How Deligatr decides who to contact, before a message ever sends.
Every outbound message starts with a decision: is this company, at this moment, a good use of a prospect's attention and our client's time? Here is exactly how that decision gets made.
Quick answer
Deligatr identifies who to contact through a four-stage pipeline: monitoring relevant real-world signals for your industry, enriching flagged companies with verified contact and firmographic data, scoring each one against your ICP, and handing the result to an AI-personalisation layer that writes messages referencing the actual reason that company was contacted. This is not a static purchased list, it runs continuously.
The pipeline
Four stages, one continuous process
Signal Monitoring
We define, per client, which real-world events indicate a company is worth reaching now. Depending on your ICP, this includes funding events, leadership changes, hiring surges, technology adoption, permits and filings, or expansion announcements. These are monitored on an ongoing basis rather than checked once.
Data Enrichment
Flagged companies are enriched with verified firmographic detail and decision-maker contact information. Enrichment draws on multiple sources rather than one static list, since any single data source is incomplete on its own, and every contact is verified before use.
AI Scoring & Prioritisation
Each enriched company is scored against your specific ICP criteria, industry fit, company size, signal strength, and likely budget, so outreach volume concentrates on the contacts most likely to convert rather than being spread evenly across a broad list.
AI-Personalised Handoff
The signal and enrichment data feed directly into message generation, so the resulting outreach references the actual reason that company was contacted, not a generic mail-merge. This is the link between research and the message a prospect actually reads.
A real example
Finding the highest-intent targets for a management consultant
For a management consulting client, the ICP workshop identified that companies which had recently closed a private equity funding round were the highest-intent targets, since post-acquisition operational benchmarking was the client's strongest service line.
Signal monitoring tracked recent funding activity. Enrichment layered in technology and organisational signals to confirm company profile fit. Each match was scored, and the highest-scoring companies were prioritised for the first wave of outreach.
The resulting campaign reached a 7.8% reply rate on cold outreach to this specific segment, well above generic, unsegmented outbound.
Read the full case studyBeing direct about limits
What this is not
Not a static purchased list
Signals are monitored continuously. Data is refreshed and verified, not scraped once and reused for months.
Not a single unified AI product
This is a pipeline of coordinated stages, not one proprietary black box. We would rather explain it accurately than oversell it.
Not fully autonomous
A person reviews messaging direction and campaign strategy. The AI accelerates research and personalisation, it does not remove human oversight.
Not identical for every client
Which signals matter is defined per client during the ICP workshop, not applied as one generic formula across every industry.
Questions about the research process
Is this the same process for every client?
Do you use third-party data providers?
How do you avoid contacting bad or outdated data?
What is a "signal" in practice?
Does the AI scoring replace human judgement?
Can I see the data behind a specific meeting that gets booked?
See this pipeline built for your ICP
Book a free strategy call and we will walk through exactly which signals matter for your industry.