Turn large prospect lists into prioritised sales opportunities with AI-powered ICP scoring, lead research, and personalised outreach, so your team knows exactly who to contact and why.
No unauthorised scraping · Human-in-the-loop outreach · Every action audit-logged
Illustrative figures based on the 1,500-lead example batch in the Dizenly product plan.
Lead Generation Dashboard
1,500 leads in database · last import 2 hours ago
Total leads
1,500
+500 this week
Qualified (≥70%)
195
13% of database
In outreach today
25
daily limit reached
Positive replies
18
4.2% reply rate
AI lead match score distribution
1,500 leads
◆ Only 195 of 1,500 leads clear the 70% qualification threshold. Nobody on the team reads the other 1,305.
Today's action queue
25 / 25 limitEvery prospect list arrives as a spreadsheet. Somebody has to read it, judge it, split it, chase it and remember it. That work is invisible, unmeasured - and it does not scale.
A 1,500-row export arrives in random order. Reps skim the first hundred names, and the best-fit prospect sitting at row 940 is never seen at all.
≈ 6 hrs a week spent skimming
Whether a lead is “good” depends on who is looking at it. No two reps qualify the same way, and none of the reasoning is written down.
No repeatable definition of a good lead
Personalising a message means opening five tabs and reading a profile. So most messages never get personalised - and the reply rate shows it.
≈ 8 minutes per message, per prospect
Replies sit in an inbox, tasks sit in a notebook, status sits in memory. Interested prospects go quiet because nobody picked them up in time.
Warm leads lost to admin gaps
The net effect: your team spends most of its week deciding who to contact, and only a sliver of it actually contacting them.
The pipeline is linear and predictable. Seven of the nine steps run without anyone touching them - a human only steps in where judgement or a real relationship matters.
Upload an Excel or CSV export. Dizenly validates the columns, normalises LinkedIn URLs and email addresses, detects duplicates against every previous import, and keeps the full import history.
AutomaticEach row becomes a normalised lead intelligence profile - role, seniority, company, industry, size, location, background, relevance and contact completeness.
AutomaticThe engine compares each lead to your company profile and Ideal Customer Profile across nine weighted factors, then returns a 0–100% match score with a written explanation.
AutomaticLeads at or above your threshold - 70% by default - enter the priority queue sorted highest first. Ties break by import date, so the ordering is always deterministic.
AutomaticRound-robin, capacity-based or criteria-based allocation. The owner is attached to the lead for its entire lifecycle, so nothing becomes nobody’s job.
AutomaticYour daily limit decides the volume - say 25 leads a day, globally or per owner. Anything that doesn’t fit rolls into tomorrow keeping its rank, never the back of the line.
AutomaticDay 1 review the profile, Day 2 engage with a post, Day 3 send the connection request. Approved integrations execute it; otherwise it appears as a one-click action in the owner’s daily list.
You actOnce the prospect connects, AI drafts a message from your services, relevant past projects and the prospect’s own role and company. Review, regenerate, edit, approve - then send.
You approveEvery reply is tagged Positive, Neutral or Negative. Positive instantly becomes a sales task for the owner; Negative closes the lead; Neutral returns to the pool for a later cycle.
AutomaticEverywhere else, the system either advances the lead itself or queues a single action for its owner.
After scoring
Does this lead clear the qualification threshold?
After ranking
Whose queue does it belong in, and on which day?
After a reply
Does this become a sales task, a follow-up, or a close?
Every stage below runs on the same record. Nothing is re-entered, re-sorted or re-explained by hand between one step and the next.
The score is not a mood reading from a language model. Nine weighted factors are evaluated with deterministic rules plus AI analysis, and the full breakdown is stored with the lead - so anyone on the team can see exactly why a prospect ranked where it did.
How the number is built
factor_contribution = factor_weight × (factor_fit% ÷ 100)
lead_match_score = Σ all factor contributions
Weights total 100 and are editable in Settings - rebalance your targeting without a code change.
Score breakdown
Stored with the leadQueued for Marta on the next outreach cycle, ahead of every lead scoring below 89.
Recomputed automatically whenever the ICP, the weights or the lead's own data change.
Contribution by factor
Why this lead scored 89%
A senior technology decision-maker at a mid-size retail SaaS business in a target country, with a product-modernisation need that maps to work you have delivered before. Only the incomplete contact record and a partial technology signal held the score below 90.
Dizenly does not add another tool to check. It removes the eight recurring chores that sit between a prospect list and a real conversation.
Deciding who to contact first
Today, by hand: Skim a 1,500-row export and guess from job titles
With Dizenly: Ranked 0–100% against your ICP, with the reasoning attached
Splitting leads across the team
Today, by hand: A manager slices the spreadsheet by hand every week
With Dizenly: Round-robin, capacity or criteria-based allocation, instantly
Deciding today's volume
Today, by hand: Whatever each person gets around to
With Dizenly: A configurable daily limit, global and per owner, enforced
Researching each prospect
Today, by hand: Five browser tabs and a scratch note per person
With Dizenly: One Lead 360° view: profile, score, history, next action
Writing the opening message
Today, by hand: Roughly eight minutes of copy-paste per prospect
With Dizenly: AI drafts it from your services and their profile - you approve
Reading and triaging replies
Today, by hand: Manual inbox triage, inconsistent judgement
With Dizenly: Auto-classified Positive, Neutral or Negative
Remembering to follow up
Today, by hand: Notebooks, reminders and memory
With Dizenly: A sales task created the moment a reply turns positive
Knowing what actually happened
Today, by hand: Nobody is quite sure
With Dizenly: Live dashboard plus a full audit trail on every lead
7 of 9
steps run with no human involved
Import, enrichment, scoring, ranking, assignment, queueing and reply classification all happen on their own.
3
human touches left in the loop
Engage with the prospect, approve the AI message, and have the actual conversation. That is the job you hired them for.
100%
of decisions are explainable
Every score, owner assignment, message and classification is written to the activity log and can be reconstructed later.
Everything below ships in the first release. Each module is independent, so the platform can be extended - new data sources, new channels, new CRMs - without rebuilding the core.
Teach the platform who you are and who you want, once.
Get prospects in from whatever source you already use.
Turn a pile of records into a ranked, explained shortlist.
Decide who does what, at what pace, in what order.
Say the right thing, then react to the answer correctly.
See what happened, and be able to prove it.
LinkedIn does not permit unauthorised scraping, third-party bots or automated connection requests, and Sales Navigator API access is not currently open to new partners. Dizenly is architected for that reality - and ready for official access the day it becomes available.
One switch, two behaviours - the rest of the platform never changes.
Approved API available
The connector executes the action automatically - no human step, no queue entry.
No approved API
The identical action appears in the owner's daily list and is completed by a person in one click.
Connection requests, engagement and message sending are performed by your own team from their own accounts. No bots, no browser extensions, no shared credentials.
Score computed, owner assigned, message generated, reply classified, task created - every state change is written to the activity log with a timestamp and an actor.
The overall score, the factor breakdown, the AI reasoning and the exact input data are all stored. AI output is monitored and reviewable, never treated as settled fact.
Enrichment providers, lead sources and outreach channels sit behind interfaces. Swap a provider, add email or WhatsApp, plug in a CRM - the core is untouched.
The first release is a complete loop - list in, conversation out. Everything on the right extends that loop rather than replacing it.
Everything needed to run the full loop end to end.
Extensions that reuse the same core, once it is proven in daily use.
Every plan includes the full pipeline - import, enrichment, AI scoring, ranking, sequences, AI messaging, reply classification and tasks. Plans differ in scale and control.
For a small sales team running one motion.
$299/ month
Book a demoFor teams running several segments at once.
$699/ month
Book a demoFor organisations with their own rules.
Custompricing
Talk to usPackaging shown is illustrative - final plans and pricing are confirmed with the Dizenly team.
No. The platform ships with a human-assisted action queue: Dizenly decides who to contact and drafts what to say, and your team performs the LinkedIn action from their own account. If official API access is granted later, the connector replaces those manual steps and nothing else in the platform changes.
Any Excel or CSV export you already have - Sales Navigator lists, event attendees, conference lists, your own research. Uploads are cumulative: a second upload of 500 records adds to the existing 1,000 rather than replacing them.
They are detected on LinkedIn URL, email address, or name plus company, then merged or updated instead of duplicated. Every import keeps its own history so you can always see where a record came from.
Yes. All nine factor weights and the qualification threshold are editable in Settings, and leads can be rescored against the new model - no development work involved.
No. Deterministic rules handle the structural matching - role, geography, company size - and AI handles the semantic judgement. Both feed one weighted model, and the breakdown plus the exact input data are stored with every lead.
It is tagged Neutral, which pauses the sequence and returns the lead to the pool for a later follow-up cycle. The system never sends the next message before a reply has been classified.
Exactly the number you set. The daily limit is configurable globally and per owner. Unused capacity rolls into the next cycle and leads keep their rank rather than moving to the back of the queue.
The platform itself is built. Setup is your company profile, your Ideal Customer Profile and your outreach owners - a short onboarding, then your first import and first scored queue.
Still have questions about how this would fit your sales motion?
Talk to the teamIn a 20-minute session we load a sample of your own list, configure your company profile and ICP, and show you the scored, ranked, owner-assigned queue that comes out the other side.
No credit card · No LinkedIn credentials required · Your data stays in your environment