LinkedIn Algorithm 2026: What 360Brew Actually Changed (And What CMOs Should Do)

Luke Costley-White

Marketer leaving office with cardboard box as LinkedIn algorithm 2026 reach graph collapses on screen behind them
一道専心
One Path, Total Commitment

Median LinkedIn reach is down 66% from its 2023 peak. Marketing teams are posting less, panicking more, and the term “360Brew” is bouncing around every B2B Slack channel worth being in.

Here’s the problem: most of what’s being written about it is technically wrong. And if you’re building your response to the wrong diagnosis, you’ll get the wrong result.

This article covers what actually changed in LinkedIn’s feed architecture, what the data really shows (including the parts that complicate the “LinkedIn is broken” story), and what B2B marketing leaders should do about it. If you’re also navigating algorithm changes across other platforms, Meta’s Andromeda update follows a similar AI-driven logic and is worth reading alongside this one.

The short answer: LinkedIn’s feed now runs on a two-stage AI pipeline: an LLM retrieval system that distributes content based on topic relevance (not connections), and a sequential ranker that learns each member’s professional interests over time. The result: niche expertise and consistent posting now drive reach more than follower count. The popular term “360Brew” refers to a research model, not the live system.

First, let’s get the name right: what 360Brew actually is

360Brew is not LinkedIn’s live production algorithm. This matters, and almost nobody gets it right.

In January 2025, LinkedIn’s FAIT research team published a paper on arXiv (arXiv:2501.16450) describing a 150-billion-parameter transformer called 360Brew. It was designed to handle 30+ predictive tasks in a single unified pipeline, replacing thousands of fragmented task-specific models. The paper was technically impressive. It attracted attention fast.

Then Forbes ran a widely-read piece in January 2026 using “360Brew” to describe LinkedIn’s algorithm changes broadly, and the name stuck across the industry.

The problem: Trust Insights’ Q1 2026 research, which traced what was actually running in production, confirmed that “360Brew never achieved superior online performance over the existing production model and struggled with network-based recommendations.”

What you’re actually dealing with is a different system — and understanding it correctly changes how you respond.

How did the LinkedIn algorithm actually change in 2026?

LinkedIn’s Engineering Blog published a technical breakdown on March 12, 2026 confirming the real production system. It’s a two-stage pipeline. Here’s what each stage does in plain terms.

Stage 1: LLM-powered retrieval (the new discovery layer)

LinkedIn now uses a large language model to semantically understand what your content is about — not keyword matching, but genuine topic comprehension. The practical result: content can now reach people who don’t follow you, as long as it aligns with their professional interests. Recall@10 (a measure of how well the system surfaces relevant content) improved 15% over the prior system.

This is the shift from a Social Graph to an Interest Graph — and it’s the most important structural change in how LinkedIn distributes content. Your connections no longer determine your reach. Your topic relevance does.

Stage 2: The sequential ranker (the new relevance layer)

The second stage processes 1,000+ of each member’s past interactions as an ordered sequence — not a static snapshot of preferences, but a professional trajectory. If someone engaged with fintech regulation content on Monday and risk management on Tuesday, Wednesday’s feed reflects that learning arc.

For content creators and marketing teams, the implication is direct: consistent topic focus compounds. Someone who posts about one subject area for 90 days will build what practitioners call “semantic authority” — a signal the algorithm recognises and rewards. Someone who posts erratically across five different topics dilutes that signal every time.

What does the LinkedIn algorithm prioritise in 2026?

Topic consistency over follower count. The Interest Graph means niche specialists are 2.8x more likely to be prioritised in distribution. A subject matter expert with 500 followers can now outreach someone with 50,000 if their content is more topically relevant to a given audience.

Deep engagement over vanity metrics. LinkedIn added Saves and Sends as creator metrics in September 2025 for a reason — they’re weighted 5-10x more than a like. Thoughtful comments that generate replies carry more weight than surface-level reactions. The algorithm surfaces content people actually find useful. One important implication for how you measure: saves and substantive comments arriving 24-72 hours after posting perform 4-6x better as ranking signals than an immediate engagement spike. If you’re judging post performance at the 24-hour mark and moving on, you’re cutting the measurement window short.

Profile-content alignment. Every post is cross-referenced against your headline, About section, and experience before distribution begins — not as a ranking signal, but as a pre-distribution gate. If your profile doesn’t establish credible expertise in the topic you’re posting about, the system restricts reach before any engagement signal is even measured. You don’t get the chance to earn your way out of it. If your headline is generic and your posts scatter across topics, the feed ranker has less to work with. Practitioners call this the “Profile Audition” effect.

Active anti-gaming filters. Engagement pods are detected and suppressed. LinkedIn confirmed on May 22, 2026 that its LLM detection system now identifies AI-generated content with 94% accuracy — and the penalty is harder than most teams realise: affected posts aren’t just deprioritised in the feed, they’re restricted to the poster’s immediate network only, cutting out-of-network discovery entirely. Human-written posts outperform AI-generated ones by 40%+. The crackdown also extends to automated bulk commenting and restatement comments — replies that simply restate the original post without adding perspective are now flagged and suppressed. “Link in first comment” no longer avoids the external link penalty: that workaround stopped working. Posting twice in 24 hours cannibalises both posts’ reach.

For a practical guide on tracking the metrics that now matter, our LinkedIn Creator Analytics guide covers how to structure your reporting around saves, sends, and comment depth.

If you want to audit your own LinkedIn presence against all five signals right now, we put together an 18-point checklist: The LinkedIn Algorithm 2026 Alignment Checklist.

What the data actually shows (and why the picture is more nuanced than the panic)

The reach numbers are real. Median LinkedIn impressions are down 66% from the 2023 peak (SayWhat, Q4 2025, analysis of 329,504 posts). Average post reach dropped 47% year-over-year (Richard van der Blom, October 2025, 1.8M+ posts). Company pages now reach 1.6–2% of followers, down from roughly 7% in 2021. Overall engagement fell 39% in the same dataset.

But here’s what those figures don’t capture: engagement per impression is up 12%. The posts that are performing are performing better than before. The reach is narrower, but the signal is cleaner.

Arjun Moorthy, a B2B operator who tracked LinkedIn performance across a cohort of B2B influencers comparing January/February 2025 to 2026, found this: “Eight saw engagement decline. Six saw it increase. There wasn’t a clean before-and-after break.”

That data point matters. The reach collapse is concentrated in specific behaviours: multi-topic posting, AI-generated content, engagement pods, external links. Accounts that had been doing those things felt the update hardest. Niche experts posting consistent, human, native content largely held steady — or improved.

The algorithm didn’t hurt LinkedIn. It hurt gaming.

The company page problem: why this is structural, not temporary

Company pages now reach 1.6–2% of followers. Personal profiles generate 561% more reach. This isn’t a glitch the algorithm will correct. It’s a design decision.

The Interest Graph is person-centric. It builds professional trajectories around individual members, not brand entities. A company page can’t accumulate semantic authority the way a person can — it has no professional trajectory, no consistent lived expertise that compounds over time.

This creates a strategic imperative most marketing teams are still treating as a tactical inconvenience: your LinkedIn strategy needs to shift meaningful effort from the company page to employee voices.

The B2B case for this is strong. 55% of B2B decision-makers use published content to vet vendors they’re considering (Edelman x LinkedIn, 2025). That vetting happens through people, not brand pages. For CMOs building pipeline through LinkedIn, the path forward runs through your subject matter experts, your founders, your sales leaders — not through a corporate page with structural reach limits.

Our LinkedIn Lead Generation guide covers how to structure this at the programme level, and our LinkedIn ABM guide covers how to reach non-follower audiences using the Interest Graph intentionally.

The CMO response: 5 practical actions for 2026

1. Audit your content themes — pick a lane

Pull your last 90 days of content from your company page and from any executives already active on LinkedIn. How many distinct topic areas are you covering?

The algorithm rewards coherence. If you’re mixing product updates, industry commentary, team culture content, and commentary across five different subject areas, the semantic signal is fragmented. Pick two or three topic areas that map directly to your buyers’ interests and commit to them. Our guide on using your LinkedIn data for content strategy covers how to do this systematically.

2. Shift your profile strategy: people over pages

Activate three to five executives or subject matter experts as consistent LinkedIn voices in your core topic areas. Each person builds their own semantic authority — your brand’s presence compounds across multiple accounts rather than concentrating in a single page with structurally limited reach.

A CMO posting consistently about B2B SaaS growth strategy will outperform your company page in pure distribution terms every time. Multiply that across four or five active voices and the aggregate reach is significantly larger than anything a company page generates.

Our LinkedIn lead generation guide covers the operational side of employee advocacy programmes.

3. Measure what the algorithm now rewards

Stop optimising for impressions and likes. Start tracking saves, sends, and comment depth — specifically whether replies are generating further replies.

LinkedIn added saves and sends as native creator metrics in September 2025 because these are the signals with the most weight in the feed ranker. A post with 40 saves and 200 impressions is algorithmically stronger than a post with 1,200 impressions and 30 likes. If your current reporting doesn’t show saves and sends, you’re optimising for the wrong outcomes.

How to Turn LinkedIn Creator Analytics Into a Content Strategy

4. Fix your content format mix

Carousels generate a 6.6% average engagement rate — the highest of any format (Socialinsider, 2026). Short native video under 90 seconds sees a 69% performance boost. Link posts average 3.25% engagement — the lowest of any format.

Native content consistently outperforms content that drives people off-platform. The Interest Graph is built to surface content people engage with on LinkedIn, not content that moves them away from it.

5. Redirect the company page — don’t abandon it

The company page still serves a purpose. Use it for social proof: customer wins, awards, team milestones, product announcements. Use it for event promotion and recruitment. Just don’t expect it to carry your organic distribution.

Your executives carry the reach now. For paid visibility, LinkedIn ads remain a viable complement to organic — though that’s a different budget conversation with different ROI expectations. Our LinkedIn Ads data guide covers how to measure the two in the same framework.

Common misconceptions worth correcting


What people think

What actually happens

“Link in first comment avoids the penalty”

Now also penalised

“More hashtags extend reach”

Irrelevant — the system reads semantic context, not hashtags

“Posting every day will restore reach”

Posting twice in 24h cannibalises both posts

“AI-edited copy is fine to post”

94% detection accuracy as of May 2026; affected posts restricted to immediate network only, not just deprioritised

“This will blow over like previous updates”

The architectural shift is confirmed by LinkedIn’s own engineering team and is permanent

The bottom line

LinkedIn isn’t broken. The algorithm is doing exactly what it was designed to do: distribute content to the people most likely to find it relevant, based on topic expertise and genuine engagement quality rather than follower count.

That’s good news for B2B marketing teams with real subject matter depth and something worth saying. The brands that will struggle are the ones that built their LinkedIn presence on post volume, engagement pods, and AI-generated content. That playbook is over.

The LinkedIn algorithm in 2026 rewards consistency, expertise, and content that earns genuine engagement from the right audience. A 90-day commitment to focused, human, expert-driven content from your key voices will outperform the old approach — not because the rules changed arbitrarily, but because LinkedIn finally has the technical infrastructure to enforce what it always said it valued.

If you want to run a quick audit of where your LinkedIn presence stands against the new signals, the LinkedIn Algorithm 2026 Alignment Checklist covers all five in 18 checks.

DOJO AI’s social analytics tools give your team visibility into which content themes are generating saves and sends, which voices are building semantic authority, and where your LinkedIn content strategy has gaps. Start a free trial and run your first content performance audit in under 24 hours.

Further reading

Sources: arXiv:2501.16450 (Firooz et al., January 2025); LinkedIn Engineering Blog, Hristo Danchev (March 12, 2026); LinkedIn Newsroom (May 22, 2026 — AI content detection update); Trust Insights Q1 2026 Unofficial LinkedIn Algorithm Guide; Richard van der Blom Algorithm Insights Report (October 2025); SayWhat/Chris Donnelly Q4 2025 State of Algorithm (329,504 posts); Socialinsider LinkedIn Benchmarks 2026; Edelman x LinkedIn Thought Leadership Impact Report (2025); Arjun Moorthy LinkedIn post (B2B influencer cohort data).

LinkedIn Algorithm 2026: What 360Brew Actually Changed (And What CMOs Should Do)

Luke Costley-White

Marketer leaving office with cardboard box as LinkedIn algorithm 2026 reach graph collapses on screen behind them
一道専心
One Path, Total Commitment

Median LinkedIn reach is down 66% from its 2023 peak. Marketing teams are posting less, panicking more, and the term “360Brew” is bouncing around every B2B Slack channel worth being in.

Here’s the problem: most of what’s being written about it is technically wrong. And if you’re building your response to the wrong diagnosis, you’ll get the wrong result.

This article covers what actually changed in LinkedIn’s feed architecture, what the data really shows (including the parts that complicate the “LinkedIn is broken” story), and what B2B marketing leaders should do about it. If you’re also navigating algorithm changes across other platforms, Meta’s Andromeda update follows a similar AI-driven logic and is worth reading alongside this one.

The short answer: LinkedIn’s feed now runs on a two-stage AI pipeline: an LLM retrieval system that distributes content based on topic relevance (not connections), and a sequential ranker that learns each member’s professional interests over time. The result: niche expertise and consistent posting now drive reach more than follower count. The popular term “360Brew” refers to a research model, not the live system.

First, let’s get the name right: what 360Brew actually is

360Brew is not LinkedIn’s live production algorithm. This matters, and almost nobody gets it right.

In January 2025, LinkedIn’s FAIT research team published a paper on arXiv (arXiv:2501.16450) describing a 150-billion-parameter transformer called 360Brew. It was designed to handle 30+ predictive tasks in a single unified pipeline, replacing thousands of fragmented task-specific models. The paper was technically impressive. It attracted attention fast.

Then Forbes ran a widely-read piece in January 2026 using “360Brew” to describe LinkedIn’s algorithm changes broadly, and the name stuck across the industry.

The problem: Trust Insights’ Q1 2026 research, which traced what was actually running in production, confirmed that “360Brew never achieved superior online performance over the existing production model and struggled with network-based recommendations.”

What you’re actually dealing with is a different system — and understanding it correctly changes how you respond.

How did the LinkedIn algorithm actually change in 2026?

LinkedIn’s Engineering Blog published a technical breakdown on March 12, 2026 confirming the real production system. It’s a two-stage pipeline. Here’s what each stage does in plain terms.

Stage 1: LLM-powered retrieval (the new discovery layer)

LinkedIn now uses a large language model to semantically understand what your content is about — not keyword matching, but genuine topic comprehension. The practical result: content can now reach people who don’t follow you, as long as it aligns with their professional interests. Recall@10 (a measure of how well the system surfaces relevant content) improved 15% over the prior system.

This is the shift from a Social Graph to an Interest Graph — and it’s the most important structural change in how LinkedIn distributes content. Your connections no longer determine your reach. Your topic relevance does.

Stage 2: The sequential ranker (the new relevance layer)

The second stage processes 1,000+ of each member’s past interactions as an ordered sequence — not a static snapshot of preferences, but a professional trajectory. If someone engaged with fintech regulation content on Monday and risk management on Tuesday, Wednesday’s feed reflects that learning arc.

For content creators and marketing teams, the implication is direct: consistent topic focus compounds. Someone who posts about one subject area for 90 days will build what practitioners call “semantic authority” — a signal the algorithm recognises and rewards. Someone who posts erratically across five different topics dilutes that signal every time.

What does the LinkedIn algorithm prioritise in 2026?

Topic consistency over follower count. The Interest Graph means niche specialists are 2.8x more likely to be prioritised in distribution. A subject matter expert with 500 followers can now outreach someone with 50,000 if their content is more topically relevant to a given audience.

Deep engagement over vanity metrics. LinkedIn added Saves and Sends as creator metrics in September 2025 for a reason — they’re weighted 5-10x more than a like. Thoughtful comments that generate replies carry more weight than surface-level reactions. The algorithm surfaces content people actually find useful. One important implication for how you measure: saves and substantive comments arriving 24-72 hours after posting perform 4-6x better as ranking signals than an immediate engagement spike. If you’re judging post performance at the 24-hour mark and moving on, you’re cutting the measurement window short.

Profile-content alignment. Every post is cross-referenced against your headline, About section, and experience before distribution begins — not as a ranking signal, but as a pre-distribution gate. If your profile doesn’t establish credible expertise in the topic you’re posting about, the system restricts reach before any engagement signal is even measured. You don’t get the chance to earn your way out of it. If your headline is generic and your posts scatter across topics, the feed ranker has less to work with. Practitioners call this the “Profile Audition” effect.

Active anti-gaming filters. Engagement pods are detected and suppressed. LinkedIn confirmed on May 22, 2026 that its LLM detection system now identifies AI-generated content with 94% accuracy — and the penalty is harder than most teams realise: affected posts aren’t just deprioritised in the feed, they’re restricted to the poster’s immediate network only, cutting out-of-network discovery entirely. Human-written posts outperform AI-generated ones by 40%+. The crackdown also extends to automated bulk commenting and restatement comments — replies that simply restate the original post without adding perspective are now flagged and suppressed. “Link in first comment” no longer avoids the external link penalty: that workaround stopped working. Posting twice in 24 hours cannibalises both posts’ reach.

For a practical guide on tracking the metrics that now matter, our LinkedIn Creator Analytics guide covers how to structure your reporting around saves, sends, and comment depth.

If you want to audit your own LinkedIn presence against all five signals right now, we put together an 18-point checklist: The LinkedIn Algorithm 2026 Alignment Checklist.

What the data actually shows (and why the picture is more nuanced than the panic)

The reach numbers are real. Median LinkedIn impressions are down 66% from the 2023 peak (SayWhat, Q4 2025, analysis of 329,504 posts). Average post reach dropped 47% year-over-year (Richard van der Blom, October 2025, 1.8M+ posts). Company pages now reach 1.6–2% of followers, down from roughly 7% in 2021. Overall engagement fell 39% in the same dataset.

But here’s what those figures don’t capture: engagement per impression is up 12%. The posts that are performing are performing better than before. The reach is narrower, but the signal is cleaner.

Arjun Moorthy, a B2B operator who tracked LinkedIn performance across a cohort of B2B influencers comparing January/February 2025 to 2026, found this: “Eight saw engagement decline. Six saw it increase. There wasn’t a clean before-and-after break.”

That data point matters. The reach collapse is concentrated in specific behaviours: multi-topic posting, AI-generated content, engagement pods, external links. Accounts that had been doing those things felt the update hardest. Niche experts posting consistent, human, native content largely held steady — or improved.

The algorithm didn’t hurt LinkedIn. It hurt gaming.

The company page problem: why this is structural, not temporary

Company pages now reach 1.6–2% of followers. Personal profiles generate 561% more reach. This isn’t a glitch the algorithm will correct. It’s a design decision.

The Interest Graph is person-centric. It builds professional trajectories around individual members, not brand entities. A company page can’t accumulate semantic authority the way a person can — it has no professional trajectory, no consistent lived expertise that compounds over time.

This creates a strategic imperative most marketing teams are still treating as a tactical inconvenience: your LinkedIn strategy needs to shift meaningful effort from the company page to employee voices.

The B2B case for this is strong. 55% of B2B decision-makers use published content to vet vendors they’re considering (Edelman x LinkedIn, 2025). That vetting happens through people, not brand pages. For CMOs building pipeline through LinkedIn, the path forward runs through your subject matter experts, your founders, your sales leaders — not through a corporate page with structural reach limits.

Our LinkedIn Lead Generation guide covers how to structure this at the programme level, and our LinkedIn ABM guide covers how to reach non-follower audiences using the Interest Graph intentionally.

The CMO response: 5 practical actions for 2026

1. Audit your content themes — pick a lane

Pull your last 90 days of content from your company page and from any executives already active on LinkedIn. How many distinct topic areas are you covering?

The algorithm rewards coherence. If you’re mixing product updates, industry commentary, team culture content, and commentary across five different subject areas, the semantic signal is fragmented. Pick two or three topic areas that map directly to your buyers’ interests and commit to them. Our guide on using your LinkedIn data for content strategy covers how to do this systematically.

2. Shift your profile strategy: people over pages

Activate three to five executives or subject matter experts as consistent LinkedIn voices in your core topic areas. Each person builds their own semantic authority — your brand’s presence compounds across multiple accounts rather than concentrating in a single page with structurally limited reach.

A CMO posting consistently about B2B SaaS growth strategy will outperform your company page in pure distribution terms every time. Multiply that across four or five active voices and the aggregate reach is significantly larger than anything a company page generates.

Our LinkedIn lead generation guide covers the operational side of employee advocacy programmes.

3. Measure what the algorithm now rewards

Stop optimising for impressions and likes. Start tracking saves, sends, and comment depth — specifically whether replies are generating further replies.

LinkedIn added saves and sends as native creator metrics in September 2025 because these are the signals with the most weight in the feed ranker. A post with 40 saves and 200 impressions is algorithmically stronger than a post with 1,200 impressions and 30 likes. If your current reporting doesn’t show saves and sends, you’re optimising for the wrong outcomes.

How to Turn LinkedIn Creator Analytics Into a Content Strategy

4. Fix your content format mix

Carousels generate a 6.6% average engagement rate — the highest of any format (Socialinsider, 2026). Short native video under 90 seconds sees a 69% performance boost. Link posts average 3.25% engagement — the lowest of any format.

Native content consistently outperforms content that drives people off-platform. The Interest Graph is built to surface content people engage with on LinkedIn, not content that moves them away from it.

5. Redirect the company page — don’t abandon it

The company page still serves a purpose. Use it for social proof: customer wins, awards, team milestones, product announcements. Use it for event promotion and recruitment. Just don’t expect it to carry your organic distribution.

Your executives carry the reach now. For paid visibility, LinkedIn ads remain a viable complement to organic — though that’s a different budget conversation with different ROI expectations. Our LinkedIn Ads data guide covers how to measure the two in the same framework.

Common misconceptions worth correcting


What people think

What actually happens

“Link in first comment avoids the penalty”

Now also penalised

“More hashtags extend reach”

Irrelevant — the system reads semantic context, not hashtags

“Posting every day will restore reach”

Posting twice in 24h cannibalises both posts

“AI-edited copy is fine to post”

94% detection accuracy as of May 2026; affected posts restricted to immediate network only, not just deprioritised

“This will blow over like previous updates”

The architectural shift is confirmed by LinkedIn’s own engineering team and is permanent

The bottom line

LinkedIn isn’t broken. The algorithm is doing exactly what it was designed to do: distribute content to the people most likely to find it relevant, based on topic expertise and genuine engagement quality rather than follower count.

That’s good news for B2B marketing teams with real subject matter depth and something worth saying. The brands that will struggle are the ones that built their LinkedIn presence on post volume, engagement pods, and AI-generated content. That playbook is over.

The LinkedIn algorithm in 2026 rewards consistency, expertise, and content that earns genuine engagement from the right audience. A 90-day commitment to focused, human, expert-driven content from your key voices will outperform the old approach — not because the rules changed arbitrarily, but because LinkedIn finally has the technical infrastructure to enforce what it always said it valued.

If you want to run a quick audit of where your LinkedIn presence stands against the new signals, the LinkedIn Algorithm 2026 Alignment Checklist covers all five in 18 checks.

DOJO AI’s social analytics tools give your team visibility into which content themes are generating saves and sends, which voices are building semantic authority, and where your LinkedIn content strategy has gaps. Start a free trial and run your first content performance audit in under 24 hours.

Further reading

Sources: arXiv:2501.16450 (Firooz et al., January 2025); LinkedIn Engineering Blog, Hristo Danchev (March 12, 2026); LinkedIn Newsroom (May 22, 2026 — AI content detection update); Trust Insights Q1 2026 Unofficial LinkedIn Algorithm Guide; Richard van der Blom Algorithm Insights Report (October 2025); SayWhat/Chris Donnelly Q4 2025 State of Algorithm (329,504 posts); Socialinsider LinkedIn Benchmarks 2026; Edelman x LinkedIn Thought Leadership Impact Report (2025); Arjun Moorthy LinkedIn post (B2B influencer cohort data).

Wired magazine logo

WIRED Magazine Top 100 Startups

Wired magazine logo

WIRED Magazine Top 100 Startups

g2 logo

G2.com 4.9 out of 5 stars

g2 logo

G2.com 4.9 out of 5 stars

Join over 100+ brands
already growing with us.

Join over 100+ brands
already growing with us.

Start a demo

FAQ

Frequently asked questions

Frequently asked questions

What is DOJO AI?

DOJO is an intelligent marketing system that watches every channel continuously, builds a living knowledge graph of your brand's marketing reality, and deploys specialised agents that execute work autonomously before you've had to ask. Not a tool. Not a platform. A system. Every signal your brand produces flows in, every action feeds back, and the system compounds its understanding over time. Most marketing software gives you data. DOJO gives you a system of record, context, and execution: one place where everything is captured, connected, and acted on. Instead of switching between Google Ads, Meta, LinkedIn, GA4, and social dashboards, you get one intelligent system that shows you what's working across all channels - and tells you exactly what to do about it. Specialized AI agents analyze your campaigns 24/7, identify opportunities competitors miss, and help you move faster than companies 10x your size.

Who is DOJO built for?

DOJO is built for marketing teams that want to spend their time on decisions that require human judgment, not on tasks that don't. If your team is stretched across too many channels, too many tools, and too many reports, DOJO replaces the operational burden with a system that runs continuously and arrives with work already done. It's used by in-house marketing teams, agencies managing multiple client accounts, and founders who want the output of a full marketing department without the overhead of one.

Is DOJO suitable for marketing agencies?

Yes. Agencies are one of DOJO's core use cases. The system connects across multiple client accounts, automates reporting and content production, and runs campaign monitoring continuously — so account managers spend time on client relationships and strategy, not on manual tasks that don't require their judgment. DOJO builds a separate knowledge graph for each client, so every recommendation and every piece of content is grounded in that client's actual brand history, not generic best practice.

How does DOJO work with existing tools?

DOJO connects to your existing channels through proprietary connectors and a live web crawler. Google Ads, Meta, LinkedIn, your website, brand mentions, competitor movements — everything flows in automatically, with no manual pulls required. You don't have to replace your stack to use DOJO. The system reads your existing data, connects it, and builds context on top of it. Over time, that context becomes the foundation for every recommendation and every action DOJO takes on your behalf.DOJO builds a separate knowledge graph for each client, so every recommendation and every piece of content is grounded in that client's actual brand history, not generic best practice.

What ROI can I expect?

DOJO customers typically see measurable cost reductions and efficiency gains within the first 90 days, with outcomes compounding as the system builds context over time. Here's what customers have reported: 79% drop in cost per acquisition(Morningstar) 3x conversion volumein the same 23-day window (Morningstar) 40% drop in acquisition costs(Broadvoice) 15x faster marketing reporting(Ozone API) 3x more efficient Google Adsquarter over quarter (Ecologi) 290% increase in content output(Broadvoice) 20 hours saved per month, returned to strategy (Morningstar) The compounding effect matters here. The longer DOJO runs, the more context it builds, and the more precisely it acts. Early results are strong; they get better.

How does DOJO compare to HubSpot, Jasper, or other AI marketing tools?

Most AI marketing tools fall into one of two categories: workflow automation (HubSpot, Marketo, ActiveCampaign) that executes campaigns you set up, or content generation (Jasper, Copy.ai) that produces copy on demand. Both share the same limitation: they start from scratch every session. No memory of your brand history, your previous campaigns, or what your competitors have been doing. DOJO maintains a continuously updated knowledge graph of your entire marketing reality and runs specialised agents that read it daily, surface what needs attention, and execute work before you've asked. The longer DOJO runs, the more precisely it acts — because it compounds what it learns about your specific brand, market, and competitors. If you're evaluating options: Email and workflow automation: HubSpot, Klaviyo, Marketo AI content writing: Jasper, Copy.ai A system that watches every channel, builds brand context, and executes proactively: DOJO

Does AI marketing software actually improve over time, or does it reset every session?

Most AI marketing software resets every session. It has no memory of your brand, your campaigns, or what worked before. Every interaction starts from a blank slate. DOJO works differently. Every signal it captures, every workflow it runs, every recommendation it makes is fed back into the DOJO Graph. The system learns what works for your specific brand, in your specific market, against your specific competitors. It builds institutional knowledge that no other system carries. A team that's been using DOJO for six months has a system that understands their brand history, their campaign patterns, and their market in detail. That depth of context changes what the agents can do. The advantage grows every day the system runs, and it never stops running.

How does DOJO handle data security and privacy?

DOJO is built on enterprise-grade infrastructure with security and data privacy at its core. Your brand data, campaign history, and knowledge graph are kept entirely separate from other customers' data. For detailed information on data handling, storage, and compliance, see our Privacy Policy and Data Processing Agreement, or speak to our team directly when you book a demo.

Join over 100+ brands already growing with us.

Book a demo

Wired magazine logo

WIRED Magazine Top 100 Startups

Wired magazine logo

WIRED Magazine Top 100 Startups

g2 logo

G2.com 4.9 out of 5 stars

g2 logo

G2.com 4.9 out of 5 stars