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.
Discovery
When you come to us with your idea, we start by analyzing your requirements, goals, and the feasibility of the project. This step helps both sides: you polish your idea and clarify demands while we understand your business and estimate the project’s timeline and costs.
Data Exploration
Our AI engineers run exploratory data analysis on your existing datasets and infrastructure to confirm there is enough signal for the project. We clean the data, identify gaps, and design how to use it as efficiently as possible before any model work begins.
Development
For larger projects we start with a small-scale prototype to validate that the final model will work in practice. Once the concept is proven we move to full-scale development in two-week sprints with daily updates and a weekly call to share progress.
Testing & QA
Our QA specialists run a wide range of tests — accuracy, latency, edge cases, prompt-injection and hallucination checks for LLM-based features — to make sure the final model behaves reliably. Anything that needs fixing gets resolved before deployment.
Deployment
We integrate the AI solution into your infrastructure with proper observability, cost monitoring, and guardrails. You start using it in production and can immediately measure the impact on the parts of your business it touches.
Monitoring & Support
AI models drift as data shifts, so deployment is not the end. ENQCODE provides ongoing monitoring for accuracy, cost, latency, and safety — plus retraining and updates as needed — so the system keeps performing long after launch.
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.
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Quality solutions, on-time delivery, post-launch support.