LLM Systems · Applied AI
We build LLM software that works in production.
RAG pipelines, stateful agents, fine-tuning, and robust evaluation suites. Engineered to hard metrics, not demo hype.
Core Capabilities
Production AI requires real software engineering.
Advanced RAG Pipelines
We build high-recall Retrieval-Augmented Generation systems using hybrid lexical and vector search, semantic chunking, and cross-encoder re-ranking. Designed so your LLM answers from your data, not hallucinations.
- Hierarchical & semantic chunking
- Hybrid search (BM25 + Dense Vectors)
- Re-ranking (Cohere, BGE-Reranker)
- Multi-vector retrieval
Agentic Workflows
Multi-agent orchestration for complex, non-deterministic tasks. Using stateful graph flows (LangGraph) with strict schema validation, feedback loops, and human-in-the-loop gates for reliable execution.
- Stateful graph-based flows
- Tool calling & sandboxed execution
- Dynamic planning & correction
- Human-in-the-loop validation
Evals & Guardrails
Evaluation harnesses measuring alignment, accuracy, and security. We implement real-time safety guardrails to prevent prompt injection, data exfiltration, and hallucinations in production.
- Real-time prompt firewalls
- Hallucination & bias tracking
- Automated synthetic test suites
- PII sanitization & masking
Model Fine-Tuning & Hosting
Domain-specific optimization of open-weight models for your use case. We deploy quantized models using high-throughput inference engines for low-latency, cost-efficient serving.
- LoRA & QLoRA fine-tuning
- Quantization (AWQ, GPTQ, GGUF)
- vLLM & ECS Fargate inference
- Serverless LLM cold-start tuning
How We Work
From concept to production in four phases.
01
Discovery & Scoping
We audit your data sources, existing stack, and business outcomes. We define the right model, retrieval strategy, and evaluation criteria before writing a single line of code.
02
Pipeline Architecture
We design the full system — chunking strategy, embedding model, vector store, retrieval flow, and prompt templates — with latency and cost constraints built in from day one.
03
Build & Evaluate
We build in iterations, running evaluation harnesses at every stage. No shipping without measurable accuracy baselines and safety thresholds agreed with your team.
04
Deploy & Monitor
Production deployment with observability (LangSmith, Arize, custom dashboards), auto-scaling inference, and ongoing evaluation drift monitoring.
The Stack
Our preferred building blocks.
Models & Orchestration
- GPT-4o / Claude 3.5 Sonnet
- Llama 3.1 & Mistral (Open weights)
- LangGraph & LangChain
- LlamaIndex & DSPy
Vector & Search DBs
- pgvector (PostgreSQL)
- Qdrant
- Pinecone
- Milvus / Weaviate
Inference & Hosting
- vLLM Engine
- Ollama
- TensorRT-LLM
- Cloudflare Workers AI
Evaluation & Safety
- Ragas Framework
- NeMo Guardrails
- LlamaGuard
- DeepEval / Custom harnesses
Common Questions
Frequently asked about LLM projects.
What LLM services does PrimeByteLabs offer?
We offer end-to-end LLM development: RAG pipeline design, agentic workflow engineering, model fine-tuning (LoRA/QLoRA), AI safety guardrails, and LLM inference hosting using engines like vLLM and TensorRT-LLM.
What is a RAG pipeline and why does my business need one?
A Retrieval-Augmented Generation (RAG) pipeline connects your LLM to your proprietary data — documents, databases, knowledge bases — so it answers accurately from your source of truth, not from general training data.
Do you work with open-source LLMs?
Yes. We work with both commercial models (GPT-4o, Claude) and open-weight models (Llama 3.1, Mistral, Qwen). We help you choose based on cost, latency, privacy, and accuracy requirements.
How long does a production LLM application take to build?
A focused RAG-based assistant can be production-ready in 6-10 weeks. Multi-agent systems with custom fine-tuned models typically take 12-20 weeks depending on data readiness and integration scope.
Ready to evaluate your LLM project?
Estimate scope and costs with our interactive tool, or book a design spike workshop directly with our founders.