Built for agencies and SMEs alike. Twelve product surfaces, no add-ons — from per-product fit scoring to a rapport-building agent.
As you read each capability, here’s the lens to keep in mind.
You probably need one or two of these. They mostly run themselves — the AI handles the rest. Read for outcomes (“does it book meetings?”), skip the multi-account bits.
You’ll use almost all of these. Read carefully for the multi-sender, pod-pacing, and per-product mechanics — that’s where the engineering depth lives.
Most outreach tools treat each LinkedIn account as a separate workspace. LinkedReach treats them as a pool. Push a sequence across all of them, balance load, and watch sender health from a single sender strip at the top of the dashboard.
Build a sequence as a chain of steps — view profile, connect, message 1, wait 3 days, message 2, escalate to InMail. Branch on accepted, no-reply, or replied. Conditional waits respect your campaign's send window down to the timezone.
A LinkedIn account gets 24–30 connection requests a day, and no more than 120–200 in a week. That weekly wall is the most expensive resource in the system — and it’s scarce. Most tools work the queue first-in-first-out. LinkedReach sorts by fit score, so the highest-potential leads enter the funnel first and the long tail waits.
Pre-flight a minimum-fit threshold and only your best-scoring prospects ever get queued. Faster results, fewer wasted slots, and a campaign that compounds because the strongest signals come back first — sharpening the model for everything downstream.
A workspace holds your senders, campaigns, leads and inbox. You invite the people who need to work in it — the principal who monitors, the account manager who QAs the copy, the closer who handles replies — and give each of them exactly the permissions their job needs. Those seats are free. You pay for connected LinkedIn senders and nothing else.
Permissions are deny-by-default: a role is granted capabilities explicitly rather than inheriting everything the role above it can do. Isolation runs at the database, not in the UI, so a member of one workspace cannot read another’s leads even by guessing an id.
A cold list is not the only place a lead comes from. An engagement campaign watches the posts your buyers are talking under — yours, a competitor’s, an industry voice’s — and captures the people who reacted or commented. Each one is enriched and scored against your products before it ever reaches a queue, exactly like an imported lead.
The sender participates rather than lurks. It reads posts, reacts, and comments in your configured voice — and when someone replies to one of those comments, or messages the sender off the back of one, that conversation lands in your inbox with the context of how you met attached.
Most teams carry an InMail balance they never work through, because the credits sit in a seat nobody opens. LinkedReach reads the real balance on each sender and treats it as a sending surface: free open-profile InMails first, then the classic wallet, then Sales Navigator credits — each spent from the wallet that actually holds them.
A paid credit is never spent on a guess. The engine checks that a recipient can genuinely receive an InMail on that surface before it composes anything, and a send that never reached LinkedIn is not counted against your allowance.
Personalisation is the part of AI outreach buyers see first. It's a small share of what the system actually does. Every box below is a workflow shipping in production today — running on the same agent that learns from every reply your pod sends.
Every lead is scored against your ICP before LinkedReach burns a daily-cap action on them. Returns a fit score, the reasoning, and a recommended action — bad-fit prospects never reach the queue.
A 1–2 line opener referencing the lead's profile, role, and recent activity — per lead, dropped into the message via tokens. Hand-personalising 30+ leads a day per sender doesn't scale; this does.
Hand the AI your offer, ICP, and pod voice. It drafts the full 5-step sequence — with tighter variance on high-stakes steps (the InMail, the first connect note) and looser variance on low-stakes follow-ups.
Every generated message is checked against prior sends from the pod. If phrasing overlaps too much, it's regenerated once with explicit "do not reuse" instructions. Keeps the pod sounding human as it scales.
Every inbound is tagged interested / objection / not now / wrong person / negative / out-of-office — routed to the right pod member's queue, with high-intent ones surfaced first. Closers stop reading "thanks but no thanks" all day.
Drafts the response, asks qualifying questions, and proposes calendar slots. Operators choose whether replies send automatically or surface for one-click approval — new campaigns ship with approval-by-default.
Every inbound is keyword-scanned for legal, GDPR, contract, pricing, refund, "remove me" — the kind of phrases where an auto-reply would be a disaster. On a match, auto-send is disabled for the thread, an alert is filed, and a human picks it up.
When a conversation runs 10+ messages, the agent keeps the topic-setting first message plus the recent nine and elides the middle. The AI stays on-topic in 30+ message threads instead of losing the plot at message twelve.
We won't tell you which AI vendor is behind it — that detail will change a dozen times in the next two years and we'd rather you bet on the workflow than the model. The agent is a frontier LLM, swappable behind the scenes, and it gets sharper as your pod sends more replies.
Personalisation isn't first-name token replacement. Each opener is generated against the lead's profile, recent posts, and your ICP brief — producing a sentence that reads like it took you ten minutes to write. Then a phrase-freshness check compares it against everything the pod has already sent, and regenerates if it sounds templated.
Most tools stop at the opener. Agent Mode keeps going. When a lead replies, the agent reads the message, scores warmth, drafts a contextual response, and offers real calendar slots once warmth crosses the threshold you set. Operators choose whether replies are sent automatically or surfaced for one-click approval. The reply-preview / approval flow is on by default for new campaigns — flip it off once you trust it.
Most outreach platforms assume you already know what a good sequence looks like. LinkedReach assumes you don’t. Tell the AI in plain English — “I want to reach VPs of marketing at B2B SaaS, my offer is X, my goal is to book a demo” — and it builds the sequence with you, suggests the tone, and ships it to the canvas in one click.
Already know what you’re doing? Hit “Generate with AI” in the canvas and the AI proposes the best-fit sequence shape for your offer. Either way: founders ship a working campaign on day one without copying someone else’s playbook.
Most “AI replies” are auto-responders that fire the same CTA on every reply. LinkedReach’s agent is built to build relationships. On every inbound, it scores warmth, reads the lead’s role, skills, and recent posts, and writes a reply that actually advances the conversation — matching the lead’s tone and length when configured to.
The meeting CTA only fires once warmth crosses the threshold you set. Before that, the agent keeps the conversation going. When it’s time, it pulls real open slots from your calendar (Microsoft 365 supported), proposes them, and books on confirmation. Operators choose whether replies send automatically or surface for one-click approval.
Setup used to mean a 90-minute “tell us about your ICP” form. LinkedReach replaces it with one field. Drop in the page where you describe your product or service — pricing page, landing page, even your homepage — and the AI extracts the name, the description, and the ideal-customer language in five seconds. You edit, accept, ship.
Then it scores every lead against every product you sell. The dashboard shows you which product is the best pitch for which prospect — before the first connection request goes out. Multi-product teams stop sending the wrong pitch to the right buyer.
Your closer doesn't want to read every "thanks but not now" reply. LinkedReach scores every replied lead on ICP fit, warmth, and intent — routes the qualified ones to the closer queue, archives the rest, and surfaces aggregate signal across the campaign.
ICP scoring is usually a pre-import filter. Tools tell you “these 500 leads are a good fit”, you trust the bucket, and by the time the message goes out the AI has forgotten why the lead was in there. The reasoning vanishes. The trust is in a number.
LinkedReach grades each lead against each product on five weighted dimensions per call. Title carries 30 points, industry 20, location 10, company size 10, custom criteria 30. Every lead is scored against every product you’ve defined — not once into a generic ICP bucket — and the reasoning is exposed in the lead row so an operator can audit any score.
Most personalisation runs at one temperature for all message types. The connect note (which you can never edit again, ever) gets the same generative variance as the day-7 follow-up. One off-tone connect request and the lead is burned permanently — the same risk profile as a low-stakes chase-up makes no sense.
LinkedReach tunes temperature per step, matched to recoverability. InMail runs at 0.6, the connect note at 0.7, regular message at 0.8, follow-up at 0.85. Higher-stakes steps — the ones you can’t take back — get tighter variance. Lower-stakes follow-ups get looser language so the pod stays fresh across thousands of sends.
An AI auto-replier is one “I’m a lawyer, you have 24 hours to delete my data” message away from a deliverability nightmare. The model doesn’t understand which replies cross from “let me handle that” into “you really do not want me to handle that”. The cost of getting that line wrong is not symmetric.
LinkedReach keyword-scans every inbound for legal, GDPR, contract, pricing, refund, “remove me”, lawyer, complaint and similar high-stakes patterns. On match: thread-level auto-send is disabled, a the agent alert is filed, and a human picks up via the inbox. No silent auto-response on a thread that should never have one.
Standard chat-completion APIs degrade once threads get long. The model starts to overweight recent messages and forgets the original ask. Your pod opens about a May meeting, the prospect engages over 12 messages across 3 weeks, and by message 13 the AI is offering to send a calendar link to a meeting that’s already booked.
LinkedReach compacts long histories by structure, not by inference. When a conversation exceeds 10 messages, the agent keeps the very first message (sets the topic) plus the most recent 9. The dropped middle is replaced with a single “[N earlier messages elided]” marker. No extra LLM call to summarise; the topic is preserved by structure.
Set a sender’s language and it holds all the way through. The agent searches in that language, reads posts in it, writes connection notes, messages, follow-ups and comments in it, and answers replies in it — without you keeping a parallel set of templates per market.
It is one authority, injected once, rather than an instruction bolted onto each prompt — so a sender working the German market does not quietly revert to English three steps into a sequence, and does not surface prospects it has no way of writing to.
Hard sending caps aren’t a feature you toggle — they’re enforced at the worker level. Operators can’t override them from the UI. Every action is randomised in timing, each account’s connection is managed for you, and any LinkedIn restriction instantly pauses the affected account and tightens pacing across the rest of the pod.
LinkedIn’s anomaly model isn’t watching raw speed — it’s watching the rhythm. Tools that batch fifty connects in five minutes trigger soft warnings the same day. A bot fires actions on a stable cadence; a person pauses between conversations to think.
LinkedReach enforces a 60-second minimum gap, scaled by multipliers that stack. Warmup day 1–3 multiplies by 3. Recent timeout multiplies by another 3. A new sender that just got warned waits 9× the floor before its next action. Capped at 4 minutes so a stale state can’t pin a worker indefinitely.
Per-account pacing tools let a warned sender’s pod-mates keep firing at full volume. LinkedIn sees the unchanged behaviour across the pod and within 24 hours restricts the whole team in lockstep. One sender takes a hit; the team takes the consequences.
The moment LinkedIn flags a sender — a restriction, an “unusual activity” notice, a “please slow down” — LinkedReach catches it and pushes a 3× pacing multiplier across the whole pod for 24 hours. A second flag inside that window auto-pauses the account.
When a pod has 8 senders, the import lists overlap — every agency adds the same target accounts to multiple campaigns. Without dedup, two SDRs in the same pod connect with the same VP of Marketing in the same week. The prospect thinks they’re being spammed by a content farm; the agency loses the lead twice.
LinkedReach runs workspace-scoped dedup on the (recipient_linkedin_id × action_type × 7-day window) tuple. The second sender’s queued action is silently postponed past the window, not failed. The action stays in the queue and fires once the window clears — no lead is dropped, no sender collides.
Switching between fifty LinkedIn tabs is how operators burn out. The unified inbox surfaces every reply across every connected sender, attributed to the right account, sortable by intent, warmth, or campaign.
Native sync with the CRMs that matter. Webhook triggers for everything else. Zapier for the long tail. Lead activity, reply state, and meeting bookings flow into your pipeline without an admin running CSV exports every Friday.
Every object in the product — senders, campaigns, sequences and their steps, leads, inbox, agents, activity — is addressable over a scoped REST API. Connect Claude or a ChatGPT Custom GPT to it and you can ask in plain language which campaigns are stalled, how many leads from the Berlin list replied, or whether the new agent is out-performing the old one, and have the answer come from real records rather than a guess.
The boundary is deliberate, and it is not the one people expect: an assistant can read and shape everything you own, and cannot send anything on LinkedIn. There is no send endpoint and no send tool. Outbound runs only inside the safety engine, which owns pacing, daily caps and the warm-up ramp — so nothing you connect can talk an account past a limit.
Open the simulator on any campaign and the agent runs the full sequence against synthetic-but-realistic leads. You read every drafted message in your voice, watch the agent handle a fake angry reply, see the warmth score after each round, and confirm the meeting slots it would offer hit your actual calendar — without burning a real connection request.
When the run finishes, one click generates a public read-only URL of the entire report — every message, every warmth trajectory, every AI decision rationale. Send it to your pod lead, your RevOps person, or your manager for greenlight signoff. They don’t need a LinkedReach account to view it.
Invite-only early access. Hand-reviewed within one business day. Connect a LinkedIn account, build a sequence, send your first message in under ten minutes.