Generative AI Development Services | LLM Integration, RAG & Fine-Tuning
Ship real GenAI features — not slideware demos.
ENQCODE builds production-grade generative AI features on OpenAI, Anthropic Claude, AWS Bedrock, Azure OpenAI, and open-source models (Llama, Mistral, Qwen). RAG pipelines with proper retrieval quality, fine-tuning where it pays for itself, smart caching to control cost, evaluation harnesses to keep quality stable, and clean integration into your existing product.
We integrate the right LLM for the job — Claude, GPT-4o, Gemini, Llama, Mistral — with disciplined prompt engineering, structured outputs (JSON-mode, tool-use), prompt caching, and model routing to balance cost and quality. The output is a feature your users actually trust, not a chatbot bolted onto the side of your product.
Production RAG is hard — most "RAG demos" fall apart on real corpora. We build retrieval pipelines that actually work: chunking strategies tuned to your data, hybrid search (BM25 + dense embeddings + reranking), Pinecone / Weaviate / pgvector / Milvus / Qdrant, citation enforcement, and grounding evaluations so the model answers from your data, not its training set.
Fine-tuning is the right answer when prompt engineering plateaus, when you need a smaller cheaper model to match a larger one, or when you have proprietary data that materially changes outputs. We run LoRA / QLoRA on Llama, Mistral, Qwen; supervised fine-tuning on OpenAI and Bedrock; and DPO/RLAIF for preference alignment — all with rigorous eval-driven iteration.
Embed AI directly into your SaaS, B2B platform, or internal tool: smart compose, summarization, AI-assisted forms, semantic search, document Q&A, AI-powered analytics, and agentic workflows. We design with your product team — not as an isolated experiment — so the feature ships with proper UX, telemetry, and feedback loops.
Generative pipelines beyond text: image generation and editing (DALL-E, Stable Diffusion, Flux), brand-consistent asset generation, voice synthesis (ElevenLabs), video generation (Runway, Sora), and OCR + image understanding workflows for back-office automation.
Every GenAI feature ships with an evaluation harness, golden-set regression tests, hallucination detection, PII redaction, jailbreak resistance, and content safety policies. Quality stays stable as you swap models, prompts, and retrievers — instead of regressing every time you change something.
Understanding Your Vision
Start by understanding your vision, goals, challenges, and what you need from us.
Creating a Plan
After knowing what you need, our experts will offer the best technical approach, considering your specific needs and budget. After considering these things, we’ll provide you with the right solution and plan.
Designing the Look and Feel
We'll create a simple layout to show you the basic structure and flow of your IT solution. Then, our designers will make it visually appealing and easy to use.
Turning Your Plans into Reality
Once you approve the design, our best developers will efficiently bring these plans to life with their best coding skills. To make the best-fit IT solution for your needs.
Testing and Perfecting
We make sure that we provide a smooth and hassle-free solution. What we mean by that is we’ll test everything to ensure it meets our quality standards before deployment.
Ensuring Long-Term Success
To keep your system working, we take full responsibility. We’ll provide ongoing support and updates to keep your system working well while adapting to future demands.
We deliver next-gen software, web, and AI-driven solutions across multiple sectors, helping organizations innovate, automate, and grow digitally.
Client Success Stories & Reviews
Find answers to common questions about our services and processes
Prompt caching on Anthropic and OpenAI cuts input costs by up to 90% on cached portions. Reusing system prompts, tool definitions, and large context blocks across requests is the single biggest cost lever in GenAI today. Most teams ship without it and overpay 3–5x for months.
In RAG systems, swapping a frontier model for an even-bigger one rarely fixes quality issues. Improving chunking, adding a reranker (Cohere, Voyage), or going hybrid (dense + BM25) almost always does. Spend 70% of your eval budget on retrieval, not generation.
JSON mode, tool-use, and Anthropic’s structured-output schemas eliminate an entire class of "the model forgot the format" bugs. Always force structure when downstream code consumes the output. The reliability win is enormous and usually free.
Without an eval set, you cannot tell if a prompt change made things better or worse — only if it feels different. The first deliverable on every GenAI engagement is a real eval set with labeled outcomes, scored automatically. Every prompt, model, and retriever change runs against it before merging.
Committed to Excellence
Got a great idea or a problem to solve? We are all ears! Share your thoughts with us, and we will explore ways to help you win. Let's start a conversation.
We sign NDAs to protect your confidential information.
Quality solutions, on-time delivery, post-launch support.