LLM Development
Custom language models engineered for your domain.
We design, fine-tune, and deploy large language models tailored to your industry's vocabulary, workflows, and compliance requirements — moving you beyond generic AI into competitive, proprietary intelligence.
From Generic AI to Your Competitive Edge
Pre-trained foundation models like GPT-4 and LLaMA are remarkable feats of engineering — but they know nothing about your products, your customers, your internal processes, or your industry's regulatory language. A generic model gives you generic output. A domain-adapted model gives you a genuine moat.
At CodeWingz, we treat LLM development as a full-stack engineering discipline. We begin with your data — documents, transcripts, product catalogues, support tickets, knowledge bases — and build a pipeline that transforms that corpus into a fine-tuned or RAG-augmented model that understands your business the way a senior employee does.
We work across the full model spectrum: fine-tuning open-source models (Mistral, LLaMA 3, Falcon) for full ownership and cost control, building RAG pipelines on top of frontier APIs for knowledge-grounded retrieval, and implementing custom embedding strategies for semantic search. Every deployment ships with evaluation harnesses, latency benchmarks, and observability dashboards.
Service Inclusions
Domain Fine-Tuning
Supervised fine-tuning (SFT) and RLHF techniques applied to open-source models using your proprietary data, resulting in a model that speaks your industry's language natively.
RAG Architecture
Retrieval-Augmented Generation pipelines with vector databases (Pinecone, Weaviate, pgvector) that ground every response in your verified knowledge base, eliminating hallucinations.
Low-Latency Inference
Model quantisation (GGUF, AWQ), vLLM deployment, and caching strategies that achieve sub-200ms P95 response times even on self-hosted infrastructure.
Evaluation Pipelines
Automated LLM evaluation with RAGAS, custom metrics, and regression test suites so every model update is benchmarked against production baselines before deployment.
Privacy-First Deployment
On-premise and VPC-hosted deployments for regulated industries. Your training data and inference requests never leave your infrastructure.
Continuous Improvement
Feedback loops that capture real user interactions, flag low-confidence outputs, and feed curated examples into periodic fine-tuning cycles for ongoing model improvement.
A Process Built for Clarity
No black boxes. No surprise invoices. Every project at Codewingz follows a disciplined four-phase process designed to reduce risk and maximise value at every stage.
Discovery & Data Audit
We map your use cases, audit your existing data assets, identify gaps, and produce a model strategy document outlining approach, timeline, and cost projections.
Data Pipeline & Preprocessing
We clean, chunk, deduplicate, and structure your corpus. For RAG systems, we define embedding strategies and build your vector store. For fine-tuning, we prepare instruction-tuning datasets.
Model Training & Evaluation
Fine-tuning runs on A100/H100 GPU clusters with real-time loss monitoring. Automated evaluation against your defined quality benchmarks at each checkpoint.
Inference Optimisation
Quantisation, batching strategies, and caching layers applied to hit your latency and throughput targets. Load testing under simulated production traffic.
Deployment & Integration
Model deployed via REST API on your infrastructure (AWS, GCP, Azure, or on-prem). SDK documentation and integration support for your engineering team.
Monitoring & Retraining
Production observability dashboard, drift detection alerts, and scheduled retraining pipeline. Ongoing support retainer available.
The Tech Stack
We select technologies based on performance, scalability, and long-term maintainability, not trends.
LLaMA 3
Meta's state-of-the-art open source LLM.
Mistral 7B
Highly efficient, high-performance small model.
GPT-4o
OpenAI's most advanced multimodal model.
PyTorch
Industry standard for deep learning research.
Hugging Face
Central hub for models and datasets.
vLLM
High-throughput serving for LLMs.
Pinecone
Managed vector database for RAG.
LangChain
Framework for building LLM applications.
FastAPI
High-performance web framework for Python.
Real-World Impact
FinSecure Analytics
The Challenge
“A mid-market financial analytics firm needed an AI assistant that could answer questions about regulatory filings, compliance documents, and internal policy manuals — without hallucinating numbers or citing non-existent regulations. Generic LLM APIs were producing dangerous inaccuracies in a regulated context.”
The Solution
We built a RAG pipeline indexing 14,000 regulatory documents and internal policy files into a Pinecone vector store, with a fine-tuned LLaMA 3 8B model handling intent classification and response synthesis. Responses are grounded in source citations, and a confidence threshold gates responses to a human reviewer when certainty drops below 85%.
Key Performance Indicators
Common Inquiries
Everything you need to know about our specialized services.
Ready to Build Your Domain-Specific AI?
Tell us your use case and we will map the right LLM architecture — fine-tuning, RAG, or hybrid — for your specific requirements and budget.