple layers of validation:
- Is this a real e-commerce business
- Does it have enough traffic to matter
- Is it in the right geography for the German market
- Who actually makes the buying decision
Doing this manually created two major inefficiencies.
1. High time cost
Hours were spent on research instead of sales.
2. High inconsistency
A yearly batch process created:
- spikes in workload
- stale data by the time it was used
- no continuous flow into CRM
What they needed was not a bigger list.
They needed a continuous qualification system.
The Goal: Turn Raw Domains Into Sales Ready Leads Automatically
We designed a system to:
- Ingest a raw 300,000-domain list
- Filter and validate in structured stages
- Enrich only high quality leads
- Score them against ICP criteria
- Deliver directly into CRM
The key requirement was simple.
Sales should never have to manually qualify a lead again.
The Solution: A Multi-Stage Automated Lead Intelligence Pipeline
We built a four-stage pipeline using n8n as the orchestration layer, with structured data storage in Supabase, and final delivery into Close CRM.
The core design principle was simple.
Do not enrich everything. Filter aggressively first.
This keeps cost low and precision high.
Stage 1: Domain Filtering
The pipeline starts by ingesting the full domain list.
Each domain is validated for:
- e-commerce platform presence such as Shopify, WooCommerce, or BigCommerce
- basic business legitimacy
Anything that does not qualify at the platform level is removed immediately.
This prevents wasted processing downstream.
Stage 2: Traffic and Market Viability
Next, remaining domains are analyzed for:
- Minimum traffic threshold of 10K+ monthly visits
- German market relevance
- Site maturity and activity level
Only domains that show real commercial viability move forward.
At this stage, the majority of the original 300,000 domains are already filtered out.
Stage 3: Contact Discovery and Enrichment
For qualified domains, the system identifies decision-makers such as:
- CEO
- CMO
- Head of E-commerce
Then it enriches each record with:
- validated email addresses
- LinkedIn profiles
- company metadata
This step is intentionally expensive, so it only runs on pre-qualified leads.
Stage 4: Lead Scoring and CRM Delivery
Each lead is scored against the client’s ideal customer profile.
Scoring factors include:
- company size
- traffic quality
- market fit
- e-commerce readiness
Finally, the lead is pushed into Close CRM with:
- priority score
- enrichment notes
- structured contact data
Sales receives ready-to-act leads, not raw data.
Expected Output
From the initial 300,000 domains:
- 15,000 to 25,000 qualified leads
- Processed at 5,000 to 10,000 domains per day
- Estimated 60 to 80 percent cost reduction versus manual workflow
Why This Architecture Works
The key design decision was not automation. It was stage-based filtering.
Instead of enriching everything upfront, the system:
- Filters cheap signals first
- Validates medium cost signals next
- Only then runs expensive enrichment
This ensures:
- enrichment costs stay low
- compute is only spent on high quality leads
- scalability is not linear with volume
Most of the dataset is eliminated before it becomes expensive.
The CRM Integration Advantage
One of the most important parts of the system is what does not change for the sales team.
Everything still flows into Close CRM.
That means:
- no new tools to learn
- no workflow disruption
- no behavioral change required
The only difference is that leads now arrive:
- pre-qualified
- pre-scored
- pre-enriched
Sales time shifts from research to conversations.
The Bigger Insight: Outbound Breaks at Scale Without Systems
Most outbound teams hit one of two problems:
- They under-invest in research, leading to low quality leads
- Or they over-invest manually, creating expensive slow pipelines
This system solves both by introducing structure.
Qualification becomes continuous, not batch-based.
That is the real shift.
Not more leads.
Not better lists.
But a pipeline that continuously qualifies data in the background.
Final Takeaway
The original problem was not a lack of leads.
It was that lead qualification did not scale.
By turning a static 300,000-domain list into a continuously processed system, we transformed outbound from a manual research task into an automated pipeline.
That meant:
- less manual research
- higher lead quality
- faster sales cycles
- and a continuously updated pipeline instead of annual batches
This is what outbound looks like when it is treated as a system, not a task.
About Ovidius AI
Ovidius AI builds AI driven systems that automate complex workflows across sales, marketing, recruiting, and content, helping teams scale output without scaling headcount.