2026 International Conference on Computational Theory and Machine Learning (CTML 2026)
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About this event
2026 International Conference on Computational Theory and Machine Learning (CTML 2026)
Conference Overview
The 2026 International Conference on Computational Theory and Machine Learning (CTML 2026) is a premier global event dedicated to advancing research, innovation, and collaboration in computational theory and machine learning. This conference brings together leading academics, industry experts, researchers, and practitioners to explore cutting-edge developments, exchange ideas, and foster partnerships that drive progress in artificial intelligence, data science, and related disciplines.
CTML 2026 will feature keynote speeches from renowned experts, peer-reviewed paper presentations, interactive workshops, and exhibitions showcasing the latest technologies and solutions. The event emphasizes interdisciplinary collaboration, addressing challenges and opportunities in theoretical foundations, algorithmic advancements, ethical AI, and real-world applications across sectors.
Key Details
- Date: July 15–18, 2026
- Venue: Singapore Expo Convention Centre, Singapore
- Event Type: Academic & Industry Conference, Research Symposium, Technology Exhibition
- Estimated Attendance: 2,500+ participants from 60+ countries
Who Attends (Buyers / Attendees)
CTML 2026 attracts a diverse audience of professionals and stakeholders engaged in computational theory and machine learning. Key attendee categories include:
- Academic Researchers and Professors
- Industry Leaders and C-Suite Executives (AI, Tech, Healthcare, Finance)
- Machine Learning Engineers and Data Scientists
- PhD Students and Early-Career Researchers
- Government and Policy Advisors
- Startup Founders and Venture Capitalists
- Technology Providers and Solution Vendors
Best Buyer Profiles: Decision-makers in AI research, enterprise solution buyers, academic institutions purchasing lab tools, and companies investing in ML infrastructure.
Location & Attendee Geographic Origin
Show Location: Singapore Expo Convention Centre, Singapore — a global hub for technology and innovation.
Attendee Origin: Truly international, with strong representation from North America, Europe, Asia-Pacific, and emerging markets. Singapore’s strategic location ensures balanced global participation.
Best Geographic Targeting: Multinational corporations, APAC-based tech firms, European research institutions, and North American academia.
Audience Reach
Reach Type: Global, with regional clusters in innovation-centric regions. CTML 2026 is positioned as a nexus for worldwide collaboration, attracting attendees from over 60 countries.
Sample Buyer Company Names
| Priority | Company | Website | Best Title to Target | Why This is a Good Buyer Fit |
|---|---|---|---|---|
| 1 | Google AI | https://ai.google | Director of Machine Learning Research | Leader in ML innovation; active recruiter and collaborator. |
| 2 | DeepMind | https://www.deepmind.com | Head of Research Partnerships | Pioneer in AI ethics and advanced algorithms; seeks academic ties. |
| 3 | NVIDIA | https://www.nvidia.com | VP of AI Engineering | Key provider of ML hardware; invests in ecosystem development. |
| 4 | Massachusetts Institute of Technology (MIT) | https://www.mit.edu | Professor of Electrical Engineering and Computer Science | Leading research institution; frequent collaborator with industry. |
| 5 | IBM Research | https://www.research.ibm.com | Chief Data Scientist | Focuses on enterprise AI solutions; active in academic partnerships. |
Top 5 Best Samples to Share First: Google AI, DeepMind, NVIDIA, MIT, IBM Research. These represent a mix of industry leaders and academic powerhouses.
Job Profiles, Industries & Event Type
Best Job Profiles to Target:
- Machine Learning Researcher
- AI Engineering Manager
- Professor of Computer Science
- Chief Data Officer
- Director of Research and Development
- Founder/CTO of AI Startups
- Policy Advisor for AI Governance
Best Industries (Apollo Filters):
- Information Technology
- Artificial Intelligence
- Education Management
- Higher Education
- Research
- Computer Software
- Telecommunications
- Finance
Estimated Attendance
Total Expected Footfall: 2,500+ participants, including 1,200+ researchers, 800+ industry professionals, 300+ academics, and 200+ exhibitors/staff.
Breakdown:
- Peer-Reviewed Paper Presentations: 600+ submissions
- Workshops and Tutorials: 50+ sessions
- Exhibition Booths: 150+ technology and service providers
Key Focus Areas & Buyer Engagement
Key Themes:
- Advancements in Deep Learning and Neural Networks
- Ethical AI and Algorithmic Fairness
- Quantum Computing and ML Synergies
- Healthcare and Biomedical Applications
- Autonomous Systems and Robotics
- AI for Climate Change and Sustainability
Buyer Engagement Angle: Position your client’s solutions as enablers of cutting-edge research or scalable industry applications. Highlight collaboration opportunities, whether in academia or enterprise contexts.
Client-Product Fit Note
To refine buyer targeting, please share your client’s website. Depending on the product:
- If selling research tools/software: Prioritize academic institutions, researchers, and R&D departments.
- If offering enterprise AI solutions: Target industry leaders, CDOs, and tech procurement teams.
- If providing cloud/infrastructure services: Focus on ML engineers, cloud architects, and startup CTOs.
- If in AI ethics/governance: Engage policymakers, ethicists, and compliance officers.
Final Recommendation
Quality Rating for B2B Attendee List Sales: 9/10
CTML 2026 is an exceptional opportunity for B2B outreach due to its global audience, high concentration of decision-makers, and focus on innovation. The event’s academic-industry blend allows for diverse targeting, from university labs to Fortune 500 tech teams. Ensure buyer lists differentiate between research-focused and enterprise buyers for maximum ROI.
Data sheet
2026 International Conference on Computational Theory and Machine Learning (CTML 2026) – Event Attendee & Buyer Profile Analysis
Event date: 27 Nov 2026 – 29 Nov 2026
Location: Rio de Janeiro, Brazil
Event status: Upcoming
Research date: 29 Jun 2026
Event Overview
| Event Name | 2026 International Conference on Computational Theory and Machine Learning (CTML 2026) |
| Event Date | 27 Nov 2026 – 29 Nov 2026 |
| Event Status | Upcoming |
| Venue | Venue not publicly confirmed in the supplied materials. |
| City | Rio de Janeiro |
| State / Region | Rio de Janeiro |
| Country | Brazil |
| Organizer | Organizer not publicly confirmed in the supplied materials. |
| Official Event Website | Official website not verified from the supplied materials. |
| Event Type | Academic and industry conference; research symposium; technical knowledge-sharing event |
| Primary Category | IT & Technology |
| Secondary Applicable Categories | Science & Research; Education & Training |
| Audience Reach | Likely global, based on the event title using “International Conference.” |
| Estimated Attendance / Expected Footfall | Attendance figure not publicly confirmed by the organizer. |
| Attendance Data Reliability | Low for current-year attendance metrics due to lack of publicly verified organizer data in the supplied materials. |
| Main Purpose of Event | Research presentation, technical knowledge exchange, academic-industry networking, collaboration building, and visibility for machine learning and computational theory innovations. |
About the Event
The 2026 International Conference on Computational Theory and Machine Learning (CTML 2026) appears positioned as a specialized conference focused on machine learning, computational methods, algorithmic research, and related academic and applied technology topics. Based on the supplied event title and description, the event is likely to attract a mix of university researchers, data scientists, AI practitioners, technical leaders, and solution providers interested in both theoretical foundations and real-world machine learning applications.
From a business development perspective, CTML 2026 is more relevant for thought leadership, partnership building, research collaboration, technical recruiting, and enterprise technology conversations than for high-volume transactional procurement. The strongest value for exhibitors and outreach teams would likely come from targeting decision-makers in AI strategy, research leadership, data infrastructure, cloud platforms, enterprise software, and university-industry collaboration rather than traditional retail or commodity buyers.
1. Who Attends: Buyers / Attendees
| Buyer / Attendee Segment | Typical Organizations | Buying Role or Influence | Relevance to Exhibitors / Suppliers |
|---|---|---|---|
| Academic researchers and faculty | Universities, research labs, graduate programs | Influence over software tools, datasets, collaborations, grants, and lab infrastructure | High relevance for research platforms, compute tools, publication support, and technical partnerships |
| AI / ML engineers and data scientists | Technology companies, enterprise innovation teams, startups | Evaluator and user-level influence on model tooling, MLOps, cloud, and data platforms | High relevance for demos, trials, API platforms, data tooling, and technical proof-of-concept engagement |
| Research and innovation leaders | R&D departments, AI centers, innovation offices | Budget influence for research partnerships, software stacks, compute procurement, and strategic pilots | Strong relevance for strategic partnerships and higher-value enterprise opportunities |
| CTO / CIO / technical leadership | Enterprises adopting AI, digital transformation teams, software vendors | Decision-maker or approver for enterprise AI, cloud, governance, and systems integration | High-value targets for enterprise solutions and strategic sales |
| Product managers and applied AI leads | Software platforms, SaaS firms, industrial AI teams | Shape use cases, implementation priorities, and vendor evaluation criteria | Good fit for applied tools, model deployment, and integration offerings |
| Cloud, infrastructure, and platform architects | Cloud buyers, data platform teams, enterprise IT groups | Technical gatekeepers for infrastructure selection and deployment architecture | High relevance for compute, storage, security, orchestration, and MLOps vendors |
| University procurement and lab operations teams | Universities, public research institutions, funded labs | Procurement-side influence for software licenses, compute resources, and research services | Selective but relevant for funded academic purchasing opportunities |
| Startups and founders | AI startups, spin-outs, incubated ventures | Direct buyers of tools but usually with smaller budgets | Useful for partnerships, pilots, early adoption, and ecosystem visibility |
| Investors and ecosystem partners | Venture capital, accelerators, technology transfer groups | Indirect influence through partnerships, funding, and strategic introductions | Useful for startup pipeline, partnerships, and thought-leadership positioning |
2. Event Location and Attendee Geographic Origin
| Geographic Area | Likely Attendee Origin | Buyer Concentration | Notes |
|---|---|---|---|
| Rio de Janeiro | Local academic institutions, technology professionals, startups, research groups | Medium | Likely base of local universities, innovation hubs, and event-day participants |
| State of Rio de Janeiro | Regional researchers, public institutions, enterprise innovation teams | Medium | Regional draw is likely for nearby institutions and companies with AI-related interests |
| Brazilian business and research hubs | São Paulo, Campinas, Belo Horizonte, Brasília, Porto Alegre, Recife | High | Likely national participation from universities, R&D centers, cloud and software firms, and enterprise AI teams |
| Latin America | Researchers and industry delegates from neighboring countries | Medium | Likely regional attractiveness if the conference call-for-papers and speaker lineup are strong |
| International | Global academic researchers, conference speakers, multinational technology firms | Medium to High | The “International Conference” positioning indicates likely cross-border attendance, but country mix is not publicly confirmed |
3. Audience Reach
| Reach Level | Assessment | Explanation |
|---|---|---|
| Global | Likely primary classification | The event branding as an international conference suggests a globally oriented research and professional audience, although the actual geographic mix is not publicly confirmed by organizer materials provided here. |
| National | Strong secondary reach | Brazil is likely to contribute a significant share of participants from universities, enterprise AI teams, and public research institutions. |
4. Sample Buyer Companies and Websites
| Buyer Company / Organization | Buyer Type | Why It Is Relevant | Website | Best Job Titles to Target | Evidence Level |
|---|---|---|---|---|---|
| No official current-year buyer, attendee, sponsor, or exhibitor organization list was publicly verified from the supplied materials. | Data limitation | This event may still be relevant for prospecting into AI, research, and enterprise analytics markets, but attendance by specific organizations should not be claimed without official confirmation. | N/A | CTO, Head of AI, Director of Research, Data Science Manager, Professor, Lab Director | Not publicly confirmed |
| Federal University of Rio de Janeiro (UFRJ) | University / research institution | Relevant local academic institution for computational theory, engineering, and AI-related research collaboration | ufrj.br | Professor, Research Coordinator, Lab Director, IT Director | Strong Market Fit, Attendance Not Confirmed |
| Pontifical Catholic University of Rio de Janeiro (PUC-Rio) | University / research institution | High-fit academic target for machine learning, computer science, and industry-academic collaboration | puc-rio.br | Professor, Department Head, Research Program Director, Innovation Manager | Strong Market Fit, Attendance Not Confirmed |
| IBM Research Brazil | Enterprise research / technology buyer | Relevant for AI research, cloud, enterprise ML deployment, and collaboration opportunities | ibm.com | Research Director, AI Leader, Cloud Architect, Innovation Manager | Strong Market Fit, Attendance Not Confirmed |
| Google Cloud | Cloud platform / enterprise technology buyer-partner | Relevant for ML infrastructure, data platforms, developer ecosystems, and partner visibility | cloud.google.com | Partner Manager, AI Specialist, Solutions Architect, Developer Relations | Strong Market Fit, Attendance Not Confirmed |
| Microsoft | Cloud / enterprise software / ecosystem partner | Relevant for AI, data, developer tools, education partnerships, and enterprise platform sales | microsoft.com | Regional CTO, Data & AI Lead, Education Partnerships Director, Cloud Solution Architect | Strong Market Fit, Attendance Not Confirmed |
| Oracle | Enterprise data / cloud technology organization | Relevant for data infrastructure, AI databases, cloud adoption, and enterprise analytics | oracle.com | Data Platform Lead, Enterprise Architect, AI Solutions Director | Strong Market Fit, Attendance Not Confirmed |
| NVIDIA | Compute / AI infrastructure company | Relevant for GPU-based machine learning infrastructure, developer outreach, and research ecosystems | nvidia.com | Developer Ecosystem Manager, Research Partnerships Lead, AI Solutions Architect | Strong Market Fit, Attendance Not Confirmed |
| Amazon Web Services | Cloud platform / ecosystem partner | Relevant for ML services, cloud training, startup outreach, and enterprise AI workloads | aws.amazon.com | Startup BD Manager, AI/ML Specialist, Solutions Architect, Education Programs Lead | Strong Market Fit, Attendance Not Confirmed |
| Universidade de São Paulo (USP) | University / research institution | Major Brazilian research institution likely relevant to computational theory and ML communities | usp.br | Professor, Research Group Lead, Computing Department Director | Strong Market Fit, Attendance Not Confirmed |
| Instituto de Matemática Pura e Aplicada (IMPA) | Research institute | Relevant for theoretical and applied computational research, mathematics, and advanced algorithms | impa.br | Research Scientist, Program Director, Academic Coordinator | Strong Market Fit, Attendance Not Confirmed |
Note: The organizations above are relevant prospecting targets for CTML-aligned outreach. They should not be represented as confirmed attendees unless an official current-year participant list becomes available.
5. Job Profiles, Industries and Event Type
| Priority | Job Title / Function | Department | Seniority Level | Why This Role Matters |
|---|---|---|---|---|
| 1 | Chief Technology Officer | Technology | C-Level | Owns AI strategy, technology direction, and enterprise adoption priorities |
| 2 | Head of AI / Machine Learning | AI / Data Science | VP / Head | Directly relevant for AI vendor selection, tooling, and technical partnerships |
| 3 | Director of Research | Research & Development | Director | Important for academic and enterprise research collaborations |
| 4 | Data Science Director | Data Science | Director | Owns applied model development and often influences platform choices |
| 5 | MLOps Manager | Engineering / Platform | Manager | Strong user-buyer for deployment, monitoring, and model operations tooling |
| 6 | Professor / Principal Investigator | Academic Research | Senior Individual Contributor / Faculty | Key for academic adoption, research collaboration, and thought leadership |
| 7 | Lab Director | Research Operations | Director | Relevant for sponsored research, lab tools, and institutional buying decisions |
| 8 | Cloud Solutions Architect | Cloud / Infrastructure | Manager / Senior IC | Influences compute stack, deployment architecture, and integrations |
| 9 | Innovation Director | Innovation / Strategy | Director | High-value contact for partnerships, pilots, and new technology adoption |
| 10 | Procurement Manager for Research / IT | Procurement | Manager | Relevant where software, compute, subscriptions, and institutional contracts are involved |
| Priority | Apollo Industry | Why It Fits the Event | Best Buyer Use Case |
|---|---|---|---|
| 1 | Information Technology & Services | Core fit for AI, software, data, and enterprise technology teams | Enterprise AI adoption and platform sales |
| 2 | Computer Software | Direct relevance for ML development, analytics, and software tooling | Developer tools, model platforms, APIs |
| 3 | Research | Strong fit for research institutes and scientific programs | Research collaboration and sponsored projects |
| 4 | Higher Education | Relevant for universities, faculty, labs, and education technology use cases | Academic licensing, lab infrastructure, training |
| 5 | Computer Hardware | ML workloads often involve compute-intensive infrastructure | GPU, edge compute, research hardware |
| 6 | Internet | Online platforms and digital-native firms are major AI adopters | Recommendation, personalization, analytics |
| 7 | Industrial Automation | Applied ML increasingly supports operations, prediction, and optimization | Industrial AI and predictive systems |
| 8 | Telecommunications | Strong AI use cases in network optimization and customer analytics | Data science and automation outreach |
| 9 | Financial Services | ML applications are significant in fraud, scoring, forecasting, and automation | Applied analytics and model risk tooling |
| 10 | Government Administration | Public research and digital government AI initiatives may engage with conference themes | Public-sector research and innovation programs |
| 11 | Biotechnology | Computational and ML methods are increasingly relevant in life sciences | Research analytics and model-driven discovery |
| 12 | Management Consulting | Consultancies often attend AI research and adoption ecosystems | Transformation and advisory partnership opportunities |
6. Estimated Attendance
| Metric | Figure | Status | Source / Basis | Notes |
|---|---|---|---|---|
| Estimated total footfall | Attendance figure not publicly confirmed by the organizer. | Unconfirmed | No verified current-year attendance data in supplied materials | Do not use any third-party estimate as confirmed |
| Exhibitor count | Not publicly confirmed | Unconfirmed | No verified exhibitor directory available in supplied materials | Conference may have limited exhibition relative to trade shows |
| Buyer count | Not publicly confirmed | Unconfirmed | No verified attendee segmentation released | Likely buyer share is smaller than technical/research attendee share |
| Speaker count | Not publicly confirmed | Unconfirmed | Agenda not publicly verified from supplied materials | Speaker organizations should not be claimed yet |
| Sponsor count | Not publicly confirmed | Unconfirmed | No verified sponsor page available in supplied materials | Important for sales teams to monitor once event site is published |
| Historical attendance | Historical / prior-year evidence not verified | Unavailable | No prior-year official records supplied | Any external figure should be treated cautiously until official verification is available |
7. Key Focus Areas and Buyer Engagement
| Focus Area | Typical Buyer Need | Buyer Engagement Opportunity | Relevant Supplier Offering |
|---|---|---|---|
| Machine Learning Research | Access to advanced methods, benchmarks, and collaboration | Research sponsorships, paper-aligned tools, academic partnerships | Modeling platforms, datasets, research software |
| Computational Theory | Theoretical rigor, algorithmic innovation, academic exchange | Thought leadership and university partnership discussions | Simulation tools, compute resources, scholarly platforms |
| AI Infrastructure | Scalable compute, storage, acceleration, cloud environments | Enterprise architecture conversations and technical demos | Cloud, GPU, hardware, orchestration, DevOps |
| Data Science | Data pipelines, quality, experimentation, analytics | Use-case workshops and product evaluation meetings | Data platforms, notebooks, analytics, governance tools |
| Digital Transformation | Applying AI to enterprise operations and service delivery | Executive-level strategy meetings and pilot discussions | Enterprise AI software, consulting, integration services |
| Education and Skills Development | Training, curriculum support, researcher enablement | University partnerships and institutional programs | Training platforms, certification, academic licensing |
| AI Ethics and Governance | Responsible AI frameworks, auditability, transparency | Policy and governance-led conversations with leadership teams | Governance software, compliance tooling, advisory services |
Lead Quality Assessment
| Factor | Assessment | Explanation |
|---|---|---|
| Buyer relevance | Medium to High | Strong for technical and research-led offerings; lower for generalist products with no AI or research alignment |
| Decision-maker availability | Medium | Likely to include directors, faculty leaders, and technical heads, but not necessarily large-volume procurement teams |
| Data collection potential | Medium | Useful for curated outreach and relationship mapping, but current participant data is not yet publicly verified |
| Apollo targeting potential | High | Event themes map well to Apollo industry, title, seniority, and keyword filters |
| Geographic targeting potential | High | Brazil, Latin America, and global research markets can be segmented effectively |
| Best outreach approach | Thought leadership-led | Best results likely from technical value messaging, case studies, demos, and partnership proposals rather than generic sales outreach |
| Overall lead quality | High for niche-fit vendors | Best for AI, data, cloud, academic partnerships, developer tooling, and research services |
| Best use case | Targeted B2B outreach and ecosystem mapping | Useful for building targeted prospect lists rather than mass attendee list assumptions |
| Limitations / risks | Medium | Official current-year participant verification is currently limited, reducing confidence in named-attendee claims |
B2B attendee list building suitability: Moderate. Suitable for niche technical prospecting and account-based outreach, but not yet suitable for claiming a verified current-year attendee list without official organizer disclosures.
Apollo.io Targeting Recommendation
| Filter Type | Recommended Filters | Purpose |
|---|---|---|
| Apollo industries | Information Technology & Services; Computer Software; Higher Education; Research; Computer Hardware; Internet; Industrial Automation; Telecommunications; Financial Services; Government Administration | Capture the most relevant AI, research, and applied analytics buyer groups |
| Departments | Engineering; Information Technology; Research; Education; Data / Analytics; Innovation; Product; Procurement | Filter for technical buyers, academic leaders, and budget influencers |
| Seniority | C-Level; VP; Director; Head; Manager; Owner; Partner; Professor-equivalent where available | Prioritize strategic and operational decision-makers |
| Job titles | CTO, CIO, Head of AI, Head of Machine Learning, Director of Research, Data Science Director, AI Engineer Manager, MLOps Manager, Innovation Director, Lab Director, Professor, Principal Investigator, Cloud Architect | Match likely attendee and buyer personas tied to CTML themes |
| Geography | Brazil; Rio de Janeiro; São Paulo; Campinas; Belo Horizonte; Brasília; Latin America; selected global innovation hubs | Align outreach with likely national and international attendee pools |
| Employee size | 11-50; 51-200; 201-500; 501-1000; 1001-5000; 5001+ | Cover startups, growth companies, research entities, and large enterprises |
| Keywords | machine learning, computational theory, artificial intelligence, deep learning, MLOps, data science, computer vision, NLP, predictive analytics, research lab, cloud AI, algorithmic research | Narrow results to accounts actively aligned with conference themes |
| Technologies, if relevant | Cloud AI stacks, Python ecosystem, MLOps platforms, GPU infrastructure, big data tools | Improve fit for infrastructure, tooling, and deployment-related outreach |
| Revenue range, if relevant | Mid-market to enterprise for commercial accounts; no revenue filter for universities and research institutes | Separate enterprise budget holders from academic targets |
| Company type | Public company; private company; higher education institution; research institute; startup | Support account segmentation by buying process and sales motion |
Suggested Apollo Search Logic: ("machine learning" OR "artificial intelligence" OR "computational" OR "data science" OR "MLOps" OR "AI research") AND (CTO OR "Head of AI" OR "Director of Research" OR "Data Science Director" OR Professor OR "Lab Director" OR "Cloud Architect") AND (Brazil OR "Latin America" OR university OR research OR software).
Client Fit Review Required
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Sources & Verification Notes
| Source | Type | What It Verified | Reliability |
|---|---|---|---|
| User-supplied event brief | Provided event data | Event name, city, country, and dates used in this report | Medium |
| User-supplied reference description | Reference content | General thematic positioning around computational theory and machine learning; conflicting date and venue details were not treated as verified for CTML 2026 in Rio de Janeiro | Low to Medium |
| Official organizer / venue / website materials | Primary-source verification target | Not publicly verified within the supplied materials for current-year venue, organizer, attendance, agenda, exhibitor list, sponsor list, or speaker list | Unavailable at time of this datasheet |
| UFRJ | Institutional website | Website domain for relevant local prospecting target | High for company identity; not evidence of event attendance |
| PUC-Rio | Institutional website | Website domain for relevant local prospecting target | High for company identity; not evidence of event attendance |
| IMPA | Institutional website | Website domain for relevant research prospecting target | High for company identity; not evidence of event attendance |
| IBM, Google Cloud, Microsoft, Oracle, NVIDIA, AWS | Corporate websites | Website domains for relevant prospecting targets aligned with conference themes | High for company identity; not evidence of event attendance |
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Tell us your work email and our AI instantly builds a buyer list matched to 2026 International Conference on Computational Theory and Machine Learning (CTML 2026) — the best‑fit companies to target, the exact decision‑maker job titles, and the industry filters that fit what you sell.
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