Source: https://daphnislabs.com/services/llm-app-development # LLM application development Give customers and staff generative AI tools inside the software they already use. [Plan My LLM App](https://daphnislabs.com/contact?service=llm-app-development) [See Deliverables](https://daphnislabs.com/services/llm-app-development#deliverables) Your LLM Application: a user request arrives with your knowledge and business data as context, the application calls the right model and your tools, the result is validated against sources, format and rules, and the validated answer is delivered back to the user. User / Interface Knowledge Business Data LLM Tools / APIs Validation Your LLM Application Input Context Retrieve Generate Validate Deliver ## Relevant technologies - LangChain - ![DeepSpeed](https://daphnislabs.com/images/ai-models/deepspeed.svg) DeepSpeed - ![LangGraph](https://daphnislabs.com/images/ai-models/langgraph.svg) LangGraph - LlamaIndex - Hugging Face - PyTorch - OpenAI [All technologies](https://daphnislabs.com/#technologies-we-use) ## What Can We Build? [Explore AI services](https://daphnislabs.com/services/ai-development) - ### Employee Q&A - ### Writing Copilots - ### Customer AI Features - ### Document Extraction - ### Knowledge Search - ### Recommendations 01 / 06 ## What You Actually Get - **Application Interface** - **Prompt Library** - **Knowledge Index** - **API Adapters** - **Access Rules** - **Evaluation Suite** - **Deployment Package** - **Admin Console** ## LLM Deployment Work We deployed LLMs for Easy Chair and Sinzo. The Sinzo case study below shows the commerce product. [View all case studies](https://daphnislabs.com/case-studies) ![Sinzo demo - the storefront introduction](https://daphnislabs.com/images/projects/sinzo/demo-home-mobile.webp) ### [Sinzo](https://daphnislabs.com/case-studies/sinzo) Supplier-data scraping and AI engineering for a cross-border shopping and warehouse platform. View project ## Example: Turn an Invoice into Records. 1. **Invoice** Uploaded PDF 2. **Fields** Supplier, date, line items 3. **Draft Record** Structured invoice data 4. **Checks** Totals + required fields 5. **Correction** Reviewer resolves a mismatch 6. **Export** Approved accounting record [Try the interactive example](https://daphnislabs.com/services/llm-app-development#llm-workflow) ## What Does It Take to Build? [Get a custom estimate](https://daphnislabs.com/contact?service=llm-app-development) Pilot Production Multi-Team - ### Pilot - One use case - One model - Limited data sources [Get an Estimate](https://daphnislabs.com/contact?service=llm-app-development) - Most popular ### Production - Customer or staff application - Multiple integrations - Production rollout [Plan My LLM App](https://daphnislabs.com/contact?service=llm-app-development) - ### Multi-Team - Multiple workflows - Multiple models - Shared administration - Ongoing engineering [Talk to Us](https://daphnislabs.com/contact?service=llm-app-development) 02 / 03 **Founded in** 2013 **Projects delivered** 550+ **Client countries** 43+ **Global offices** 3 Engineering teams New Delhi · Dubai · Cyberjaya Proof [Case studies](https://daphnislabs.com/case-studies) Stack [Technologies we build with](https://daphnislabs.com/#technologies-we-use) [AI Development](https://daphnislabs.com/services/ai-development) [RAG Development](https://daphnislabs.com/services/rag-development) [AI Evaluation & Guardrails](https://daphnislabs.com/services/ai-evaluation-guardrails) [AI for SaaS](https://daphnislabs.com/solutions/ai-for-saas) ## FAQs ### How does LLM app development differ from model development? Model development focuses on training or adapting the model itself. LLM app development builds a product around a model. This service covers applications and deployment using existing models. ### What is included in the build? The product interface, prompt and model orchestration, a knowledge or retrieval layer where the use case needs one, integrations with your systems, permissions, evaluation and deployment. Scope is agreed per use case before build starts. ### Can you work with our existing systems? Yes. Existing APIs, databases and internal applications are connected through explicit schemas, identity scope and validated read and write boundaries rather than ad-hoc calls. ### Which AI models can you use? Model choice follows the problem. OpenAI, Claude, Gemini and other providers can be used, and the application is built so a model can be changed without rewriting the product around it. ### Will our documents or customer data be retained by a model provider or used for training? That depends on the provider, product, model, deployment and contractual settings. Private documents can be connected through permission-aware retrieval, but that does not by itself prevent a provider from retaining submitted context. Before sending sensitive data, check the selected service terms, training controls, retention periods, deletion options and any feature-specific exceptions. Retention and use for training are separate questions; application logs and connected tools also need review. Tenant and document permissions should limit what each user can retrieve, with the agreed data-handling boundaries recorded before implementation. ### How do you measure answer quality? Through repeatable evaluation scenarios, structured-output checks, grounding and citation checks, failure-mode review and production monitoring - tracked over time rather than judged from a single demo. ### What affects project cost and timeline? Use-case clarity, data readiness and access, integration count, permission and compliance requirements, evaluation depth and the scope of the interface shape delivery. ## What should your product help people do? Bring a user journey or an early prototype. We’ll define the first release. [Plan My LLM App](https://daphnislabs.com/contact?service=llm-app-development) [AI Opportunity Finder](https://daphnislabs.com/ai-opportunity-finder)