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Production-Grade AI Agents, LLM Fine-Tuning & Autonomous Decision Engines
Deploying intelligent agent workflows, fine-tuned LLMs, Retrieval-Augmented Generation (RAG), and computer vision pipelines that automate real business operations.
Every system crafted at CodingBurg adheres to rigorous architectural invariants designed for high concurrency, multi-decade maintainability, and zero unexpected downtime.
Engineered with strict domain-driven boundaries, hexagonal ports, and event-driven decoupling. Your core business logic remains pristine and testable across decades of technology shifts.
Optimized database execution paths, intelligent distributed caching hierarchies, edge compute, and async worker queues guaranteeing rapid time-to-first-byte and low round-trip latencies.
End-to-end cryptographic verification, role-based authorization, automated secret rotation, strict schema validation, and continuous vulnerability scanning embedded in every PR.
Direct engineering competencies integrated into this service tier to guarantee end-to-end technical excellence.
Autonomous multi-agent orchestration systems
High-precision RAG with vector embeddings and hybrid search
Custom fine-tuning of open-source models (Llama, Mistral)
Low-latency model serving with vLLM and TensorRT-LLM
Guardrails, hallucination prevention, and security sandboxing
Tangible, audited source code repositories, automated pipelines, and operational assets handed over to your team.
Production-ready AI microservice with streaming API responses
Vector database integration (pgvector, Pinecone, Qdrant)
Prompt evaluation framework with regression benchmarking
Model monitoring dashboard for latency, token costs, and accuracy
Validated for extreme performance, security, and developer ergonomics
From whiteboard architecture to battle-hardened cloud production, our agile sprint cadence delivers visible software every two weeks.
We interview stakeholders, identify domain boundary invariants, model data entities, and produce Architecture Decision Records (ADRs).
High-fidelity wireframes, interactive API prototypes, and baseline benchmark environments are stood up in sandbox cloud clusters.
Bi-weekly demo releases, automated CI/CD unit and integration test suites, and transparent telemetry tracking real-time velocity.
Penetration testing, load testing at peak volumes, blue-green deployment cutover, and 24/7 telemetry monitoring handover.
Detailed technical breakdown of design trade-offs, protocols, and data models implemented under this engineering practice.
Moving generative AI and machine learning from proof-of-concept demos to mission-critical production requires rigorous software engineering, deterministic guardrails, and cost management.
Naive vector similarity search fails when queries involve structured constraints or domain jargon. We build hybrid RAG pipelines: - **BM25 + Dense Embeddings**: Combining lexical keyword matching with deep semantic vector search via Reciprocal Rank Fusion (RRF). - **Contextual Chunking**: Preserving document hierarchy and metadata to eliminate retrieval blindness. - **Reranking Models**: Utilizing cross-encoders to ensure only top relevant context enters the LLM context window.
LLM outputs must conform strictly to typed schemas to integrate with relational databases and API consumers. We implement JSON Schema enforcement and rigorous validation to eliminate hallucinations and schema corruption.
An exhaustive technical blueprint documenting enterprise RAG pipelines, dense vector embeddings, hybrid semantic search, LLM fine-tuning, autonomous multi-agent orchestration, and private GPU cluster deployments.
Off-the-shelf Large Language Models (LLMs) hallucinate facts, lack knowledge of private corporate documents, and present serious intellectual property compliance risks when queried through public endpoints. CodingBurg engineers Deterministic Enterprise RAG Systems that ground language models in verified corporate knowledge bases with sub-second retrieval latencies.
This dossier is indexed and structured around primary commercial and technical intent keywords:
All architectural blueprints and implementation standards documented in this dossier represent live production benchmarks deployed across CodingBurg’s enterprise customer portfolio.
Schedule Architecture ReviewComprehensive architectural blueprint of multi-stage RAG pipelines: contextual chunking, dense vector embeddings, hybrid BM25 + vector search, and cross-encoder reranking.
Off-the-shelf Large Language Models (LLMs) hallucinate facts, lack knowledge of private corporate documents, and present serious intellectual property compliance risks when queried through public endpoints. CodingBurg engineers Deterministic Enterprise RAG Systems that ground language models in verified corporate knowledge bases with sub-second retrieval latencies.
Custom domain fine-tuning of open-weight models (Llama 3, Mistral, Qwen) using QLoRA, synthetic dataset curation, and 4-bit/8-bit quantization.
While prompt engineering and RAG provide factual grounding, complex domain reasoning, brand voice alignment, and structured JSON formatting require specialized weights. CodingBurg builds Automated Model Fine-Tuning & Quantization Pipelines for open-weight models, enabling enterprises to own sovereign AI assets without recurring vendor API costs.
A and B, rank r=16 to r=64) are attached to key attention projection layers (q_proj, v_proj, k_proj). Only 0.1% to 1% of the total parameter count is actively updated during backpropagation.Designing collaborative agent networks using LangGraph, CrewAI, AutoGen, state machines, tool calling protocols, and human-in-the-loop validation.
Simple single-shot prompts are insufficient for complex enterprise operations that require research, planning, code generation, validation, and multi-step execution. CodingBurg engineers Autonomous Multi-Agent Ecosystems powered by graph-based state machines.
ResearcherAgent, ArchitectAgent, ValidationAgent, CodeGeneratorAgent) and edges define conditional routing logic based on output validation.Deploying scalable AI inference infrastructure: vLLM, Triton Inference Server, NVIDIA GPU autoscaling, prompt caching, and Langfuse telemetry.
Deploying AI models to production requires rigorous systems engineering to control GPU costs, manage queue concurrency, and track prompt drift. CodingBurg designs enterprise MLOps pipelines that maximize GPU utilization and provide complete observability.
Clear answers regarding intellectual property, governance, and delivery guarantees.
You retain 100% full intellectual property ownership. Upon project delivery and milestone fulfillment, all repository rights, architectural schematics, deployment scripts, and credentials transfer directly to your organization with zero vendor lock-in.
We sign standard mutual Non-Disclosure Agreements (NDAs) prior to examining your proprietary workflows, data schemas, or business requirements. All development occurs within dedicated encrypted environments.
Yes. Our agile 2-week sprint cadence incorporates backlog grooming and sprint reviews, allowing your product leadership to reprioritize features based on customer feedback and live market validation.
We offer tiered Service Level Agreements (SLAs) ranging from 99.99% uptime monitoring and automated patching to continuous feature iteration and dedicated DevOps on-call engineering.
Architecting resilient, distributed enterprise systems tailored to complex domain logic, uncompromising security protocols, and mission-critical performance requirements.
Building lightning-fast, highly responsive web platforms using Next.js, React, and modern edge infrastructure that drive conversion and captivate users.
Crafting native iOS and Android experiences with Flutter and React Native that deliver 60fps animations, offline-first reliability, and seamless device integration.
Connect with our principal architects. We conduct a structured review of your technical constraints, domain objectives, and deliver an actionable execution blueprint.