systems, understanding user intent is key especially in the client service domain where I operate. Yet across enterprise teams, intent recognition often happens in silos, each team constructing bespoke pipelines for various products, from troubleshooting assistants to chatbots and issue triage tools. This redundancy slows innovation and makes scaling a challenge.
Spotting a Pattern in a Tangle of Systems
Across AI workflows, we observed a pattern — lots of projects, although serving different purposes, involved understanding of the user input and classifying them in labels. Each project was tackling it independently with some variations. One system might pair FAISS with MiniLM embeddings and LLM summarization for trending topics, while one other blended keyword search with semantic models. Though effective individually, these pipelines shared underlying components and challenges, which was a major opportunity for consolidation.
We mapped them out and realized all of them boiled right down to the identical essential pattern — clean the input, turn it into embeddings, seek for similar examples, rating the similarity, and assign a label. When you see that, it feels obvious: why rebuild the identical plumbing again and again? Wouldn’t or not it’s higher to create a modular system that different teams could configure for their very own needs without ranging from scratch? That query set us on the trail to what we now call the Unified Intent Recognition Engine (UIRE).
Recognizing that, we saw a chance. Fairly than letting every team construct a one-off solution, we could standardize the core components, things like preprocessing, embedding, and similarity scoring, while leaving enough flexibility for every product team to plug in their very own label sets, business logic, and risk thresholds. That concept became the muse for the UIRE framework.
A Modular Framework Designed for Reuse
At its core, UIRE is a configurable pipeline made up of reusable parts and project-specific plug-ins. The reusable components stay consistent — text preprocessing, embedding models, vector search, and scoring logic. Then, each team can add their very own label sets, routing rules, and risk parameters on top of that.
Here’s what the flow typically looks like:
Input → Preprocessing → Summarization → Embedding → Vector Search → Similarity Scoring → Label Matching → Routing
We organized components this fashion:
- Repeatable Components: Preprocessing steps, summarization (if required), embedding and vector search tools (like MiniLM, SBERT, FAISS, Pinecone), similarity scoring logic, threshold tuning frameworks,.
- Project-Specific Elements: Custom intent labels, training data, business-specific routing rules, confidence thresholds adjusted to risk, and optional LLM summarization decisions.
Here’s a visual to represent this:
The worth of this setup became clear almost immediately. In a single case, we repurposed an existing pipeline for a brand new classification problem and got it up and running in two days. That typically used to take us almost two weeks when constructing from scratch. Having that head start meant we could spend more time improving accuracy, identifying edge cases and experimenting with configurations as a substitute of wiring up infrastructure.
Even higher, this type of design is of course future proof. If a brand new project requires multilingual support, we will drop in a model like Jina-Embeddings-v3. If one other product team wants to categorise images or audio, the identical vector search flow works there too by swapping out the embedding model. The backbone stays the identical.
Turning a Framework right into a Living Repository for Continuous Growth
One other advantage of a unified engine is the potential to construct a shared, living repository. As different teams adopt the framework, their customizations including latest embedding models, threshold configurations, or preprocessing techniques, may be contributed back to a standard library. Over time, this collective intelligence would produce a comprehensive, enterprise-grade toolkit of best practices, accelerating adoption and innovation.
This eliminates a standard struggle of “siloed systems” that prevails in lots of enterprises. Good ideas stay trapped in individual projects. But with shared infrastructure, it becomes far easier to experiment, learn from one another, and steadily improve the general system.
Why This Approach Matters
For giant organizations with multiple ongoing AI initiatives, this type of modular system offers lots of benefits:
- Avoid duplicated engineering work and reduce maintenance overhead
- Speed up prototyping and scaling since teams can mix and match pre-built components
- Let teams give attention to what actually matters — improving accuracy, refining edge cases, and fine-tuning experiences, not rebuilding infrastructure
- Make it simpler to increase into latest languages, business domains, and even data types like images and audio
This modular architecture aligns well with where AI system design is heading. Research from Sung et al. (2023), Puig (2024), and Tang et al. (2023) highlights the worth of embedding-based, reusable pipelines for intent classification. Their work shows that systems built on vector-based workflows are more scalable, adaptable, and easier to take care of than traditional one-off classifiers.
Advanced Features for handling the real-world scenarios
In fact, real-world conversations rarely follow clean, single-intent patterns. People ask messy, layered, sometimes ambiguous questions. That’s where this modular approach really shines, since it makes it easier to layer in advanced handling strategies. You’ll be able to construct these features once, and so they may be reused in other projects.
- Multi-intent detection when a question asks several things directly
- Out-of-scope detection to flag unfamiliar inputs and route them to a human or fallback answer
- Lightweight explainability by retrieving examples of the closest neighbors within the vector space to clarify how a call was made
Features like these help AI systems stay reliable and reduce friction for end-users, whilst products expand into increasingly unpredictable, high-variance environments.
Closing Thoughts
The Unified Intent Recognition Engine is less a packaged product and more a practical strategy for scaling AI intelligently. When developing the concept, we recognized that the projects are unique, are deployed in several environments, and want different levels of customization. By offering pre-built components with tons of flexibility, teams can move faster, avoid redundant work, and deliver smarter, more reliable systems.
In our experience, applications of this setup delivered meaningful results — faster deployment times, less time wasted on redundant infrastructure, and more opportunity to give attention to accuracy and edge cases with lots of potential for future advancements. As AI-powered products proceed to multiply across industries, frameworks like this might change into essential tools for constructing scalable, reliable, and versatile systems.
In regards to the Authors
Shruti Tiwari is an AI product manager at Dell Technologies, where she leads AI initiatives to boost enterprise customer support using generative AI, agentic frameworks, and traditional AI. Her work has been featured in VentureBeat, CMSWire, and Product Led Alliance, and he or she mentors professionals on constructing scalable and responsible AI products.
Vadiraj Kulkarni is an information scientist at Dell Technologies, focused on constructing and deploying multimodal AI solutions for enterprise customer support. His work spans generative AI, agentic AI and traditional AI to enhance support outcomes. His work was published on VentureBeat on applying agentic frameworks in multimodal applications.
References :
- Sung, M., Gung, J., Mansimov, E., Pappas, N., Shu, R., Romeo, S., Zhang, Y., & Castelli, V. (2023). . arXiv preprint arXiv:2305.14827. https://arxiv.org/abs/2305.14827
- Puig, M. (2024). . Medium. https://medium.com/@marc.puig/mastering-intent-classification-with-embeddings-34a4f92b63fb
- Tang, Y.-C., Wang, W.-Y., Yen, A.-Z., & Peng, W.-C. (2023). . arXiv preprint arXiv:2310.09773. https://arxiv.org/abs/2310.09773
- Jina AI GmbH. (2024). . arXiv preprint arXiv:2409.10173. https://arxiv.org/abs/2409.10173
