Generative AI & Agentic Workflows
Move beyond basic ChatGPT wrappers. AeroCodix designs and deploys custom enterprise Generative AI architectures: private LLM fine-tuning, deterministic Retrieval-Augmented Generation (RAG), vector databases, and autonomous multi-agent workflows.
Transform Unstructured Data into Actionable Autonomous Intelligence
We connect your proprietary PDFs, ERP records, customer emails, and databases into intelligent AI agents that retrieve exact answers, automate operational workflows, and generate accurate reports.
How We Deliver Generative AI & Agentic Workflows
A disciplined, predictable 4-phase agile engineering roadmap with transparent weekly milestones and zero surprises.
Enterprise Knowledge Audit & RAG Blueprint
Auditing source documents (PDFs, Notion, SQL), selecting embedding models, designing vector chunking strategies, and benchmarking accuracy criteria.
- RAG Architectural Specification
- Chunking & Embedding Strategy
- Data Privacy Architecture Plan
- POC Accuracy Benchmark
Vector Pipeline & Agentic Workflow Build
Building automated ingestion pipelines, setting up pgvector/Pinecone indexes, developing LangGraph agent tools, and configuring streaming APIs.
- Automated ETL Vector Ingestion Pipeline
- LangGraph Multi-Agent Engine
- Streaming FastAPI Web Interface
- Semantic Cache Setup
Hallucination Mitigation & Adversarial Testing
Stress testing with 500+ real domain questions, implementing semantic rerankers, and enforcing strict NeMo guardrails against prompt injection.
- 99%+ Accuracy Benchmark Report
- NeMo Guardrails Configuration
- Prompt Injection Penetration Signoff
- UAT Signoff
GPU Cloud Deployment & Telemetry Dashboard
Deploying inference APIs on private AWS/GCP GPU instances with LangSmith/Arize real-time token telemetry and automated evaluation.
- Production GPU Cluster Live
- LangSmith Real-Time Telemetry
- Automated Retraining Script
- 24/7 SLA Support
The Challenges We Solve
Translating complex architectural hurdles into measurable bottom-line business advantage.
LLM Hallucinations on Domain-Specific Queries
Hybrid BM25 + Vector embeddings with Cohere re-ranking and source citation enforcement.
Data Privacy & Regulatory Leakage Risks
Isolated private VPC deployment with self-hosted vLLM inference and zero cloud retention.
High Latency and API Cost Overruns
Semantic caching with Redis Vector and intelligent query routing to smaller quantized models.
Core Deliverables & Specifications
Modular engineering building blocks tailored for high throughput, security, and scalability.
Production RAG Systems
Document chunking, vector embedding pipelines, and semantic re-ranking engines.
Autonomous Multi-Agent Systems
Multi-agent workflows where specialized LLM agents collaborate to solve tasks.
Private LLM Fine-Tuning & Serving
LoRA/QLoRA fine-tuning on proprietary data served via high-throughput vLLM.
Guardrails & Prompt Defense
Automated NeMo guardrails blocking prompt injections and data leaks.
LLM Prompt Engineering & Real-Time Vector Similarity Analytics
Our dedicated engineering pods build with strict architectural rigor. Every component is subjected to automated regression checks, load simulations, and zero-trust security audits to guarantee continuous operational excellence.
The Engineering Stack
We deploy industry-leading technologies vetted for speed, security, and long-term maintainability.
LLM Orchestration
Vector Databases
Model Serving & Fine-Tuning
Telemetry & Guardrails
Frequently Asked Questions
Have questions about engagement models, delivery timelines, or technical specifications?
Will our proprietary company data be used to train public models?
Never. We deploy dedicated private instances where data is stored in your own encrypted vector database and never shared with third-party model training datasets.
What is the difference between RAG and Fine-Tuning?
RAG gives the AI dynamic access to look up live company documents and cite sources accurately. Fine-Tuning teaches the model a specific tone, style, or structured output format. We frequently combine both for maximum accuracy.
Let's Build Something Extraordinary
Tell us about your project roadmap, timeline, or engineering needs. Our technical architects will respond with a tailored proposal within 24 hours.
Our Office
âš¡ Guaranteed Response SLA
Every inquiry is reviewed directly by Usman Ali and our Principal Solutions Architects. You will receive an initial technical feasibility response in under 24 business hours.