We train
AI Engineers.
- Built by operators from
/ / / / / MILA - Proprietary AI playbooks for Portfolio companies,
Prop-Trading firms and Asset Managers. - Deep AI × Domain × Deployment expertise in one team.
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/ - 500K+ AI practitioners taught since 2019.
- 200K+ Newsletter Subscribers
(incl. Nvidia CEO Jensen Huang). - 100K+ Discord members. Join us.
We deploy
Enterprise AI.
Towards AI is the AI deployment and education firm. We turn AI from a tool people experiment with into a capability teams rely on.
We believe AI initiatives fail when organisations lack the skills, education, and deployment capability to realise value, so we train your team while we build.
Three ways to help you.
For
Individuals
- Academy:From developer to full-stack AI engineer
- Content:Newsletter, YouTube, LinkedIn
- Community:100k+ members on Discord
For Enterprise
Workforce Trainings
- Foundations:AI literacy, policy, and governance
- Non-technical teams:10x productivity with sub-agents
- Engineering teams:Become best-in-class AI engineers
- Leadership:AI strategy and value-creation playbooks
For Enterprise
Custom AI Deployments
- Scope:Identify high-impact AI opportunities
- Build:Ship production-grade AI, not demos
- Sustain:Tools your team owns end-to-end
Three groups we train.
Our enablement practice is built for distinct audiences. Select a group.
From AI-curious to AI-capable, in your own time.
Self-paced courses, our book that has sold 10,000+ copies and is used inside enterprise teams, our weekly newsletter, daily Discord, and a writer network of 4,000+ AI practitioners.
- Full-stack AI Engineer (1k+ courses sold!)
- Certifications recognised by hiring teams
- A community where you can actually ask questions and get answers
Where we create value.
Our value-creation practice ships custom AI into operations. Select a sector to see how.
AI deployments tied to EBITDA.
Our co-founders come from BCG and have worked inside leading PE firms. We partner with PE partners, operating partners, and portco CEOs to scope, build, and roll out AI use cases that show up on the next quarter’s P&L. We measure engineering proof, adoption, and financial impact separately.
- Diligence support and value-creation thesis pressure-testing
- Document automation, revenue-ops co-pilots, support deflection
- Cross-portfolio enablement so one playbook lifts every company
Three recent engagements.
Workers logged material costs per job on paper. Backoffice manually re-typed from paper into Excel. Backlog piled up increasing Sales Outstanding, while monthly cost reporting was unstructured, leading to weaker audit trails.
- AI pipeline that takes PDFs of paper receipts
- Automatic extraction & calculation of material costs per Job
- Real-time consolidation & job-level reporting
- Audit-ready monthly reports
Hours of finance team time saved per week. Audit-ready reports available instantly, not at month-end.
Multiple team members spent hours daily scanning fragmented tender sites across many geographies. Easy to miss high-value tenders and impossible to quickly prioritise the ones worth pursuing most.
- Automated scraping across 5+ tender platforms
- AI ranking by relevance for Veloce
- AI categorization of likely win-rate
- Daily report of newly-relevant opportunities
Many hours of analyst time recovered per week. Pipeline focused on tenders Veloce is structurally most likely to win.
Special agents needed to apply state-of-the-art AI tools to mission-critical intelligence workflows, but lacked technical confidence with modern LLM pipelines.
- Multi-day intensive bootcamp on LLM pipelines
- Tailored to law-enforcement workflows
- Secure deployment for sensitive intelligence work
- Hands-on labs for context engineering
Special agents trained and ready to deploy AI tools across operational intelligence workflows.
One flywheel. Content to delivery.
Most AI firms are educators, consultants, or builders. Towards AI is all three. The flywheel keeps the company state-of-the-art and grounded in real outcomes.
Publication, Discord & Newsletter
- check_circle200k+ newsletter readers, including Jensen Huang
- check_circle100k+ Discord members and 4,000+ writers
Academy
- check_circle500,000+ learners taught since 2019
- check_circleSelf-paced courses, certifications, and cohort bootcamps
Community
- check_circleLive Discord, writer network, and alumni from every cohort
- check_circlePatterns surface from real engineering pain, not theory
Enterprise Work
- check_circleCustom AI deployments and enterprise enablement
- check_circleProduction lessons feed back into the curriculum
AI Engineering For Production. How to engineer the systems around the model.
Written by our CTO Louis-François Bouchard and CEO Louie Peters, the authors of Building LLMs for Production. It covers prompting, retrieval, evaluation, workflows, agents, and the harnesses that hold everything together, so a system your team can test, observe, and improve replaces one that worked once.
- check_circleComing before the end of 2026, release date to be announced
- check_circleFrom the authors of Building LLMs for Production, 10,000+ copies sold
- check_circleWaitlist members hear the release date first and get an invite to the live launch
Which AI model writes like a person?
We give 84 models the same briefs and ask each one to write a script in a real creator’s voice. A panel of four judges scores every draft blind, and the board updates as new models ship. Compare writing quality and cost per script before you choose a model for your product.
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Whatever you need from AI — learning, training, or building — you land with the same team of senior AI engineers and instructors.