An outbound engine that prospects while your team closes
Lead sourcing, enrichment, research-grade personalization and reply detection, running end to end.
Your custom strategy and systems, delivered in under a month. Your named people using them every week by day 30. Or we keep working for free until both are true.
Drawn from this system's own stages. A dashed ring marks a stage where a person acts.
01How it runs
Stage by stage.
Built to respect CASL: consent basis tracked per contact, sender identification on every message, a working unsubscribe, records kept.
- 01
Sourcing
Leads pulled continuously from multiple data sources matched to your ICP, instead of one-at-a-time manual research.
- 02
Enrichment
Contact and company data gathered, verified and merged into one clean record per lead. No more hand-scraping.
- 03
AI strategy
A custom analysis for every single lead: who they are, what they need, why you fit. This layer is what makes the next one work.
- 04
Personalized outreach
Messages built from the strategy layer, not templates with a first-name token. Sent on a deliverability-safe schedule.
- 05
Deck creation
Proposals assembled automatically when interest shows, personalized from the same research.
- 06
Reply detection
Responses caught the moment they land and routed to a human in Slack. Intent never sits unread.
A person acts here
- 07
Follow-up
Sequences that continue politely until there is an answer, and stop the moment there is.
02What it does
Four jobs it takes on.
Prospect sourcing
Leads pulled from live data sources matched to your ICP, without a person searching.
Per lead strategy
A custom analysis of who they are, what they need and why you fit, before any message sends.
Reply detection
Interest caught the moment it lands and routed to a human with full context.
Reply to deck
A personalized proposal assembled from the same research the moment a prospect shows interest.
What it replaces
Manual prospecting has a ceiling.
A person can research and write only so many good touches in a day. Your pipeline is capped by hours, not by market size.
Personalization and volume fight each other.
Templates scale but read like templates. Real research converts but takes an hour per lead. You should not have to pick.
Replies go cold in the inbox.
The lead answered. Nobody saw it for two days. That deal is gone and nobody can say why.
The agency retainer bought activity, not pipeline.
Reports full of sends and opens. Calendar still empty.
Your CRM is a graveyard.
Half the contacts are stale, the notes stop in March, and nobody trusts the data enough to act on it.
03Proof
Where it runs.
Breez
DeliveredBreez's lead engine found 2,488 prospects and scored every one against its ideal customer profile
Read the story2,488prospects found and scored for fit with the ideal customerSystem loggedby Advizr, 20 Aug 2026How this was measured
417 in the top tier and 484 at a fit of 70 or above, read from the engine's own database.
Merchant House Capital
DeliveredMerchant House Capital's pipeline researched 1,924 leads before a single message was written
Read the story1,924leads sourced and researched by the pipelineSystem loggedby Advizr, 20 Aug 2026
04What is included
In the build.
- The full pipeline, built on your data and run on your isolated infrastructure
- Sending infrastructure set up safely: warm-up, volume ramps, monitoring
- Your team trained to run and adjust it without us
- Monitoring, iteration and support after launch, inside the monthly partnership
05How it is built
Five layers under every system.
The same five layers sit under all 14 systems. Here is each one in this system's own terms.
Retrieval
Every lead is researched against its own public footprint and your own history with that account, so personalization cites a real fact rather than merging a template. Past conversations are indexed so nobody gets contacted twice with the same angle.
- pgvector
- Full-text BM25
- Account history index
Agents and orchestration
Sourcing, enrichment, drafting and reply detection run as separate agents on a schedule, each with a spend ceiling. Sending is rate-limited by domain reputation, not by how fast the pipeline can generate.
- agent-worker
- browser-sandbox
- Per-agent budgets
Evaluation
Drafts are graded against messages that actually got replies before volume goes up. Reply classification is measured against a labelled set, because a missed positive reply costs more than any send.
- Eval graders
- Reply classification accuracy
- quality-worker
Models
Frontier models for research and drafting. Ordinary classifiers for reply intent and routing, which are faster, cheaper and easier to audit on thousands of messages a week.
- Frontier models through one gateway
- Embedding models via the same gateway
- Intent classifiers
Data boundary
One lead store in your own database, one part of the isolated infrastructure each client gets: its own repository, database and deployment. The send step checks a suppression list first, so nobody who opted out or sits in a live deal is messaged.
- Supabase row-level security
- Suppression ledger
- Training off without your consent
Named tools in this buildApify, Instantly + HeyReach, Supabase, Slack
06How we build it
In this order.
- 01
Fix targeting before volume.
The first build narrows who gets contacted at all. Sending more of the wrong message faster is the most common way this fails, and it damages a domain you cannot easily replace.
- 02
Earn the volume with reply data.
Drafts are graded against messages that got real replies. Volume ramps only as the reply rate holds, with warm-up and domain reputation respected.
- 03
Close the loop into the calendar.
Reply detection routes to a person within minutes. The metric is booked calls, not sends, and the pipeline reports on that.
What we will not automate
Sending without a suppression check, and any claim in a message that is not traceable to something real about that account. We also will not buy or scrape data whose provenance we cannot explain to a client.
07Questions
Straight answers.
Is this legal under CASL?
The system is designed around CASL from day one: consent categories tracked per contact, sender identification on every message, a working unsubscribe, and records kept. You stay the sender of record, and we walk your team through the obligations before anything sends.
Will prospects know it's AI?
They will know the message understood them, which is the only thing that matters. Every message is built from research on that specific lead, and your team approves the voice before launch.
What about deliverability and domain reputation?
Separate sending domains, gradual warm-up, volume ramps and continuous monitoring are part of every build. Protecting your primary domain is a hard requirement, not an option.
Why not just buy Instantly ourselves?
You can, and you would own a sending tool. The engine is the part around it: sourcing, the per-lead strategy layer, reply routing, and a team trained to run it. Tools send. Systems book calls.
What happens when someone replies?
Reply detection catches it immediately and routes it to a human in Slack with full context. Automation stops the moment intent appears. People close deals. The system gets them the conversation.
How fast is it live?
The engine is delivered in under a month, and your named people use it every week by day 30, or we work for free until both are true. Volume then ramps on reply data, because warm-up and domain reputation set that pace, not us.
Built alongside it
Who to call next
The market ranked and refreshed while your team works the live deals.
Content engine
Your expertise becomes a weekly cadence your team only approves.
Run your whole company on AI.
Tell us how your business runs. If we're not the right fit, we'll say so on the call.
Your custom strategy and systems, delivered in under a month. Your named people using them every week by day 30. Or we keep working for free until both are true.
The AI operating layer for enterprises.