We build internal knowledge systems that find answers in seconds with traceable source citations. Senior-led implementation with production-ready security, privacy filters, and cost controls.
Full-stack and cloud engineering across .NET, Azure, and Python systems.
Enterprise search architecture with LangChain, LangGraph, MCP, and Azure DevOps pipelines.
Portfolio projects based on deployed internal copilots and engineering workflow tools.
All implementation is done by senior engineers, never delegated offshore.
Fixed-scope, senior-delivered engagements with clear timelines, concrete deliverables, and no open-ended consulting bloat.
Deploy a secure, grounded internal knowledge copilot connected directly to your company knowledge base with traceable source citations.
Audit, secure, and harden AI tools with controls for access, usage, rate limits, privacy, and compliance.
Reduce engineering review latency with custom AI-assisted pull request and workflow automation for Azure DevOps and GitHub.
We work with mid-market teams that need better internal search, safer AI rollout, and less engineering workflow drag.
Your key files are scattered across SharePoint, Confluence, and team channels, making answers slow to find.
You want to deploy LLM-based tools without exposing private data, runaway costs, or unreliable outputs.
Senior engineers lose time to repetitive code review, triage work, and manual handoffs across delivery workflows.
EventArgs works as a senior engineering partner, not a high-level strategy layer.
Collaborate directly with the senior engineers writing your codebase. We eliminate intermediate managers and junior handoffs.
We bypass lengthy assessment slide decks to deliver running software inside your cloud environment through direct, agile iterations.
Your code and documents remain isolated within your network boundaries. We enforce strict data containment, rate-limiting, and cost controls.
We build production-ready systems, not hypothetical prototypes. Here is a look at recent work.
Unstructured operational documentation was scattered across SharePoint and network folders, making search slow. We built a grounded RAG copilot that indexes documentation and enforces traceable citations. This reduced search times by 75% while ensuring answer verifiability.
Read Case StudySingle-prompt AI agents failed on complex workflows due to context limits and state loss. We built a LangGraph-based orchestrator that coordinates specialized, stateful sub-agents. This automated 60% of repetitive task triage while keeping developers in control.
Read Case StudyHow we work with you to move from data constraints to secure, production-ready AI systems.
A 30-minute session to map your internal data sources and detail security and budget requirements.
We design the technical architecture, model selection, proxy gates, and fixed timeline scoping.
Senior engineers build and test custom ingestion pipelines and core retrieval infrastructure.
We deploy cost caching, prompt-injection defenses, and hand over clean source code and tests.
Discuss your environment, constraints, and pilot fit directly with a senior engineer.
Book a discovery call