Headcount
Growth Metrics
24-month headcount trend
215
Employees
+134%
2.3x
Headcount
92 → 215
1.6 yrs
Median Tenure
Growth Trajectory
24-month headcount – 2.3x growth since Feb '24
22218815412086
Feb '24Feb '26
215 now
Departments
Team Composition
Department breakdown with growth rates (last 3/6/12 months)
Largest Team
Entrepreneurship18.5%
27 employees · +23% YoY
| Entrepreneurship | 2718.5% | +8% 3 mo | |
| Customer Success and Support | 2517.1% | — | |
| Sales | 2215.1% | — | |
| Engineering | 1913.0% | +6% 3 mo | |
| Information Technology | 1812.3% | — | |
| Marketing | 117.5% | -8% 3 mo | |
| Business Development | 106.8% | — | |
| Consulting | 32.1% | — | |
| Education | 21.4% | — | |
| Accounting | 21.4% | — |
Workforce
Workforce DNA
Geography, skills and academic background
Top Locations
- United States41
- India30
- United Kingdom14
- Canada13
- England, United Kingdom13
Top Skills
- Lead Generation86
- Sales73
- Communication67
- Customer Service65
- Email Marketing60
Top Schools
- University of Amsterdam3
- Brainster2
- Udemy Alumni2
- The Islamia University of Bahawalpur2
- Indian Institute of Technology, Bombay2
Fields of Study
- Computer Science20
- Computational Science17
- Business Administration and Management, General16
- Marketing16
- Business/Commerce, General15
Content
Brand Presence
Recent posts and external mentions (last 30 days). Click to open on LinkedIn.
9.3+
Mentions / Wk
Notable External Mentions
DateMentionReactions
- Feb 16Mensur Zahirovic ⚡🔥 – For years I dreamt about this! I just created Openclaw (Jarvis) and then connected it to Instantly.ai. This will change outreach industry forever. Look how it created an entire campaign, imported leads and wrote emails in less than an minute. Im transferring my entire business to this. from lead generation, managing linkedin, cold calling. Everything will be run here. instead of spending hours on finding leads, writing emails this will manage everything. ! #instantly #outreach #b2b38
- Feb 20Christopher Monnier – Learn some battle-tested and vetted ways to use AI in your UX research while avoiding the s-word!0
- Feb 18Max Escuder – Is That AI… Or Have We Forgotten How To Be Amazed? There’s a parody video of a young guy in a park doing an impressive handstand routine. Athletic. Fluid. Real talent. Two men watching, one immediately dismisses it: “That’s AI.” More people gather. Same conclusion. It must be artificial. It must be generated. It must be fake. Only when someone walks closer to appreciate the performance does the joke land — a visual glitch appears, and the crowd proudly declares, "wait he has six fingers". “See? AI.” - "Told you it's AI" It’s funny. It’s satire. But it’s also telling. We’ve reached a moment in history where excellence is automatically questioned. A beautiful sunset? Filter. An incredible athlete? Generated. A stunning design? Prompted. AI is extraordinary. It’s transforming productivity, creativity, and entire industries. I’m bullish on it. But here’s the tension: not everything impressive is artificial. Mother Nature still produces visuals no model can rival. Human discipline still creates performance no algorithm can replicate. Real talent still exists — without a prompt. As leaders, innovators, and creators, we should embrace AI. But we shouldn’t lose our ability to recognize and celebrate what’s authentically human. If everything remarkable becomes “probably AI,” we risk dulling our sense of wonder. And that would be the real loss. (this video wasn't generated by AI 😉) #ArtificialIntelligence #Leadership #HumanExperience54
- Feb 20Anurag Jain – China is testing AI-powered sprinkler systems that detect heat patterns and activate automatically — stopping fires before they spread. This is more than firefighting innovation. It’s a powerful signal of where AI is heading next: 👉 From prediction to prevention 👉 From monitoring to autonomous response 👉 From reactive systems to real-time risk management Across industries — from infrastructure to hospitality, manufacturing to smart cities — the shift is clear: AI is becoming an active decision-maker, not just an analyst. 💡 3 takeaways for leaders as AI adoption accelerates 1️⃣ Move upstream in risk management Don’t just detect problems — invest in AI that prevents them before impact. 2️⃣ Automate critical response loops The biggest value comes when AI can act instantly, not wait for human intervention. 3️⃣ Embed intelligence into physical operations The next wave of AI ROI will come from real-world environments — buildings, supply chains, energy systems, safety infrastructure. The strategic question for leaders is no longer “Should we use AI?” It’s “Where can AI intervene earlier than humans ever could?” 🔎 Thoughts? #AI #Innovation #AIsummit #ArtificialIntelligence243
- Feb 12Otto Tatton – Clay > Instantly.ai > HubSpot I combined a stack that turns cold outreach into a $1M revenue engine. From signal → inbox → booked call → closed revenue ⚡ No generic lists. No deliverability disasters. No “where did that lead go?” chaos. Just: ➤ Build your ICP & intent signals inside Clay ➤ Enrich with 150+ data sources (job changes, funding, buying signals) ➤ Score & export only high-intent leads ➤ Push into Instantly and set up DFY domain infrastructure ➤ Scale safely across multiple mailboxes (no burnout) ➤ Build sequences that convert ➤ Let our AI Reply Agent auto-handle objections & book meetings ➤ Sync everything natively into HubSpot ➤ Auto-qualify, auto-route & track every deal ➤ Close with full visibility Signal → Delivery → Close. (No theory, just execution) Want the full Step-By-Step GTM Blueprint? ➤ Like & Connect with me ➤ Comment “GTM” and I’ll DM it over 🫡 Lets start building predictable pipeline today. ⚡418
- Feb 18Yogaraj Shanmuganathan – I’m happy to share this Enterprise AI Data Quality Platform project I worked on. Check it out here: https://lnkd.in/gyPf-YSc Building an AI-Driven Enterprise Data Quality Platform In enterprise SAP migrations and large-scale data integrations, ensuring data accuracy before BI reporting is critical. I recently built a cloud-ready Enterprise AI Data Quality Platform that simulates SAP-style sales data and applies machine learning–based anomaly detection to identify transformation errors and data inconsistencies. 🏗 Architecture Synthetic Data → ETL (Python) → PostgreSQL (Docker) → Isolation Forest → Anomaly Validation ⚙ Tech Stack PostgreSQL (Dockerized) Python (Pandas, SQLAlchemy) Isolation Forest (scikit-learn) Environment-based configuration GitHub version control 🎯 What This Demonstrates Enterprise data engineering pipeline design Data validation during migration scenarios ML integration within traditional ETL Cloud-ready architecture GitHub repository: 👉 https://lnkd.in/gM96TWDS Always exploring how AI can enhance enterprise data platforms. #DataEngineering #AI #PostgreSQL #SAP #MachineLearning #CloudArchitecture18
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