# DSPy in Production

<p class="dspy-lede">DSPy is in production at <strong>Shopify, Databricks, Dropbox, Microsoft AI, Moody's, AWS, Sephora, VMware</strong>, and dozens more.</p>

This list represents companies that have publicly shared their use cases or have provided permission to be included. It reflects a selection of the many industry applications of DSPy currently in production. To add yours, please [open a PR](https://github.com/stanfordnlp/dspy/edit/main/docs/docs/community/use-cases.md).

<div class="dspy-logo-wall">
  <a href="https://www.shopify.com/" title="Shopify"><img class="logo-shopify" src="../../static/img/logos/shopify-wordmark.svg" alt="Shopify"></a>
  <a href="https://www.databricks.com/" title="Databricks"><img class="logo-databricks" src="../../static/img/logos/databricks-wordmark.svg" alt="Databricks"></a>
  <a href="https://www.dropbox.com/" title="Dropbox"><img class="logo-dropbox" src="../../static/img/logos/dropbox-wordmark.svg" alt="Dropbox"></a>
  <a href="https://microsoft.ai/" title="Microsoft AI"><img class="logo-microsoft-ai" src="../../static/img/logos/microsoft-ai.svg" alt="Microsoft AI"></a>
  <a href="https://replit.com/" title="Replit"><img class="logo-replit" src="../../static/img/logos/replit.svg" alt="Replit"></a>
  <a href="https://aws.amazon.com/" title="AWS"><img class="logo-aws" src="../../static/img/logos/aws.svg" alt="AWS"></a>
</div>

## Featured case studies

<div class="dspy-spotlights">

<div class="dspy-spotlight">
  <div class="dspy-spotlight-logo"><img class="logo-shopify" src="../../static/img/logos/shopify-wordmark.svg" alt="Shopify"></div>
  <div class="dspy-spotlight-metric">~550× cost reduction</div>
  <div class="dspy-spotlight-desc">Scaled DSPy + GEPA for structured metadata extraction across <strong>all</strong> Shopify shops.</div>
  <div class="dspy-spotlight-link"><a href="https://www.youtube.com/watch?v=bxToahwOVpY">Watch the talk →</a></div>
</div>

<div class="dspy-spotlight">
  <div class="dspy-spotlight-logo"><img class="logo-dropbox" src="../../static/img/logos/dropbox-wordmark.svg" alt="Dropbox"></div>
  <div class="dspy-spotlight-metric">Relevance judge, automated</div>
  <div class="dspy-spotlight-desc">Optimized Dash's relevance judge with DSPy for retrieval ranking and offline evaluation.</div>
  <div class="dspy-spotlight-link"><a href="https://dropbox.tech/machine-learning/optimizing-dropbox-dash-relevance-judge-with-dspy">Engineering blog →</a> · <a href="https://www.youtube.com/watch?v=gGGCJWbqHqc">Talk →</a></div>
</div>

</div>

---

## All companies

| **Company** | **Use case** |
|---|---|
| **[Shopify](https://www.shopify.com/)** | Structured metadata extraction across all Shopify shops with DSPy + GEPA; reduced yearly costs ~550×. [Talk](https://www.youtube.com/watch?v=bxToahwOVpY) |
| **[Dropbox](https://www.dropbox.com/)** | Optimized Dash's relevance judge using DSPy across ranking, training data generation, and offline evaluation. [Engineering blog](https://dropbox.tech/machine-learning/optimizing-dropbox-dash-relevance-judge-with-dspy), [Talk](https://www.youtube.com/watch?v=gGGCJWbqHqc) |
| **[Microsoft AI](https://microsoft.ai/)** | Curated pretraining data for MAI-Thinking-1 with DSPy + GEPA, optimizing LLM judge prompts that score web-page quality from ~2,000 human labels. [Paper](https://microsoft.ai/pdf/mai-thinking-1.pdf), [Blog](https://haroldbenoit.com/blog/ode_to_gepa/) |
| **[AWS](https://aws.amazon.com/)** | Migrates prompts from larger to smaller models on Amazon Nova while maintaining performance. [Blog](https://aws.amazon.com/blogs/machine-learning/improve-amazon-nova-migration-performance-with-data-aware-prompt-optimization/) |
| **[JetBlue](https://www.jetblue.com/)** | Multiple chatbot use cases. [Blog](https://www.databricks.com/blog/optimizing-databricks-llm-pipelines-dspy) |
| **[Replit](https://replit.com/)** | Synthesize diffs using code LLMs using a DSPy pipeline. [Blog](https://blog.replit.com/code-repair) |
| **[Databricks](https://www.databricks.com/)** | Research, products, and customer solutions around LM judges, RAG, classification, and other applications. [Blog](https://www.databricks.com/blog/dspy-databricks) |
| **[DDI](https://www.ddiworld.com/)** | Leadership coaching agents built on Databricks with DSPy. [Customer Story](https://www.databricks.com/customers/ddi) |
| **[Sephora](https://www.sephora.com/)** | Undisclosed agent usecases; perspectives shared in [DAIS Session](https://www.youtube.com/watch?v=D2HurSldDkE). |
| **[Zoro UK](https://www.zoro.co.uk/)** | E-commerce applications around structured shopping. [Portkey Session](https://www.youtube.com/watch?v=_vGKSc1tekE) |
| **[VMware](https://www.vmware.com/)** | RAG and other prompt optimization applications. [Interview in The Register.](https://www.theregister.com/2024/02/22/prompt_engineering_ai_models/) [Business Insider.](https://www.businessinsider.com/chaptgpt-large-language-model-ai-prompt-engineering-automated-optimizer-2024-3) |
| **[Nous Research](https://nousresearch.com/)** | Evolutionary self-improvement for the Hermes agent: optimizes skills, prompts, and code using DSPy + GEPA. [GitHub](https://github.com/NousResearch/hermes-agent-self-evolution) |
| **[Haize Labs](https://www.haizelabs.com/)** | Automated red-teaming for LLMs. [Blog](https://blog.haizelabs.com/posts/dspy/) |
| **[Plastic Labs](https://www.plasticlabs.ai/)** | R&D pipelines for Honcho. [Blog](https://blog.plasticlabs.ai/blog/User-State-is-State-of-the-Art) |
| **[PingCAP](https://pingcap.com/)** | Building a knowledge graph. [Article](https://www.pingcap.com/article/building-a-graphrag-from-wikipedia-page-using-dspy-openai-and-tidb-vector-database/) |
| **[Salomatic](https://langtrace.ai/blog/case-study-how-salomatic-used-langtrace-to-build-a-reliable-medical-report-generation-system)** | Enriching medical reports using DSPy. [Blog](https://langtrace.ai/blog/case-study-how-salomatic-used-langtrace-to-build-a-reliable-medical-report-generation-system) |
| **[Truelaw](https://www.youtube.com/watch?v=O0F3RAWZNfM)** | How Truelaw builds bespoke LLM pipelines for law firms using DSPy. [Podcast](https://www.youtube.com/watch?v=O0F3RAWZNfM) |
| **[STChealth](https://stchealth.com/)** | Using DSPy for entity resolution including human-readable rationale for decisions. |
| **[Moody's](https://www.moodys.com/)** | Leveraging DSPy to optimize RAG systems, LLM-as-a-Judge, and agentic systems for financial workflows. |
| **[Normal Computing](https://www.normalcomputing.com/)** | Translate specs from chip companies from English to intermediate formal languages |
| **[Procure.FYI](https://www.procure.fyi/)** | Process messy, publicly available technology spending and pricing data via DSPy. |
| **[RadiantLogic](https://www.radiantlogic.com/)** | AI Data Assistant. DSPy is used for the agent that routes the query, the context extraction module, the text-to-sql conversion engine, and the table summarization module. |
| **[Raia](https://raiahealth.com/)** | Using DSPy for AI-powered Personal Healthcare Agents. |
| **[Hyperlint](https://hyperlint.com)** | Uses DSPy to generate technical documentation. DSPy helps to fetch relevant information and synthesize that into tutorials. |
| **[Starops](https://staropshq.com/) & [Saya](https://heysaya.ai/)** | Building research documents given a user's corpus. Generate prompts to create more articles from example articles. |
| **[Tessel AI](https://tesselai.com/)** | Enhancing human-machine interaction with data use cases. |
| **[Dicer.ai](https://dicer.ai/)** | Uses DSPy for marketing AI to get the most from their paid ads. |
| **[Howie](https://howie.ai)** | Using DSPy to automate meeting scheduling through email. |
| **[Isoform.ai](https://isoform.ai)** | Building custom integrations using DSPy. |
| **[Trampoline AI](https://trampoline.ai)** | Uses DSPy to power their data-augmentation and LM pipelines. |
| **[Pretrain](https://pretrain.com)** | Uses DSPy to automatically optimize AI performance towards user-defined tasks based on uploaded examples. |
| **[Spindle AI](https://spindle.ai)** | Turns natural-language constrained optimization problems into solvable mathematical programs whose candidate solutions are scenarios. |
| **[Infinitus](https://www.infinitus.ai/product/ai-agents/)** | Leverages DSPy to build and optimize healthcare AI agents |
