可自动执行复杂工作流程（例如 B2B 销售或工业维护）的企业 AI 代理需要使用大量高质量的特定领域数据训练的模型。对于许多公司来说，创建此类数据是一项主要瓶颈，因为人工标记速度慢且成本高昂，而通用模型可能缺乏必要的细微差别。
InstaLILY AI 是一个面向自主垂直 AI 代理的企业平台，可帮助公司在销售、服务和运营方面实现复杂工作流的自动化和运行。对于其客户 PartsTown，他们需要构建一个实时搜索引擎，以便 AI 代理能够立即将现场服务技术人员与目录中超过 500 万件特定替换零件相匹配。这需要一种可扩缩的方式来生成数百万个高质量的标签，以用于模型训练。
为了解决这个问题，InstaLILY AI 开发了一个多阶段合成数据生成流水线。该流水线采用师生架构，其中 Gemini 2.5 Pro 充当“教师”模型来生成黄金标准训练数据，而微调后的 Gemma 模型充当“学生”模型，以实现可扩缩的低成本生产部署。
InstaLILY AI 设计了一个三阶段流水线，该流水线使用 Gemini 2.5 Pro 的高级推理功能来创建高质量标签，然后将这些知识提炼到更小、更高效的生产模型中。
流水线的工作方式如下：
合成数据生成（教师模型）：Gemini 2.5 Pro 为查询-部分对生成黄金标准标签。为了实现高准确度，InstaLILY AI 使用多视角思维链 (Multi-CoT) 推理，提示模型从多个角度分析零件，包括品牌、类别、规格和复杂的业务逻辑（用于兼容性）。在盲测集中，此方法的判断结果与人类专家的判断结果一致的比例达到了 94%。
学生模型训练：使用 Gemini 2.5 Pro 提供的高质量标签来微调 Gemma-7B。InstaLILY AI 采用了多种技术来优化学生模型，包括直接偏好优化 (DPO)，该技术可将假正例减少 40%。他们还创建了一个由 3 个微调后的 Gemma 变体组成的集成模型，该模型可对每个样本进行投票，从而将标签精确度提高到 96%。
“加入 Google Accelerator 计划后，我们才得以采用这种全新的方法，”InstaLILY 的创始人兼首席执行官 Amit Shah 说道。“Google 提供的实操技术支持、Gemini 和 Gemma 抢先体验机会以及丰厚的 Cloud 赠金，帮助我们在数周（而非数月）内从原型设计阶段过渡到生产阶段。”
多模态和持续学习方面的未来发展
InstaLILY AI 计划通过纳入 Gemini 的多模态功能来扩展其 AI 代理的功能。这样，技术人员就可以上传损坏设备的照片，以便进行诊断。他们还在开发一项持续主动学习服务，该服务会标记低置信度的实时查询，将其路由到 Gemini 进行注释，并每周重新训练生产模型。
InstaLILY AI 针对其 AI 代理打造的搜索引擎取得了巨大成功，这表明教师-学生架构（将 Gemini 2.5 Pro 的推理能力与经过微调的 Gemma 模型的高效性相结合）可以解决复杂的数据生成难题，并实现高性能、可扩缩的 AI 应用。
[[["易于理解","easyToUnderstand","thumb-up"],["解决了我的问题","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["没有我需要的信息","missingTheInformationINeed","thumb-down"],["太复杂/步骤太多","tooComplicatedTooManySteps","thumb-down"],["内容需要更新","outOfDate","thumb-down"],["翻译问题","translationIssue","thumb-down"],["示例/代码问题","samplesCodeIssue","thumb-down"],["其他","otherDown","thumb-down"]],[],[],[],null,["[](/showcase) \nShare\nAUG 29, 2025 \n\nInstaLILY: An agentic enterprise search engine, powered by Gemini \nAmit Shah\n\nCEO \\& Co-Founder, Instalily.ai \nMatt Ridenour\n\nHead of Accelerator \\& Startup Ecosystem USA, Google \n\nEnterprise AI agents that automate complex workflows, like B2B sales or industrial maintenance, require models trained on vast amounts of high-quality, domain-specific data. For many companies, creating this data is a primary bottleneck, as manual labeling is slow and expensive, and generic models can lack the necessary nuance.\n\n\n\u003cbr /\u003e\n\n\n[InstaLILY AI](https://instalily.ai/), an enterprise platform for autonomous and vertical AI agents, helps companies automate and run complex workflows in sales, service and operations. For one of their clients, PartsTown, they needed to build a real-time search engine for AI Agents to instantly match field service technicians with specific replacement parts from a catalog of over five million items. This required a scalable way to generate millions of high-quality labels for model training. \n\n\u003cbr /\u003e\n\n\nTo solve this, InstaLILY AI developed a multi-stage synthetic data generation pipeline. The pipeline uses a teacher-student architecture, with Gemini 2.5 Pro acting as the \"teacher\" model to generate gold-standard training data, and a fine-tuned Gemma model as the \"student\" to enable scalable, low-cost production deployment.\n\nThe challenge of creating specialized training data at scale \n\nThe core of the parts search engine is a relevancy model that connects a service technician's query (e.g., \"compressor for a Northland refrigerator\") to the exact part number. Training this model required a massive dataset of query-part pairs.\n\n\n\u003cbr /\u003e\n\n\nInstaLILY AI faced several challenges with traditional methods:\n\n- **Scalability:** Manually labeling millions of work-order lines was not feasible.\n- **Cost and quality:** Using other frontier models for labeling was three times more expensive and resulted in 15% lower agreement rates compared to their final solution.\n- **Performance:** A live LLM-powered search would be too slow, with initial tests showing two-minute latency, and unable to handle the required 500+ queries per second (QPS) in production.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\nThey needed a system that could cost-effectively generate high-quality data, leading to a fast and accurate final model.\n\n\u003cbr /\u003e\n\nvideo.title\n\nA three-stage pipeline with Gemini and Gemma \n\nInstaLILY AI engineered a three-stage pipeline that uses Gemini 2.5 Pro's advanced reasoning to create high-quality labels and then distills that knowledge into smaller, more efficient models for production.\n\n\n\u003cbr /\u003e\n\n\nThe pipeline works as follows:\n\n- **Synthetic data generation (teacher model):** Gemini 2.5 Pro generates gold-standard labels for query-part pairs. To achieve high accuracy, InstaLILY AI uses multi-perspective chain-of-thought (Multi-CoT) reasoning, prompting the model to analyze parts from multiple angles, including brand, category, specifications, and complex business logic for compatibility. This approach achieved 94% agreement with human experts on a blind test set.\n- **Student model training:** The high-quality labels from Gemini 2.5 Pro are used to fine-tune Gemma-7B. InstaLILY AI used several techniques to optimize the student model, including Direct Preference Optimization (DPO), which reduced false positives by 40%. They also created an ensemble of three fine-tuned Gemma variants that vote on each sample, increasing label precision to 96%.\n- **Production serving:** The knowledge from the Gemma models is distilled into a lightweight BERT model (110M parameters) for the final production environment. This smaller model maintains 89% F1-score accuracy while serving requests at 600 QPS.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n**\"Without LLM's chain‑of‑thought labeling to bootstrap our distilled model, we'd be hand‑tagging an enormous amount of data,\"** said the InstaLILY AI team. **\"Gemini significantly accelerated data preparation and allowed us to reallocate hundreds of engineering hours to higher leverage tasks like fine-tuning and orchestration.\"**\n\n\u003cbr /\u003e\n\nReducing latency by 99.8% and costs by 98.3% \n\nThe teacher-student architecture delivered significant improvements in speed, cost, and accuracy.\n\n\n\u003cbr /\u003e\n\n\nThe final system achieved:\n\n- **Query latency reduction:** From 2 minutes to 0.2 seconds (a 99.8% improvement).\n- **Serving cost reduction:** From $0.12 to $0.002 per 1,000 queries (a 98.3% reduction).\n- **High accuracy:** \\~90% F1-score on a blind hold-out dataset.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\nThe development process was also accelerated. The team built a prototype in 48 hours and a production-ready pipeline in four weeks---a process they estimate would have taken three to four months without the Gemini and Gemma ecosystem. \n\n\"Being part of the [Google Accelerator](https://startup.google.com/programs/accelerator/) unlocked this entire approach,\" said Amit Shah, Founder \\& CEO of InstaLILY. \"The hands-on technical support, early access to Gemini and Gemma, and generous Cloud credits helped us move from prototype to production in weeks---not months.\"\n\n\u003cbr /\u003e\n\nFuture development with multimodal and continuous learning \n\nInstaLILY AI plans to expand the capabilities of its AI agents by incorporating Gemini's multimodal features. This will allow technicians to upload a photo of a broken unit to aid in diagnosis. They are also developing a continuous active-learning service that flags low-confidence live queries, routes them to Gemini for annotation, and retrains the production models weekly.\n\n\n\u003cbr /\u003e\n\n\nThe success of InstaLILY AI's search engine for their AI Agents demonstrates how a teacher-student architecture, combining the reasoning power of Gemini 2.5 Pro with the efficiency of fine-tuned Gemma models, can solve complex data generation challenges and enable high-performance, scalable AI applications.\n\n\n\u003cbr /\u003e\n\n\nTo start building with Gemini and Gemma models, read our [API documentation](https://ai.google.dev/gemini-api/docs). \n\nRelated case studies \n[Passionfroot\nPassionfroot uses AI to help brands run creator marketing campaigns by automating manual tasks and providing tools for seamless collaboration.](/showcase/passionfroot) [Vela Partners\nVela Partners uses Grounding with Google Search for Deeper, Faster Insights](/showcase/vela) [Wolf Games\nWolf Games uses Gemini API to boost content generation accuracy to 96% and slash latency to under 20 seconds for their daily crime stories.](/showcase/wolfgames)"]]
