Haystack vs LangChain: Which Is Best for Your LLM‑powered Solution?

Haystack vs LangChain is a key comparison for anyone building applications with large language models (LLMs). Haystack and LangChain are both open-source frameworks that help developers harness LLMs for tasks like answering questions, searching documents, or powering chatbots. Each framework has its own strengths and ideal use cases. In this article, we provide an updated, detailed comparison – including new statistics, real-world examples, and evidence – to determine which tool is best for your needs. Both frameworks are popular (LangChain rapidly grew to over 100k GitHub stars since its 2022 launch, and Haystack’s latest version is used by major companies like Apple, Meta, Netflix and more). Below, we break down their features, differences, and when to choose haystack vs langchain for your LLM-powered solution.

Overview of Haystack vs LangChain

Haystack vs LangChain serve different aspects of LLM application development. LangChain is a versatile toolkit for chaining LLM calls with external data and tools, enabling complex agents and workflows. Haystack, on the other hand, focuses on end-to-end retrieval and answer generation pipelines, originally born for search and now revamped for modern LLM integrations. Both frameworks support building retrieval-augmented generation (RAG) systems and conversational AI, but they approach the problem in unique ways. In short, LangChain shines in flexibility and integrations (suited to enterprise AI applications), whereas Haystack excels in simplicity and search-centric tasks (ideal for large-scale search systems and conversational AI bots). Each comes with advantages and disadvantages that we will explore.

What is Haystack?

Haystack is an open-source Python framework from deepset for building AI applications with pipelines centering on retrieval and generation. It is an LLM orchestration system to help connect components (like language models, vector databases, and document stores) into a production-ready pipeline. Haystack originated as a solution for semantic search and question answering over documents, and in 2023 it evolved into Haystack 2.0 to better support composable LLM applications. This new version emphasizes flexibility, easy customization, and enterprise deployment. Haystack’s architecture focuses on the concept of configurable components (for tasks like querying, filtering, and generation) and pipelines that define how data flows through those components. The learning curve is relatively gentle – developers often find the “component + pipeline” model intuitive, aided by excellent documentation.

Key Features of Haystack

Modular Pipeline and Components

Haystack provides a modular pipeline system with built-in components for retrieval, reading, and generation. Developers can plug in retrievers (e.g. BM25, dense vectors) and document stores (Elasticsearch, FAISS, etc.) to build custom search workflows. For example, you might configure Haystack to first retrieve relevant documents and then use a reader model to extract answers – all as part of one pipeline. This explicit pipeline design makes it clear how data moves from one step to the next.

Support for Diverse Data & Models

Haystack is technology-agnostic and lets you choose your embeddings, models, and storage. It integrates with multiple model providers like OpenAI, Hugging Face, Azure, or local models, allowing easy switching of LLM backends. It also introduces new data structures (documents, streams, chat messages) to handle various data modalities (text, images, audio) within the same framework. This means you can use Haystack for multi-modal applications (e.g. an image captioning or audio transcript pipeline) in addition to text-based tasks.

Specialized Components and Nodes

The framework offers specialized nodes for tasks such as document preprocessing, ranking, or prompting. For instance, Haystack 2.0 has dedicated components for embedding generation, document conversion, and result re-ranking. These pre-built pieces save development time and are optimized for their functions. Yet if needed, you can easily extend Haystack by writing a custom component in Python (marked with a simple decorator) to plug into the pipeline – a process developers find very “pythonic” and straightforward.

Production-Ready Scalability

A major focus of Haystack is enterprise scalability and stability. It fits production environments with ease. Haystack supports Kubernetes-native deployments, horizontal scaling, and works smoothly with cloud services for search and storage. It also includes observability hooks for monitoring pipeline performance. In practice, this means Haystack can power large-scale search systems in enterprises.Iindeed, it’s reliable in production of firms like Airbus and Intel for searching vast document collections.

Easy to Use with Great Documentation

Haystack’s user-friendly interface and well-documented processes make it accessible even to beginners. The documentation is famous for being comprehensive and up-to-date. In a side-by-side experience, one engineer was able to complete a proof-of-concept with Haystack in days (after struggling for a week with LangChain) thanks to Haystack’s clear component APIs and docs. This lowers the barrier to entry for new developers. Haystack’s community also runs initiatives like the “Advent of Haystack” to encourage learning and sharing, reflecting a collaborative culture.

What is LangChain?

LangChain has been a powerful framework since late 2022 for developing LLM-powered applications. It is essentially a toolkit to chain together language model interactions with various data sources and external tools. LangChain abstracts many of the lower-level details of prompt handling, model calls, memory, and integration, enabling developers to focus on higher-level application logic. The name “LangChain” reflects its purpose: it helps you compose multiple steps (or “chains”) involving LLMs to create complex behaviors that a single model call can’t handle. For example, LangChain makes it easier to build an AI agent that can consult a database or call an API in the middle of a conversation. It provides the structure to manage these multi-step reasoning processes.

Key Features of LangChain

Extensive Integrations Ecosystem

LangChain is popular for its ecosystem-first approach. It comes with connectors for “almost every” major LLM service, vector database, and third-party tool out there. This includes integrations with OpenAI and Anthropic models, Hugging Face Hub models, popular vector stores like Pinecone, Weaviate, Chroma, FAISS, and many APIs. In fact, LangChain supports 100+ integrations ranging from databases to web scraping utilities. This breadth means you can easily plug LangChain into your existing data stack – whether you need to fetch documents from a CMS, use a specific embedding model, or call external APIs, chances are LangChain already has a module for it. It’s the connective tissue of LLM applications.

Agentic Capabilities (Tools and Actions)

A standout feature of LangChain is its support for building agent systems. LangChain allows LLMs to act as agents that make decisions and invoke tools in an automated loop. It provides standard agent frameworks (like ReAct) where the model can plan actions (e.g. deciding to do a web search) and then execute those via tools. For example, LangChain’s agent system could let you create a travel assistant that calls a flight-search API and then summarizes the results using an LLM. This kind of dynamic tool use is harder to implement from scratch; LangChain makes it much simpler. The framework comes with many pre-built tools and the ability to easily define custom tools. This agentic feature is a key reason LangChain is often popular for complex workflows that go beyond simple question-answering.

Prompt Templates and Memory

LangChain includes robust utilities for prompt management. You can define prompt templates with placeholders and easily feed in context or examples, which helps in optimizing LLM output quality. Moreover, LangChain offers various memory implementations to maintain conversational state. For instance, it can store the dialogue history so the AI remembers previous user inputs during a chat session. These features allow for building conversational AI that feels context-aware and can carry information across turns. The library’s standardized interfaces for prompts, memory, and chains provide consistency and reusability across projects.

Standardized Interfaces and Modules

LangChain provides a suite of modular components (such as LLM wrappers, chains, indexes, vector stores, output parsers, etc.) with standardized interfaces. This design means each part of your application (retrieving data, calling the LLM, parsing outputs) is loosely coupled and swappable. For example, you can switch from an OpenAI GPT-4 model to a local HuggingFace model by just changing the LLM object configuration – the surrounding chain logic remains the same. The uniform interfaces simplify integration with other systems and make LangChain highly extensible.

Multi-Language and Platform Support

While Python is the primary language for LangChain, it also has support for JavaScript/TypeScript (useful for Node.js developers building LLM apps). This makes LangChain accessible to a wider developer audience beyond Python. Additionally, the community has contributed wrappers and examples of LangChain usage in various environments. The cross-language support indicates how LangChain has grown to meet developers where they are.

LangChain’s comprehensive feature set has made it a go-to for many developers prototyping cutting-edge LLM applications. Its rapid growth is also because of an active community producing numerous open-source extensions, tutorials, and fine-tuned integrations. However, with this breadth comes complexity – as we’ll see in the detailed comparison, LangChain can have a steeper learning curve and may introduce more overhead for simpler projects.

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Detailed Comparisons Between LangChain vs Haystack

Now that we’ve introduced each framework, let’s compare LangChain vs Haystack head-to-head across several crucial dimensions. Both LangChain vs Haystack can accomplish similar end goals (for example, you could build a question-answering bot with either), but they differ in design philosophy, ease of use, performance in certain tasks, and suitability for various scenarios.

Learning Curve and Development Speed

Haystack is famous for its simplicity and low learning curve. Its design is less abstract – you deal with concrete components like retrievers, readers, and pipelines, which many developers find intuitive. For instance, one user noted that Haystack was “simple, easy to understand and extend,” allowing his team to implement a custom solution in a couple of days, whereas a LangChain approach had taken over a week. Haystack’s clear documentation and straightforward API contribute to faster initial progress for standard use cases.

In contrast, LangChain, with its many modules and abstractions, can feel overwhelming at first. It offers more flexibility but at the cost of complexity; a developer might need to learn about prompts, chains, agents, and various integration classes before being productive. That said, LangChain’s extensive documentation and examples do help, and once you climb the learning curve, it enables extremely powerful capabilities. In summary, Haystack gets you going quickly for typical pipelines, while LangChain demands more learning upfront but pays off when implementing complex logic.

Features and Flexibility

LangChain clearly leads Haystack in all-around flexibility and features. It has a far broader scope of functionality (memory management, agent tools, advanced prompt templating, etc.) built-in. This makes LangChain suitable for complex, multi-step AI applications beyond just retrieval and answering. You can orchestrate an LLM to use multiple tools and handle different branches of logic with LangChain’s chains/agents – something Haystack alone can’t do. For example, if you need an AI agent to not only answer questions from documents but also perform calculations or trigger external APIs, LangChain provides the scaffolding to do that within one framework.

Haystack is more focused: it excels at what it was built for – retrieval-augmented QA, semantic search, and summarization pipelines. It has limited “agent” capabilities (Haystack recently introduced an Agent node, but it’s relatively basic). In practice, Haystack’s narrower focus can be an advantage when you only require those capabilities. But if your project’s requirements sprawl into areas like managing long conversations or complex decision-making flows, LangChain’s extensive modules might serve you better. Developers have observed that using LangChain for simple use cases can be overkill (“too heavyweight and clunky”), whereas using Haystack for an complex agent use-case might be limiting – it’s about choosing the right tool for the job.

Retrieval-Augmented Generation (RAG) Performance

Both LangChain vs Haystack can help build RAG systems (where an LLM augmentation has a document knowledge base). Recent evaluations suggest that Haystack often has an edge in pure RAG scenarios. In one benchmark test of answering questions using a fixed document set, Haystack outperformed LangChain overall – it achieved a higher average answer similarity score and more consistent results across queries. The study’s author noted that Haystack’s RAG pipeline provided correct or near-correct answers more often, and with less variance, than LangChain’s pipeline.

Moreover, Haystack was easier to work with during this evaluation, citing “drastically better” documentation and simpler debugging. This led to the recommendation that Haystack is a great choice for production RAG systems, due to its reliability and dev experience. LangChain’s RAG implementation was also strong in accuracy (especially due to the complexity of the task), but its higher flexibility came with more things to configure (vector stores, callbacks, etc.). The same evaluation did point out an important caveat: if your RAG needs to integrate with a complex system of agents or tools, LangChain might be more attractive because it can orchestrate those additional steps in the workflow.

In summary, for a straight question-answering chatbot over your documents, Haystack is often the faster path to high performance and stability. If your application requires that plus other interactive abilities (like browsing the web or performing actions), LangChain can incorporate those, albeit with more setup.

Integration and Ecosystem

When it comes to integrating into larger systems or leveraging other services, both frameworks have strengths but in different ways. LangChain, as noted, has an unmatched integration ecosystem – it’s like the Swiss army knife that connects to everything. It can be suitable for variousenvironments (web apps, scripts, etc.) and is frequently updated to support the latest AI services. This makes LangChain very appealing for experimentation and for companies that want to try many providers/tools before committing. Haystack’s integrations are more enterprise-oriented. It natively supports connections to popular databases and vector search engines (such as Elasticsearch, Pinecone, Weaviate) and can work within cloud infrastructure securely.

Haystack has official support for AWS and Azure services (e.g. it can easily use an AWS OpenSearch as a document store, or Azure Cognitive Search, etc.), reflecting its production focus. One analysis put it this way: If you are comparing LangChain vs Haystack, Haystack is the stronger choice for enterprises – it connects seamlessly with AWS, Azure, and GCP and is ready for production at scale. LangChain can also be popular in production, but developers often have to harden it (for example, handle rate limits, caching, custom logging) on their own or rely on community plugins. Haystack, in contrast, comes with more built-in support for things like telemetry, versioning of pipelines, and other features that larger organizations care about. Also, updates to Haystack are more conservative and thorough, whereas LangChain’s rapid evolution sometimes leads to breaking changes.

Use Cases and Strengths

In terms of use cases, there is a bit of an overlap but also clear distinctions in what each framework is best at. Haystack is best-of-breed for building search-oriented applications: FAQ chatbots, enterprise document search engines, knowledge base Q&A, etc. It can replace traditional keyword search with smarter semantic search and QA. For example, companies have used Haystack to index millions of support tickets or research papers and then ask natural language questions over them.

Haystack’s pipeline can combine keyword and vector search, do document ranking, then feed into an LLM for answer generation – all with relative ease. It also shines in summarization tasks where you retrieve documents then summarize (its pipeline can be configured for that pattern). And for conversational AI with foundation in data, Haystack provides a solid backbone (you would handle the dialogue logic and let Haystack handle retrieving relevant info to each user query). 

LangChain, on the other hand, shines for more interactive and dynamic AI agents. If you need a conversational agent that not only answers from a knowledge base but can take actions (like “schedule a meeting” or “open the weather app”), LangChain’s tool integration is invaluable. Likewise, for AI that involves multi-step reasoning (“thinking” through a problem, asking follow-up questions, calling external functions), LangChain is purpose-built. Developers building things like code assistant bots, data analysis agents, or complex workflow automation with LLMs often prefer LangChain because it gives building blocks for managing each step (e.g. chain together a code generation step with a testing step and an error-handling step). LangChain is also useful in ML research or prototyping new LLM capabilities, because it’s easy to swap in new models or chain novel sequences of operations.

Other Considerations (Alternatives and Complementary Tools)

It’s worth noting that LangChain vs Haystack are not the only frameworks in this space, and sometimes they can go together. LlamaIndex (formerly GPT Index) is another open-source library focusing on the data side of RAG – it helps structure and index large datasets so they can be efficiently queried by LLMs. In fact, LlamaIndex can be complementary: one could use LlamaIndex to ingest and index documents, Haystack to retrieve relevant documents, and then LangChain to orchestrate the LLM response – leveraging all three for a sophisticated solution. There are also frameworks like Microsoft’s Semantic Kernel (which targets .NET developers and emphasizes planning and memory) and LangGraph (which provides a more visual, graph-based approach to designing agent workflows). These alternatives each have their niches.

And importantly, Hugging Face is not exactly an alternative framework but rather a provider of models and an ecosystem that LangChain vs Haystack tap into. Hugging Face’s Transformers library provides many of the LLMs and model APIs that you might use within LangChain or Haystack. Recently, Hugging Face introduced its own “Transformers Agent” system to compete with LangChain’s agent capabilities. Early reviews of that Hugging Face agent indicate that it’s heavier and more complex to use, and that LangChain remains the more user-friendly choice for intelligent agent implementations.

In one comparison, LangChain’s description was lighter, more adaptable and better documented, whereas the Hugging Face agent was still in beta and catered more to expert users than beginners. The takeaway is that LangChain vs Haystack currently lead in their respective domains (general LLM orchestration vs. focused RAG pipelines), and other tools either complement them or are still catching up in maturity.

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Is LangChain Better than Haystack?

After comparing Haystack vs Langchain attributes, you might still wonder: which one is better overall? The truth is there is no one-size-fits-all answer – it truly depends on what “better” means for your project. Each framework excels in different dimensions:

Ease of Use vs. Capability

If “better” to you means quicker development, easier understanding, and more stability, then Haystack often has the edge. Developers consistently report that Haystack is simpler and faster for building a standard LLM-powered QA system or search engine. Its opinionated design leads you down a path that just works for those use cases. In production, Haystack’s stability (fewer breaking changes, a clear upgrade path) can also be “better” in terms of maintenance burden. On the other hand, if “better” means more power and flexibility, then LangChain offers more. It has a richer feature set for advanced applications (agents, custom logic, diverse integrations) that Haystack cannot handle as directly. For cutting-edge applications or research prototypes, LangChain’s breadth might make it the better choice.

Enterprise Reliability vs. Innovation

Haystack is ready for enterprise scenarios that demand reliability at scale. It is often the safer bet for a mission-critical system that primarily does retrieval and generation on private data. In fact, some experts conclude that Haystack is the better choice for production-level systems, while LangChain is more suited for projects that require experimentation and exploration. This sentiment reflects that LangChain’s rapid evolution is great for innovation but can introduce hiccups in long-running products, whereas Haystack’s steadiness is valuable in production. Conversely, LangChain’s innovation and huge community mean it has many new capabilities (like new agent types, integration with the latest APIs, etc.) – if your definition of “better” is being on the cutting edge, LangChain leads that charge.

In a direct sense, neither framework is universally better than the other; they are for different goals. An apt summary from one comparison is: “Haystack is the better choice for production-level systems and quick POCs, while LangChain suits projects that require greater flexibility and time to experiment.”. LangChain has more “magic” and versatility, and Haystack has more “pragmatism” and focus. Rather than asking which is better in absolute terms, it’s more useful to ask which is better for your specific use case – which leads to the next section.

When Should You Use LangChain vs Haystack?

Choosing between LangChain vs Haystack comes down to your project’s requirements, scope, and constraints. Here are some guidelines on when to use LangChain or Haystack:

Use LangChain If

• Your application requires complex decision-making or tool use by the AI. LangChain is ideal if you need an agent that might call APIs, use calculators, query databases, or perform multi-step reasoning in genera. For example, building an AI assistant that plans travel itineraries (searching flights, checking weather, booking hotels via APIs) would lean toward LangChain’s agent framework.
• You need maximum flexibility and integration with external systems. LangChain can connect to a vast array of data sources and services out-of-the-box. If your pipeline involves stitching together many components (different model providers, various vector databases, custom functions), LangChain will likely have ready solutions. It’s often the glue in complex AI stacks.
• You want to leverage a large community and rapid iteration. LangChain’s popularity means there are abundant community extensions, tutorials, and support. New features and improvements roll out frequently. If you’re in a fast-moving environment where you’ll be trying cutting-edge LLM techniques (and can handle the maintenance), LangChain offers a rich playground.
• Your focus is on developing an AI with sophisticated dialogue or memory. LangChain makes handling conversation state and context easier, which is crucial for chatbots that need to remember past interactions. Its memory modules and prompt templates will save you time if long conversations or iterative prompting are part of the plan.
• Example scenarios for LangChain: A chatbot that converses with users and also performs actions (like fetching user data from CRM), an AI coding assistant that uses tools to check and run code, a research assistant that aggregates information from multiple APIs and then summarizes findings.

Use Haystack IF

• Your application centers on retrieval-augmented generation (RAG) from your proprietary data (documents, knowledge base, etc.), and you need a robust, production-ready solution. Haystack is for RAG-style question answering and excels at it. If you have a set of documents and you want an AI system to answer questions or create summaries from them reliably, Haystack will get you there with minimal fuss.
• You value simplicity and quick prototyping for search/Q&A use cases. With Haystack, you can stand up an end-to-end pipeline (document store → retriever → LLM) in a matter of minutes using its high-level API. This makes it great for proof-of-concept demos, which can later be directly turned into production pipelines. The framework’s learning curve is friendly, so teams can get productive quickly.
• Stability and maintainability are top concerns. If you’re working in an enterprise setting where long-term support, clear versioning, and low maintenance overhead are important, Haystack is a strong choice. Its updates are incremental and carefully documented, and the core functionality doesn’t shift under your feet. As noted, Haystack’s clear documentation and well-defined components reduce the chance of integration issues down the line.
• Your use case can be handled within Haystack’s scope of features (search, filtering, basic QA, summarization) without requiring complex custom logic. In other words, if you don’t explicitly need the advanced capabilities of LangChain, you might prefer Haystack’s leaner approach. It’s often sufficient for building things like enterprise search portals, FAQ chatbots, or summary generators, where the primary tasks are retrieving relevant info and generating a response.
• You plan to scale up and deploy on cloud infrastructure quickly. Haystack comes ready with enterprise integrations – for instance, hooking Haystack to an existing ElasticSearch cluster or deploying it via Docker/Kubernetes is straightforward.

FAQs

Are Haystacks Still Used Today?

Yes – Haystack is very much in use today, and in fact it’s gaining momentum in the LLM application community. The release of Haystack 2.0 in 2024 revitalized interest in the framework by making it more flexible and LLM-friendly. Many developers who initially tried LangChain have since experimented with Haystack, especially for straightforward RAG use cases, and report positive experiences. Haystack is an actively maintained open-source project (with thousands of commits on its GitHub) and a strong community. It’s also enterprise-proven – as mentioned earlier, companies like Apple, Netflix, NVIDIA, and Meta use Haystack as part of their AI stacks.

This indicates that Haystack is trusted for real-world, production applications in 2025. Far from being obsolete, Haystack has carved out a reputation for reliability in its domain. In online discussions, some users have noted that the initial hype around LangChain has leveled off and that Haystack’s practical approach is appealing for those focused on results. In summary, Haystack is not only used today but is considered a leading framework for LLM applications in categories like enterprise search and retrieval-based QA. Its continued development (with frequent releases) and adoption by industry players underscore its relevance.

Can You Use Haystack vs LangChain Together?

Absolutely. While Haystack vs LangChain are often seen as alternatives, they can be integrated to complement each other’s strengths. Developers have successfully combined them in projects where, for example, Haystack handles the document retrieval and LangChain manages the language generation or agent logic. One way to integrate is to use a Haystack retriever or pipeline as a tool within a LangChain agent. LangChain’s toolkit allows custom tools, so you could wrap a Haystack query such that whenever the LangChain agent needs information, it calls Haystack to get relevant documents.

Conversely, you might use LangChain’s model wrappers (for OpenAI, etc.) inside a Haystack PromptNode, effectively letting Haystack call an LLM via LangChain’s integration laye. This approach could leverage LangChain’s easy access to a variety of LLM providers while still using Haystack’s robust retrieval pipeline. Another example is using LlamaIndex to pre-process data, Haystack to retrieve, and LangChain to orchestrate the final answer – a pipeline that some advanced teams have explored.

However, using both frameworks together does add complexity, so it’s only recommended if you truly need features from both. A straightforward Q&A bot likely doesn’t need a dual framework setup. But if you find that LangChain alone isn’t giving you the retrieval quality you want, or Haystack alone isn’t giving you the agent tools you need, integrating them is feasible. Both being Python-based, they interoperate through function calls and there are community examples demonstrating such hybrids. In short, you can use Haystack and LangChain together, and doing so can yield a very powerful system – just be prepared to handle the complexity of two frameworks in your codebase.

Which Framework, Haystack vs LangChain, is Best for Building RAG Systems?

For building a RAG (Retrieval-Augmented Generation) system, many experts lean towards Haystack as the best suited framework, with some considerations. Haystack was fundamentally designed to enable RAG – it provides all the pieces to store documents, retrieve relevant snippets, and integrate with an LLM to generate answers. Out of the box, it offers various retrievers (keyword, dense vector, hybrid) and has optimized pipelines for search and answer generation. In competitive evaluations of RAG systems, Haystack has come out on top in terms of answer accuracy and consistency, as well as developer experience.

For instance, a detailed comparison found that Haystack’s RAG implementation outperformed LangChain’s, and importantly, was easier to work with and had superior documentation. The author of that comparison explicitly recommended using Haystack in production for RAG due to these advantages. The only noted exception was if your RAG system needs to be part of a larger agent-type system (with complex decision flows), in which case LangChain’s capabilities might be necessary.

LangChain can certainly be used to build RAG systems as well – it has integrations with vector stores and can retrieve and feed context to LLMs effectively. Some users prefer LangChain for RAG if they are already using it for other parts of their application, or if they need to do more custom processing in between (LangChain gives you that granularity). But LangChain doesn’t internally provide a search index; it relies on external vector databases or search APIs. Haystack includes an efficient document store and can even do sparse + dense hybrid search easily, which can simplify a RAG setup. Moreover, Haystack’s focused nature means there’s less to configure for a basic RAG scenario – you don’t have to wire up as many components as you would in LangChain.

Conclusion

The choice between Haystack vs LangChain ultimately depends on your project’s needs. Haystack is a strong candidate for developers and enterprises focused on retrieval-augmented generation (RAG) and reliable, large-scale search systems. LangChain shines for complex agent-driven workflows and conversational AI that requires flexibility and integration with diverse tools. Both frameworks are proven, and in some cases, they can even complement each other to deliver the best results.

At Designveloper, we’ve seen firsthand how crucial it is to select the right foundation for AI projects. As a leading software development company in Vietnam with more than 150+ successful projects delivered globally, we’ve partnered with startups and enterprises alike to build cutting-edge solutions. Our portfolio spans diverse domains—from Lumin PDF (a document management platform with over 70 million users worldwide) to enterprise-grade SaaS products that demand scalability and security.

When clients approach us about integrating LLMs, we don’t just ask “Haystack or LangChain?”—we evaluate the end-to-end requirements: scalability, retrieval precision, conversational depth, and long-term maintainability. Our expertise in AI, web development, mobile apps, and custom software allows us to architect solutions where the right frameworks are matched with the right infrastructure. Whether it’s building a production-ready RAG system with Haystack or designing an intelligent multi-agent workflow with LangChain, we ensure the system fits business goals and technical constraints.