Anshar Labs Deploys TekFlix AI to Turn Corporate Manuals Into Reels

TekFlix: How AI Turns Data Into Cinematic Stories With Anshar Labs — Ai4

Ai4

As 72 million skilled workers head for retirement by 2030, enterprise training must abandon static documentation and fragmented RAG searches for knowledge-graph-driven micro-video tailored to Gen Z.

AI-assisted summary of TekFlix: How AI Turns Data Into Cinematic Stories With Anshar Labs — original source. Check the original for full context.

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## Background Published on YouTube by Ai4 on August 15, 2026, this presentation features Pinaki Saha, Founder & CEO of Anshar Labs, introducing TekFlix. The session explores how Anshar Labs leverages its proprietary knowledge graph engine, Graphit, to convert dense enterprise PDFs, webinars, and operational manuals into bingeable, cinematic micro-stories.

## TL;DR Addressing the urgent collision between an impending retirement cliff of 72 million workers and the sub-60-second attention spans of incoming Gen Z talent, TekFlix transforms hundreds of pages of static enterprise documentation into episodic micro-learning curricula. To solve the narrative limitations of standard vector RAG pipelines, the platform uses Graphit to map complex conceptual ontologies before an agentic media engine autonomously generates cinematic video, voiceover, and dynamic B-roll. The resulting swipeable mobile interface replaces legacy corporate LMS platforms and is actively deployed in high-stakes environments ranging from oncology discharge care to Wall Street trading floors and hands-on industrial trades.

## Atmosphere & style The presentation carries a sharp, visionary keynote energy that balances macroeconomic urgency with sleek tech-forward optimism. Saha delivers a crisp, structured narrative that vividly contrasts dusty legacy training binders with the cinematic pacing of prestige television and TikTok feeds. It feels like a focused, pragmatic pitch aimed at solving a critical enterprise crisis, moving seamlessly from architectural deep-dives to real-world impact across healthcare and industrial operations.

## Key takeaways - [00:04](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=4s) TekFlix combines TikTok's rapid vertical delivery with Netflix's episodic curriculum architecture to bridge the pedagogical gap for incoming Gen Z workers. - [00:04](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=4s) The platform addresses the clash between enterprise content explosion and cognitive attention compression by converting dense SOPs into high-impact 40- to 60-second reels. - [02:13](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=133s) With 72 million workers retiring by 2030 against 60 million Gen Z entrants, hands-on trades and critical infrastructure roles require accelerated onboarding that bypasses lengthy classroom training. - [02:13](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=133s) Multi-agent pipelines can ingest 150 to 500+ pages of unstructured enterprise documentation and structure a multi-season, episodic curriculum within 24 to 48 hours. - [04:13](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=253s) Standard RAG architectures fail in complex training workflows because vector similarity search retrieves fragmented chunks without maintaining pedagogical dependencies or narrative continuity. - [04:13](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=253s) The proprietary Graphit engine maps multimodal inputs—including PDFs, podcasts, and legacy videos—into an ontological knowledge graph before creative generation begins. - [04:13](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=253s) A dual-tier agentic architecture separates structural curriculum design in Graphit from the autonomous media engine that generates cinematic B-roll, audio, and visual assets. - [07:09](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=429s) TekFlix replaces legacy LMS frameworks with a consumer-grade mobile interface featuring vertical reels, autoplay trailers, and dynamic AI-generated quizzes. - [07:09](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=429s) Administrative analytics continuously evaluate employee concept retention and pinpoint real-time knowledge decay across operational teams. - [07:09](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=429s) In healthcare applications, converting dense oncology discharge packets into micro-videos significantly improves post-chemotherapy patient adherence and treatment recall. - [07:09](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=429s) The platform functions as a unified framework combining deep conceptual understanding (Graphit), cinematic expression (generative AI), and interactive experience (mobile application).

## Detailed walkthrough ### [00:04](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=4s) TekFlix and the Gen Z Workforce Shift

Anshar Labs operates as an AI holding company developing focused enterprise technologies, with its flagship platform TekFlix launching as a deliberate hybrid of TikTok and Netflix. The impetus behind the platform stems from an urgent demographic transformation within corporate America. As Gen Z enters the workforce, organizations are confronting a fundamental pedagogical divide: younger workers do not learn the way Baby Boomers, Gen Xers, or even Millennials did. Long-form training videos, dense operational manuals, and hundred-page binders fail to register with a generation whose cognitive habits have been forged on sub-60-second, swipeable mobile media.

```mermaid flowchart LR accTitle: TekFlix Concept Origin subgraph Forces["Market Forces"] A["Dense Enterprise Data<br/>(SOPs, PDFs, Manuals)"] B["Attention Compression<br/>(Gen Z Swipeable Media)"] end subgraph Solution["TekFlix Hybrid Model"] C["Netflix Architecture<br/>(Seasons & Episodes)"] D["TikTok Delivery<br/>(Under 60s Reels)"] end A --> C B --> D C --> E["Bingeable Enterprise Training"] D --> E ```

This dynamic creates a sharp clash between a massive "content explosion" across enterprise documentation and severe "attention compression" among incoming talent. Corporate onboarding still relies on standard operating procedures (SOPs), extensive PDFs, and legacy compliance binders. TekFlix tackles the challenge of compressing these dense institutional materials into 40- to 60-second episodic micro-learning modules that match the consumption appetites of digital natives without diluting critical information.

- TekFlix name origin: Portmanteau blending the bingeable episodic layout of Netflix with the rapid, vertical format of TikTok. - Core constraint: Compressing complex operational procedures into high-impact snippets running 60 seconds or less. - Target demographic: An incoming Gen Z cohort that fundamentally rejects static reading packets and marathon classroom lectures.

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[02:13](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=133s) The Enterprise Retirement Cliff and Automated Curriculum Generation

The operational urgency for this format is compounded by a macroeconomic workforce crisis. By 2030, an estimated 72 million experienced workers will retire, while roughly 60 million Gen Z employees enter the market. Although generative AI continues to automate certain white-collar tasks, physical and hands-on industries—including plumbing, electrical work, construction, manufacturing, and data center infrastructure—remain entirely dependent on human labor. Incoming workers in these sectors cannot reasonably be expected to sit in training rooms for two weeks or endure 10-hour lecture marathons to master technical skills.

TekFlix automates the transition from institutional knowledge to bite-sized media using multi-agent systems. When an organization supplies 150 to 500 pages of raw operational documentation, the platform does not merely summarize the text. Instead, an agentic pipeline analyzes domain relationships, extracts underlying ontologies, and architects a full multi-season, episodic curriculum within a day or two. The structural pacing mirrors prestige television with clearly defined seasons and episodes, while individual chapters execute at TikTok speed.

- Workforce transition metrics: 72 million retirements colliding with ~60 million incoming Gen Z workers by 2030. - Manual and technical trade dependencies: Critical hands-on roles across data center hardware, wiring, facilities, and trades cannot be replaced by software and require rapid human onboarding. - Ingestion speed: 150 to 500+ pages of unstructured enterprise intellectual property converted into a structured, episodic curriculum in 24 to 48 hours.

---

[04:13](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=253s) Why RAG Fails: Graphit Knowledge Engine and Agentic Media Production

While contemporary foundation models such as Gemini and Claude excel at generative creative experiments, visual effects, and targeted advertising, the enterprise training sector remains largely neglected. Saha points out a glaring industry vacuum: billions of dollars flow into production-grade cinematic AI video for ad placements, yet the identical consumers viewing those advertisements are walking into uninspired corporate workplaces where structured training is absent. Furthermore, standard Retrieval-Augmented Generation (RAG) pipelines fail when translating deep procedural manuals into coherent instructional journeys because vector similarity searches lack structural narrative continuity.

```mermaid flowchart TD accTitle: Graphit and Media Engine Pipeline subgraph Ingestion["1. Multimodal Ingestion"] I1["PDFs & SOP Manuals"] I2["Podcasts & Long-form Video"] end subgraph Engine["2. Graphit Knowledge Graph"] G1["Ontological Association & Entity Extraction"] G2["Curriculum Architecture: Seasons & Episodes"] end subgraph Production["3. Agentic Media Engine"] M1["Cinematic Video Generation"] M2["Dynamic B-Roll & Audio Assembly"] end subgraph Output["4. Delivery Platform"] P["TekFlix Application Interface"] end

Ingestion --> Engine G1 --> G2 G2 --> Production M1 --> Output M2 --> Output ```

To resolve this limitation, Anshar Labs built Graphit, a proprietary knowledge graph engine serving as the system's "secret sauce." Graphit ingests disparate multimodal inputs—including PDFs, SOP manuals, internal podcasts, and legacy long-form video archives—and maps their ontological relationships, technical glossaries, and conceptual hierarchies. Once Graphit defines the narrative curriculum, a secondary agentic production engine takes over to compose the creative layer. This autonomous creative director synthesizes cinematic B-roll, high-fidelity music, and domain-appropriate visual assets into a cohesive instructional package.

- The RAG limitation: Standard RAG retrieves fragmented document chunks, failing to map pedagogical dependencies or maintain narrative continuity. - Ingestion capabilities: Unstructured PDFs, operational logs, internal audio, and historical video libraries mapped into interconnected knowledge nodes. - Dual-tier agentic architecture: Tier 1 structures the pedagogical ontology via Graphit; Tier 2 assembles cinematic B-roll, voiceover, music, and production assets.

---

[07:09](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=429s) The TekFlix Platform Experience and Real-World Deployments

The resulting user experience functions as a modern learning environment designed to supplant the legacy corporate Learning Management System (LMS). Users interact with vertical, swipeable reels featuring autoplay trailers, embedded interactive glossary popouts, and integrated social learning mechanisms. Rather than relying on rigid, pre-authored multiple-choice questions, the platform's AI dynamically generates contextual quizzes and certification milestones based on real-time user progress. An administrative analytics backend continuously monitors engagement, diagnosing which employees are recalling concepts, who is falling behind, and where knowledge decay occurs.

```mermaid flowchart LR accTitle: TekFlix Platform Features and Enterprise Sectors subgraph UX["TekFlix Platform Experience"] U1["Vertical Swipeable Reels"] U2["Autoplay Trailers & Glossary Popouts"] U3["Dynamic AI Quizzes & Analytics"] end subgraph Sectors["Active Implementations"] S1["Healthcare: Oncology Patient Recovery"] S2["Finance: High-Net-Worth Trading Desks"] S3["Enterprise: Automotive & Airline Security"] end UX --> Sectors ```

Beyond onboarding Gen Z workers (who will soon account for 30% of the active labor force), TekFlix has expanded across several mission-critical verticals: - Healthcare & Oncology: Cancer hospitals deploy the platform for outpatient post-chemotherapy care. Dense clinical discharge packets detailing complex dietary restrictions and physical exercise regimens are converted into under-60-second micro-videos, driving higher patient adherence, treatment comprehension, and clinical recall. - Capital Markets & Trading: Wall Street trading floors leverage TekFlix to train sales representatives on communicating nuanced financial products to high-net-worth clients. - Mission-Critical Operations: Active implementations span airline security personnel, automotive dealership sales teams, and commercial real estate operations.

Saha positions the platform as a unified three-part paradigm: deep conceptual "understanding" powered by Graphit, cinematic "expression" via AI-directed generative video, and interactive "experience" through the consumer-grade mobile application. The session concluded with an open invitation for enterprise pilot partnerships, vertical-specific co-development, and strategic investment.

## Notable quotes > What if your densest knowledge, the webinars, the PDFs, the hour-long trainings, could become a 90-second reel someone actually watches? — Pinaki Saha

> [00:04](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=4s) Content has exploded, but attention has compressed, and enterprises are losing institutional knowledge to retirement faster than anyone can document it. — Pinaki Saha

> [00:04](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=4s) turns unstructured content into cinematic micro-stories — Pinaki Saha

> [04:13](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=253s) Graphit, the knowledge graph engine behind it that lets AI understand a story before it can tell one. — Pinaki Saha

> [07:09](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=429s) turns oncology webinars into patient education and onboarding manuals into bingeable series. — Pinaki Saha

> [07:09](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=429s) Come see how understanding, expression, and experience come together to make knowledge spread like content instead of sitting like documentation. — Pinaki Saha

> [02:13](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=133s) By 2030, an estimated 72 million experienced workers will retire, while roughly 60 million Gen Z employees enter the market. — Pinaki Saha

> [04:13](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=253s) Standard RAG retrieves fragmented document chunks, failing to map pedagogical dependencies or maintain narrative continuity. — Pinaki Saha

## Mentioned Pinaki Saha, Anshar Labs, TekFlix, Graphit, Ai4, TikTok, Netflix, Gemini, Claude, Gen Z, Baby Boomers, Gen X, Millennials

## Video description Pinaki Saha - Founder & CEO, Anshar Labs

What if your densest knowledge, the webinars, the PDFs, the hour-long trainings, could become a 90-second reel someone actually watches? Content has exploded, but attention has compressed, and enterprises are losing institutional knowledge to retirement faster than anyone can document it. I'll introduce TekFlix, the platform I've built that turns unstructured content into cinematic micro-stories, and Graphit, the knowledge graph engine behind it that lets AI understand a story before it can tell one. The talk will walk through how this pipeline plays out in healthcare, enterprise training, and media, where the same system turns oncology webinars into patient education and onboarding manuals into bingeable series. Come see how understanding, expression, and experience come together to make knowledge spread like content instead of sitting like documentation.

[Source](https://www.youtube.com/watch?v=9PO5W3GDP-8)

中文简报

Anshar Labs 部署 TekFlix AI 将企业手册转化为短视频 随着 7200 万技术工人将在 2030 年前退休，企业培训必须摒弃静态文档和碎片化的 RAG 检索，转向面向 Gen Z 量身定制、由知识图谱驱动的微视频。 为应对 7200 万工人即将面临的退休潮与新入职 Gen Z 人才不足 60 秒注意力时长之间的紧迫冲突，TekFlix 将数百页静态企业文档转化为剧集式微学习课程。为解决标准向量 RAG 流程在叙事上的局限，该平台在智能体媒体引擎自主生成电影级视频、配音和动态 B-roll 之前，先利用 Graphit 绘制复杂的概念本体图谱。由此构建的可滑动移动端界面取代了传统企业 LMS 平台，并已在从肿瘤出院护理、华尔街交易大厅到实操工业技术等高风险关键环境中投入部署。 Anshar Labs 推出融合 TikTok 竖屏短视频与 Netflix 分季分集架构的平台 TekFlix，致力于将冗长繁杂的企业标准作业程序（SOP）压缩为 40 至 60 秒的高效微学习模块，以契合 Z 世代员工的认知习惯。 (0:04) 面对 2030 年前 7200 万资深员工退休及 6000 万 Z 世代涌入的代际断层，TekFlix 采用多 Agent 管线，可在 24 至 48 小时内将 150 至 500+ 页的非结构化企业文档转化为分季体系化培训课程。 (2:13) 针对标准 RAG 检索碎片化且缺乏教学连贯性的缺陷，TekFlix 搭载专有知识图谱引擎 Graphit，先梳理多模态输入背后的本体层级与叙事框架，再驱动 Agent 生成电影级 B-roll 与视听资产。 (4:13) TekFlix 旨在替代传统 LMS 学习系统，提供包含自动播放预告、动态 AI 测验等消费级移动端体验，目前已拓展至肿瘤出院患者宣教、金融交易培训及航司安保等关键业务场景。 (7:09)

Reading summary

Anshar Labs developed TekFlix, an AI-driven enterprise training platform combining Netflix-style episodic structure with TikTok-style vertical micro-learning, designed to address the divide between dense institutional documentation and the learning habits of the incoming Gen Z workforce. Demographic Shift and the Enterprise Retirement Cliff (2:13) Corporate onboarding faces an imminent demographic transition: by 2030, an estimated 72 million experienced workers will retire as approximately 60 million Gen Z employees enter the market, soon comprising 30% of the active labor force. Traditional training depends heavily on standard operating procedures, multi-hundred-page compliance binders, and static reading materials. However, younger workers' cognitive habits are shaped by sub-60-second mobile video, creating a sharp friction between expanding enterprise documentation and compressing employee attention spans. This gap is especially urgent in hands-on technical trades and physical infrastructure operations—including plumbing, electrical work, manufacturing, and data center facilities—that cannot be automated by software. Workers entering these sectors require fast, accurate operational training, yet conventional models demand that they sit through multi-week classroom seminars or 10-hour lecture marathons. Why RAG Fails: Graphit Knowledge Engine (4:13) While foundation models are widely utilized in advertising and visual effects, enterprise learning systems remain largely unaddressed. Anshar Labs notes that standard Retrieval-Augmented Generation (RAG) pipelines break down when converting procedural operational manuals into instructional courses, because vector similarity searches extract fragmented document chunks that lack narrative continuity and pedagogical sequencing. To overcome this limitation, Anshar Labs built Graphit, a proprietary knowledge graph engine. The system ingests between 150 and 500 pages of unstructured documentation, internal podcasts, and legacy videos within 24 to 48 hours. It maps domain ontologies, technical glossaries, and conceptual hierarchies to architect a multi-season curriculum. A secondary agentic production engine then functions as an automated creative director, assembling cinematic B-roll, voiceover, and audio into 40- to 60-second instructional episodes. Platform Experience and High-Stakes Industry Deployments (7:09) Designed to supplant legacy corporate Learning Management Systems, the platform delivers content through vertical, swipeable reels equipped with autoplay previews and embedded glossary popouts. Instead of static multiple-choice tests, the system dynamically generates contextual quizzes and certification milestones based on learner progress, while backend analytics track engagement and identify knowledge decay. Beyond general onboarding, the system has expanded into specialized operational environments. In oncology care, cancer hospitals convert dense post-chemotherapy discharge instructions covering dietary and physical regimens into micro-videos to improve patient adherence and clinical recall. Additional implementations include Wall Street trading desks training sales personnel on complex financial products, as well as airline security staff, automotive dealerships, and commercial real estate operations. Saha conceptualizes the platform as an integration of conceptual understanding through Graphit, cinematic expression via generative media, and an interactive mobile delivery interface, while Anshar Labs is actively seeking enterprise pilot partners and strategic co-development.

精炼阅读摘要

针对Z世代员工对传统长文本培训的排斥以及2030年即将到来的资深员工退休潮，Anshar Labs开发了融合Netflix剧集架构与TikTok短视频体验的TekFlix平台，利用专有知识图谱与多智能体系统将企业操作规程转化为高密度的电影级微学习内容。 劳动力认知变迁与企业培训内容冲突 (0:04) 随着Z世代进入职场，企业面临着认知与学习习惯层面的结构性鸿沟。传统企业入职与合规培训长期依赖动辄数百页的标准作业程序（SOP）、静态PDF文件与厚重活页夹，而年轻一代员工的注意力习惯是在移动端60秒以内的滑动流媒体中形成的。这种差异在企业内部引发了“内容规模爆炸”与新员工“注意力严重压缩”之间的剧烈摩擦。 TekFlix平台的设计旨在缓解上述矛盾。其名称融合了Netflix的剧集叙事布局与TikTok的快节奏竖屏形式，试图在不稀释核心业务规程的前提下，将繁复的企业文档浓缩为时长40至60秒的微型剧集模块，以契合数字原住民的信息摄入偏好。 退休断崖危机与知识资产自动化转化 (2:13) 宏观人口结构变化进一步加剧了企业知识转移的紧迫性。据预测，到2030年将有约7200万名经验丰富的资深员工退休，而同期进入职场的Z世代员工仅约6000万人。尽管生成式AI能够接管部分白领任务，但在电气施工、管道维修、精密制造和数据中心基础设施等高度依赖实操的行业中，人工依然不可替代，且新员工无法耗费数周时间脱产参与漫长的课堂讲授。 为缩短专业知识交接周期，TekFlix利用多智能体流水线在24至48小时内处理150至500页以上的非结构化企业文档。该系统不单纯提炼文本摘要，而是分析领域关系并提取底层本体，自动构建包含季（Seasons）和集（Episodes）的模块化课程大纲，将长篇操作标准转化为短剧式的学习路径。 破解传统RAG局限：Graphit与双层架构 (4:13) 演讲者Saha指出，尽管主流基础模型在创意广告和视觉特效领域投入巨大，但企业严肃培训市场仍存在技术空白。标准的检索增强生成（RAG）在处理深层操作手册时表现不佳，原因在于向量相似度检索容易割裂知识片段，无法维系教学所需的先验逻辑依赖与连贯叙事框架。 为此，Anshar Labs构建了名为Graphit的专有知识图谱引擎作为底层支柱。Graphit能够接入PDF、操作规程、内部播客及历史视频等多模态资料，梳理概念层级与术语关联。在此基础上，系统通过双层智能体架构运作：第一层由Graphit确定教学本体框架，第二层创意智能体则自动调度电影级B-roll素材、语音解说与音效，合成结构严谨的视听教学内容。 消费级移动交互体验与跨行业落地场景 (7:09) 在交付端，TekFlix致力于替代传统的企业学习管理系统（LMS）。其前端界面采用类似社交媒体的竖屏滑动流，配备自动播放预览、交互式术语弹窗及社交学习功能；系统摒弃预设的固定选择题，转由AI根据学习进度动态生成情境化测验，管理端则可实时追踪知识吸收曲线并定位概念遗忘盲点。 除面向预计占劳动力30%的Z世代群体外，该系统已拓展至特定专业场景：在肿瘤医疗领域，医院将复杂的化疗后出院宣教手册转化为60秒微视频，帮助提升患者的康复依从性；在华尔街金融交易室，该工具被用于培训销售人员推介复杂金融产品；此外，平台在航空安保、汽车经销及商业地产运营等业务流程中也展开了实际应用。 Saha将TekFlix的技术路径总结为三元范式：由Graphit支撑的深层知识“理解”、生成式AI视听引擎呈现的电影级“表达”，以及消费级移动端带来的交互“体验”。目前该平台正面向企业开放试点合作，但复杂高危实操场景对生成内容的零幻觉要求与长期技能转化效果，仍有待更广泛的跨行业部署检验。

Detailed notes — zh

## Background 该视频由 YouTube 频道 Ai4 于 2026 年 8 月 15 日发布。内容展示了 Anshar Labs 创始人兼首席执行官 Pinaki Saha 的主题演讲，主要介绍了其研发的微学习平台 TekFlix 与底层知识图谱引擎 Graphit，阐述如何将企业沉淀的厚重 PDF、研讨会与 SOP 转化为 60 秒以内的影视级微短剧。 ## TL;DR 面对 2030 年庞大退休潮带来的经验流失以及 Z 世代员工对长篇手册的抗拒，TekFlix 融合了 Netflix 的剧集逻辑与 TikTok 的竖屏滑屏形态，化解了企业“内容爆炸”与员工“注意力压缩”的冲突。系统依托专有知识图谱引擎 Graphit 克服了传统纯 RAG 缺乏全局业务逻辑的缺陷，可在 1 至 2 天内将数百页非结构化文档全自动转化为包含 B-roll 视听包装的微剧集。平台彻底颠覆了迟钝僵化的传统 LMS，提供单手滑动体验与 AI 实时动态考核。目前该技术已深度拓展至肿瘤患者康复宣教、华尔街金融培训与实体工业现场教学等广泛领域。 ## Atmosphere & style 演讲整体呈现出敏锐的商业洞察与前沿科技相融合的明快节奏，主讲人 Pinaki Saha 以严谨而富有说服力的叙事层层剖析代际更替带来的真实痛点。整场分享不仅有对知识图谱底层架构的理性拆解，更通过生动的视听化对比展现了微媒体颠覆传统教学的巨大潜力，兼具行业前瞻性与落地实操感。 ## Key takeaways - [00:04](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=4s) TekFlix 将 TikTok 的微短形态与 Netflix 的剧集架构相结合，旨在解决企业知识“内容爆炸”与员工“注意力压缩”之间的尖锐矛盾。 - [00:04](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=4s) 进入职场的 Z 世代深度依赖 60 秒以内的可滑动竖屏媒体，传统长篇 PDF、手册及数十小时的长视频培训已无法有效传达知识。 - [02:13](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=133s) 预计到 2030 年将有 7,200 万资深员工退休，制造业、建筑与电气工程等无法被 AI 完全替代的实体产业面临严峻的经验断代与新人培训压力。 - [02:13](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=133s) 借助智能体协同工作流，数百页的复杂专有流程与 SOP 文档可在 1 至 2 天内全自动转化为结构连贯的剧集式微课程体系。 - [04:13](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=253s) 针对当下 AI 视频工具过度聚焦商业广告与影视宣发的市场真空，Anshar Labs 专注于研发严肃的企业培训与教学大纲生成系统。 - [04:13](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=253s) 底层 Graphit 知识图谱引擎克服了纯 RAG 易丢失全局上下文的缺陷，遵循“先建立认知结构，再进行视听表达”的生成逻辑。 - [04:13](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=253s) 系统具备端到端的全自动媒体管线，能根据提取的知识点全自动匹配并渲染上下文 B-roll 素材与背景配乐。 - [06:06](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=366s) 颠覆传统 LMS 学习管理系统，提供单手滑动短视频、悬浮词典以及基于员工实际进度由 AI 实时动态生成的考评测验。 - [06:06](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=366s) 管理后台提供高粒度的行为分析，可深度追踪员工的知识记忆召回率、遗忘薄弱环节及高频复习数据。 - [07:55](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=475s) 在严肃医疗领域，将晦涩的肿瘤康复手册重塑为 60 秒微视频后，患者对健康指引的记忆留存率与康复方案依从性显著提升。 - [07:55](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=475s) TekFlix 进一步落地于金融投行销售、航空安保应急演练等多元场景，实现“理解、表达、体验”三位一体的知识闭环。 ## Detailed walkthrough ### [00:04](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=4s) 劳动力代际更替与注意力压缩：TekFlix 的诞生背景 AI 控股创新公司 Anshar Labs 孵化了多项前沿 AI 技术，其在年初推出的核心产品名为 TekFlix。创始人兼首席执行官 Pinaki Saha 解释道，这个名字直接融合了 TikTok（微短内容形态）与 Netflix（剧集架构体系）。研发 TekFlix 的初衷源于对进入职场的 Z 世代（Gen Z）学习行为的深度洞察。与婴儿潮一代（Baby Boomers）、X 世代（Gen X）甚至千禧一代不同，Z 世代拥有截然不同的信息摄取习惯：他们不再愿意翻阅长篇技术手册、PDF 或是厚重的 SOP 标准作业程序，对动辄数小时的传统长视频更缺乏耐心，取而代之的是对时长在 60 秒以内的“可滑动微媒体”（swipeable media that's only 60 seconds or less）的高度依赖。这种自幼养成的数字内容消费习惯，使得他们在迈向企业职场时，强烈要求企业培训与知识学习也以同样的极简碎片形态交付。 当前企业界面临着严峻的“内容爆炸”（content explosion）与“注意力压缩”（attention compression）的双重挤压。海量的内部知识、操作规程与合规文档层出不穷，然而员工能付诸单次学习的注意力窗口却被急剧压缩在几十秒之内。传统的企业培训手段无法满足这一诉求，如何将繁琐冗长的业务流程精准压缩为 40 至 60 秒、极具网感且易于消化的情景化片段，成为数字化转型必须跨越的鸿沟。为此，Anshar Labs 引入了基于智能体架构（Agentic system）的自动化生产管线，利用 AI 的大规模生成与高周转效率，彻底解决了企业培训内容无法低成本、高并发转化为短视频资产的规模化瓶颈。 - **核心受众习惯**：Z 世代习惯消费 60 秒以内的竖屏滑屏媒体，抗拒传统手册与大部头 SOP。 - **产品名称寓意**：TekFlix = TikTok（短平快形态）+ Netflix（剧集化知识索引）。 - **企业核心矛盾**：制度与知识的“内容爆炸”碰撞员工极度有限的“注意力压缩”。 --- ### [02:13](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=133s) Netflix 结构遇上 TikTok 切片：迎战 2030 企业退休潮 TekFlix 的内容设计哲学在于博采众长：它完全借鉴了 Netflix 的宏观架构，将企业知识沉淀为具备连贯叙事逻辑的“季”（Seasons）与“集”（Episodes），但在微观呈现上却采用类似 TikTok 的极致压缩形态，将每个知识点切成不超过 60 秒的微剧集。这一流程完全由后台智能体动态协同驱动：系统首先摄入企业的非结构化沉淀，通过自然语言处理与本体映射（Ontology mapping）构建深度知识关联网络，在短短一至两天内，便能将长达 150 页、200 页甚至 500 页的核心业务流程与知识产权文档，迅速重塑为结构清晰、步步推进的剧集式数字化微课程体系。 ```mermaid flowchart TD accTitle: TekFlix 核心知识生产与消费流程图 accDescr: 展示从企业原始文档到 Graphit 知识图谱抽取，再到媒体渲染与最终交互平台的端到端流程。 subgraph Ingestion["1. 知识摄取与解析"] A["多模态非结构化资产<br/>(SOP / PDF / 播客 / 视频)"] --> B["Graphit 知识图谱引擎"] end subgraph Structuring["2. 课程构建与媒体合成"] B --> C["本体映射与关联抽取"] C --> D["剧集式微课程架构<br/>(Seasons & Episodes)"] D --> E["AI 媒体生成智能体<br/>(B-roll / 配乐 / 视听合成)"] end subgraph Consumption["3. TekFlix 消费终端"] E --> F["60秒以内可滑动短视频"] F --> G["动态测验 / 术语卡片 / 后台行为分析"] end ``` 这种敏捷培训基础设施的落地对当下的企业而言至关重要。宏观人口结构正在经历剧烈震荡：预计到 2030 年，将有约 7,200 万名资深员工步入退休，而紧随其后补充进来的 Z 世代劳动力接近 6,000 万。虽然生成式 AI 在部分知识型岗位上逐步接替人力，但在诸多实体产业——如管道工程、电气施工、先进制造、建筑营造等硬核技术领域，现场操作人员依然不可或缺。面对这批绝不可能在传统培训教室里连续静坐十小时或两周的新生代劳动力，企业亟须借助 TekFlix 这类高速媒介载体，在知识经验永久流失前，将其无缝过渡给年轻一代。 - **劳动力代际断层数据**：到 2030 年全美约有 7,200 万人退休，将有近 6,000 万 Z 世代员工进入职场。 - **实体工业刚需**：建筑、水暖、电气制造等现场岗位无法完全被 AI 替代，新人必须快速完成沉浸式上岗培训。 - **高阶内容转化速度**：数百页的专有业务文档与 SOP 可在 1 至 2 天内全自动转化为结构化视频课程。 --- ### [04:13](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=253s) 底层引擎 Graphit：破除 RAG 局限的知识图谱与智能体媒体链 当前生成式 AI 领域群雄逐鹿，从 Gemini、Claude 到各类前沿影视级视频工具，大都在争相追逐广告变现与高规格宣传片制作，却留下了巨大的市场真空——“完全没有人致力于构建成体系的企业培训与教学大纲”。许多厂商将那些消费广告的终端员工视作单一客群，却忽视了他们在工作场景中亟待升级的学习需求。Anshar Labs 选择切入这一空白，让 AI 真正扮演“AI 创意总监”（AI creative director）的角色，深耕企业知识转化与教学表达。 TekFlix 实现这一跨越的技术底盘是其专有引擎 Graphit。Pinaki Saha 直言“纯粹的 RAG（检索增强生成）单打独斗必然会失败”，因为盲目的片段检索无法理解复杂的业务逻辑链条。Graphit 作为底层知识图谱引擎，能够全面消化长篇 PDF、手册、播客音频乃至冗长录像，抽取出精确的概念关联、术语词汇及逻辑脉络。系统遵循“先建立认知结构，再进行视听表达”的铁律：在 Graphit 梳理出严谨的教学结构后，第二层媒体智能体才会介入，全自动拼接契合上下文的 B-roll 空镜头、背景音乐与高质量视听素材，最终渲染出兼具严谨性与观赏性的微视频。无论是数据中心纷繁复杂的机房布线与光纤铺设，还是跨厂商工种对接，枯燥的技术细节都能被转化成引人入胜的视听片段。 - **行业痛点定位**：市面上的 AI 视频技术过度向广告与商业片倾斜，缺乏针对严肃知识结构化编排的训练引擎。 - **Graphit 架构优势**：摒弃传统纯 RAG 容易丢失全局上下文的缺陷，依托知识图谱构建完备的本体知识网。 - **全自动视听管线**：实现从结构化内容直接联动分镜生成、B-roll 素材调度与配乐剪辑的一体化自动化生产。 --- ### [06:06](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=366s) 颠覆传统 LMS：端到端闭环与多维互动学习平台 TekFlix 的端到端智能体工作流将“知识抽取、结构生成、媒体激活”融为一体，构建起适配各行各业的标准化生产线。其落地场景极其宽广：既可以面向汽车行业培训终端销售代表，也能运用于数据中心工程培训水暖排线工人，甚至能够解析华尔街投资银行的专业资产包，训练交易员如何面对超高净值人群（HNWIs）精准推介金融产品。无论来源是何种复杂度的资料资产，系统都能以统一的标准将其提炼并转化为影视级的学习资源。 在前端交付体验上，TekFlix 彻底颠覆了迟钝僵化的传统学习管理系统（LMS）。平台提供类似短视频软件的单手滑动（Swipeable reels）体验、课程自动播放预告片（Autoplay trailers）以及沉浸式术语悬浮词典（Glossary popouts）。更为重要的是，平台深度集成了 AI 驱动的动态社交化评测机制——考试问答与认证测验完全由 AI 根据员工当前的实际消费进度实时动态生成，而非事先写死的死板题库。管理后台则配备了高粒度的学习分析看板，培训导师和管理员可以清晰洞察谁在积极学习、谁的进度掉队、员工对知识点的即时回忆水平如何，以及哪些业务环节更容易出现遗忘，从而实现了对传统 LMS 系统的全面升级与替代。 | 功能模块 | 传统企业 LMS 系统 | TekFlix 沉浸式微学习平台 | |:--- |:--- |:--- | | **内容组织形态** | 冗长静态文档、PPT、数十分钟长视频 | Netflix 式剧集体系 + 60秒以内竖屏切片 | | **内容制作周期** | 人工编写制作，耗时数周至数月 | 基于 Graphit 引擎，1~2天内端到端全自动生成 | | **考核与测试机制**| 预设死板选择题库，易抄袭应付 | AI 结合实际内容与学习进度实时动态生成考核 | | **学习行为分析** | 仅记录打卡时间与完播率 | 深度追踪员工记忆召回率、遗忘节点与高频重访数据 | --- ### [07:55](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=475s) 跨越代际的纵深场景拓展与生态合作愿景 TekFlix 的应用空间早已突破了 Gen Z 员工的初级范畴，并在严肃医疗领域展现出极高的社会价值。Anshar Labs 目前正与多家知名医疗机构及癌症专科医院展开深度合作。以化疗患者为例，许多病患离开医院回家后，往往难以完整消化医生递交的厚重指导手册，无法准确掌握复杂的饮食禁忌与康复锻炼要求。TekFlix 将原本晦涩难懂的肿瘤学专业宣教资料，重塑为一目了然、低于 60 秒的滑动式康复微视频。临床数据显示，直观的视觉刺激使得患者对健康指引的记忆留存率和对康复方案的依从性均获得了质的飞跃。 在专业商业服务与实体经济领域，TekFlix 同样加速渗透： - **华尔街交易大厅**：辅导交易员与财富管理人员掌握复杂的合规话术，精炼与高净值客户（HNWIs）的沟通销售技巧。 - **航司安保系统**：针对突发情境与机场地勤安检标准，推行微短化应急处置演练。 - **汽车与房地产**：将庞杂的车型技术参数与地产区位法规快速拆解，赋能一线销售顾问即学即用。 Pinaki Saha 总结道，TekFlix 的愿景是将知识学习重新定义为一个“理解（Understanding）、表达（Expression）与体验（Experience）”完美交融的闭环管道：由知识图谱深刻理解数据内涵，由 AI 视频生成赋予其生动表达，再由微端平台提供极致的学习体验。在演讲尾声，他面向行业各界发出合作倡议，热忱邀请各类企业参与基于自有专有内容的试点（Pilot）落地，并欢迎垂类行业合作伙伴以及具备战略协同价值的投资机构携手同行。 ## Notable quotes > What if your densest knowledge, the webinars, the PDFs, the hour-long trainings, could become a 90-second reel someone actually watches? — Pinaki Saha > Content has exploded, but attention has compressed, and enterprises are losing institutional knowledge to retirement faster than anyone can document it. — Pinaki Saha > Come see how understanding, expression, and experience come together to make knowledge spread like content instead of sitting like documentation. — Pinaki Saha > [00:04](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=4s) swipeable media that's only 60 seconds or less — Pinaki Saha > [04:13](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=253s) 纯粹的 RAG（检索增强生成）单打独斗必然会失败 — Pinaki Saha > [04:13](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=253s) 完全没有人致力于构建成体系的企业培训与教学大纲 — Pinaki Saha > [04:13](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=253s) 先建立认知结构，再进行视听表达 — Pinaki Saha > [07:55](https://www.youtube.com/watch?v=9PO5W3GDP-8&t=475s) 理解（Understanding）、表达（Expression）与体验（Experience） — Pinaki Saha ## Mentioned TekFlix, Anshar Labs, Pinaki Saha, Graphit, TikTok, Netflix, Gemini, Claude, RAG ## Video description Pinaki Saha - Founder & CEO, Anshar Labs What if your densest knowledge, the webinars, the PDFs, the hour-long trainings, could become a 90-second reel someone actually watches? Content has exploded, but attention has compressed, and enterprises are losing institutional knowledge to retirement faster than anyone can document it. I'll introduce TekFlix, the platform I've built that turns unstructured content into cinematic micro-stories, and Graphit, the knowledge graph engine behind it that lets AI understand a story before it can tell one. The talk will walk through how this pipeline plays out in healthcare, enterprise training, and media, where the same system turns oncology webinars into patient education and onboarding manuals into bingeable series. Come see how understanding, expression, and experience come together to make knowledge spread like content instead of sitting like documentation. [Source](https://www.youtube.com/watch?v=9PO5W3GDP-8)

Source quotes

Pinaki Saha: What if your densest knowledge, the webinars, the PDFs, the hour-long trainings, could become a 90-second reel someone actually watches? Pinaki Saha: Content has exploded, but attention has compressed, and enterprises are losing institutional knowledge to retirement faster than anyone can document it. Pinaki Saha: Come see how understanding, expression, and experience come together to make knowledge spread like content instead of sitting like documentation. [0:04] Pinaki Saha: swipeable media that's only 60 seconds or less [4:13] Pinaki Saha: 纯粹的 RAG（检索增强生成）单打独斗必然会失败 [4:13] Pinaki Saha: 完全没有人致力于构建成体系的企业培训与教学大纲 [4:13] Pinaki Saha: 先建立认知结构，再进行视听表达 [7:55] Pinaki Saha: 理解（Understanding）、表达（Expression）与体验（Experience）

Source quotes

Pinaki Saha: What if your densest knowledge, the webinars, the PDFs, the hour-long trainings, could become a 90-second reel someone actually watches? [0:04] Pinaki Saha: Content has exploded, but attention has compressed, and enterprises are losing institutional knowledge to retirement faster than anyone can document it. [0:04] Pinaki Saha: turns unstructured content into cinematic micro-stories [4:13] Pinaki Saha: Graphit, the knowledge graph engine behind it that lets AI understand a story before it can tell one. [7:09] Pinaki Saha: turns oncology webinars into patient education and onboarding manuals into bingeable series. [7:09] Pinaki Saha: Come see how understanding, expression, and experience come together to make knowledge spread like content instead of sitting like documentation. [2:13] Pinaki Saha: By 2030, an estimated 72 million experienced workers will retire, while roughly 60 million Gen Z employees enter the market. [4:13] Pinaki Saha: Standard RAG retrieves fragmented document chunks, failing to map pedagogical dependencies or maintain narrative continuity.

Original transcript

[0:04] My company that I uh own is Anure Labs and uh  we are a holding company who build multiple [0:09] technologies around AI. One of the product that  we launched earlier this year called Techflick. Uh [0:15] the name actually comes from a combination  of Tik Tok and Netflix. Uh the challenge we [0:20] faced uh where this one comes from is a population  that is coming in workforce Gen Z and it all comes [0:27] from learning and coaching and training and as we  all know uh Gen Z tends to learn differently than [0:33] how baby boomers, Gen Xers and even millennials  to certain extent have learned through their [0:37] lives. Uh they don't read uh long books. They  don't read manuals. They don't watch long form [0:44] content. They're extremely extremely focused on  swipeable media that's only 60 seconds or less [0:51] and that's the format they grew up with. So  when they come into workforce today, they [0:56] are also asking for learning content, educational  content or training content in that same format. [1:02] Now tech which is again brings that into context.  So there's a content explosion and there's a as I [1:10] said attention compression because you're only  trying to squeeze everything into less than 60 [1:15] seconds. And when you go to corporate America and  you look at training and you bring that training [1:21] to people who have to learn this process and  there are SOPs, there are PDFs, there are manuals. [1:27] How do you shrink that whole thing into a 40 to  60 second digestible snippet episodic content [1:34] like how exactly Tik Tok moves into something  that is palatable to that taste and format. [1:41] So that's where we introduced tech. Now the  interesting scale issues that we had over there [1:46] is that there's so much content out there for  training. How do you convert them into 60-second [1:52] or less bingeable assets in scale and speed? And  that's where AI came in. And that's where we built [2:01] the Gentic system. And we'll talk about that a  little bit. But the idea was to how to bring that [2:06] kind of content format for learning and educating  in corporate America in the format of Tik Tok. [2:13] So what Netflix does in electrifying speed is that  it brings all the articles like all the SOPs and [2:20] documentation that you have for training gets  it into our knowledge graph that we have built [2:24] with natural language processing and then brings  it into byite-size curriculum kind of looks like [2:30] that. Think about like how Netflix looks like.  So you have seasons and even episodes in Netflix [2:36] but they're long form some degree 45 minutes  or so. What we did is that we built that whole [2:41] structure. We took it from Netflix and then  we made the content of a size of Tik Tok. [2:46] So that's why it's structured like Netflix  episodic built out but it moves like a format [2:52] of 60 seconds or less in Tik Tok and this whole  curriculum is structured and built through agents [2:57] that harnesses that data goes through it brings  ontology ma matches the relationship and creates [3:03] the whole curriculum in flight. So what it  means is that if you bring on let's say 150 [3:08] 200 400 500 pages of documentation of all your  intellectual property of how you run the process [3:14] inside your organization it then takes it  in and within a day or two it converts it [3:19] into that whole curriculum and structured data  from an unstructured content that's what tech does [3:26] so why enterprises need this now 72  million people are retiring by 2030 [3:32] and there's a close to 60 million Gen Z coming  on board. How do you plan to train them? [3:37] How do you plan to keep them in the work? Now,  of course, AI is coming out there and they're [3:41] replacing manpower. We understand all that. But if  you look at people and the interest of industries [3:47] where it's not purely replaceable, trade schools,  I mean trading, I mean like think about plumbing, [3:52] electricals, um manufacturing, construction,  all those parts of the world where it will [3:58] still need manpower to go in there. But there's  a new generation of people who will not sit in a [4:03] kind of a training room for 10 hours or two weeks  and learn all these things. So how do you bring [4:08] them into that speed of content and learning using  a platform like tech that's where the tech comes [4:13] to use for enterprises. So AI as a new creative  partner now we all seeing today seance and um [4:20] Gemini and all kinds of structures that are coming  claw design everybody is putting up content taking [4:26] structure and creating content but nobody's  creating curriculum nobody's creating training [4:31] that's the vacuum that we are seeing that nobody's  addressing it it's all going to cinematic and it's [4:36] all going to production quality videos but for  ad placements and ad revenue and all that stuff [4:41] but not enough attention is being paid into  training side of it because the same users [4:45] who are consuming those ads are also going to  workplace and also needs to be trained. That's [4:50] why we kind of approach that industry and see  that where we can bring in the creative director, [4:55] the AI creative director and make  that work towards training content. [5:00] So the secret sauce behind tech is a graphit.  The graphit is our underlying knowledge graph [5:05] that kind of sucks in all this information creates  a structure from the ontological references [5:10] and then brings the whole architectural side  of episodic and seasons and designs of that. [5:16] So if you think about you have a very boring um  process of building up a whole data center and [5:23] you're bringing off different vendors and these  vendors are putting plumbing and fiber optics and [5:28] cables and all that they all need to be trained.  So how do you train those people from those boring [5:33] journals and SOPs? That's what the creative  media, the graphit builds that whole structure [5:39] where media production comes in. But you cannot  produce media until you have a structured content. [5:44] So we grab all that unstructured content, creates  the curriculum and then we have a second agentic [5:49] system that builds a media to sit on top which  basically brings B-rolls, music, uh production [5:55] quality assets, everything all stitched together  to create this whole training system. So this is [6:01] all about why rag alone fails and graphit solves  it from a standpoint of knowledge graph. So that's [6:06] what we have built in here. Um how graphit works.  It ingests all the content PDFs, manuals, podcast, [6:13] videos, long form videos. It ingests everything.  Then it extracts the whole relationship from that [6:19] that what is tied to what language, what is tied  to what meaningful association of glossery items [6:24] and all that. And then it creates a structure and  then once the structure is created then it creates [6:29] activation through media and it builds all the  assets and everything is done in one workflow. We [6:34] have a pipeline that we have built our proprietary  pipeline that builds the entire asset class across [6:39] all industries. It could be in automotive you're  training salespeople. It could be in data center. [6:45] You're training plumbing. You're training  people in high net worth individuals selling [6:49] assets in uh Wall Street. It can be ingesting any  assets and converted into media level training. [6:57] So the agentic workflow is all about extracting  building the structure and creating the media [7:04] and then putting them all together into a  platform that can be consumed like Tik Tok. [7:09] So tech platform is again swipeable reels,  autoplay trailers, glossery popouts, social [7:15] learning. We have a whole social indexing in there  where we are also giving quizzes and train like [7:20] certification based on answering questions  how much you are consuming. AI provides those [7:25] quizzes. It's not baked in from ahead. So all  that thing is built in and then there's analytics. [7:30] So the analytics provides understanding  from the back end so that the administrator [7:35] or the tutor also understands who's consuming  the content, who's not getting back to it, [7:39] who's performing, who's not performing, who's  recalling assets, who's forgetting more often. [7:44] So that is a more structured learning platform  which is way different from our conventional LMS. [7:50] So in some degree it replaces the conventional  LMS as well. Again designed for Gen Z as I [7:56] mentioned to you. It uh it is a 30% of the  workforce that's going to be there and they [8:00] will be consuming content the way for learning  the way they consume content for entertainment. [8:06] We're already working with healthcare  institutions. We are working with cancer hospitals [8:10] where they are actually taking it beyond genz  where they're also when people have chemotherapy [8:15] they go back home they need to know about their  diets they no need to know how do they need to [8:19] exercise so this kind of media which are again  swipeable less than 60 seconds take all those [8:25] manuals and converts them into media people are  consuming better people are recalling better [8:30] people are also attaching to the regimen so  there's a lot of utilization beyond just Gen [8:34] Z as well uh we also seeing things in Wall Street  we We're working on trading floors. We are working [8:40] with an organization pretty big out there where  in airline security side where we are training [8:45] in the trade desk to learn how to communicate with  high net worth individuals to sell their products. [8:51] And then also in media and creativity we are also  bringing media and in advertisement industry. [8:56] Uh we are very much introducing it into automotive  space in real estate space. So that's where we [9:01] are going after with this media generation and  contextual learning. Again, Techfix we see as the [9:07] future of understanding knowledge uh expressing it  through AI video generation and then experiencing [9:14] through our platform. So it's kind of a whole  suite of products and services that integrated [9:17] together in one uh ability to in one conduit  to teach people how to learn um their work. [9:26] Uh call to action. We'd love to  have people who can partner with us [9:29] uh coming in to partner with us for the content,  create a pilot of our tech platform and then also [9:35] explore strategic investment opportunities. We're  open to that as well and uh collaborate with us [9:40] in specific industry verticals. So I think I'm  right on time. Um I'm more than happy to take [9:45] questions over here. Uh but I believe uh that's  what Techfix is all about. Thank you so much.