Fixed-scope, senior-delivered engagements with clear timelines, concrete deliverables, and no open-ended consulting bloat.
EventArgs LLC specializes in building internal AI copilots for Microsoft 365 and Azure-heavy environments — not generic chatbots, but source-grounded systems that retrieve answers from your actual internal documentation with full citation traceability. Each engagement is fixed-scope and senior-led, so your team receives production-ready software, not a prototype.
Where teams struggle with documentation sprawl, manual review bottlenecks, or unsafe AI rollouts, we apply three distinct capabilities: source-grounded RAG for enterprise knowledge retrieval, copilot governance & hardening for organizations that need access controls and compliance guardrails, and engineering workflow automation with AI review loops that reduce pull request latency and improve release confidence.
All three offers are built for Microsoft-ecosystem teams — SharePoint, Microsoft 365, Azure DevOps, and Azure-hosted environments — with production RAG backends such as FastAPI and PostgreSQL/pgvector, citation guardrails, and cost governance as non-negotiable requirements.
Deliver a secure internal copilot that returns citation-grounded answers instead of unverifiable summaries.
Audience: Teams buried in SharePoint, Microsoft 365, file shares, local files and fragmented operational knowledge.
Problem: Operational guidelines, runbooks, and product documentations are scattered across SharePoint, Confluence, and team drives, costing developers hours of search lookup latency.
Pricing: Starting at $30,000
Automated ingestion pipeline parsing PDF, markdown, and Word documents safely from SharePoint & Azure files.
Data routing pipelines with user authentication checks to ensure access isolation.
Strict retrieval distance limits and system instructions mapping source citations to every statement.
Infrastructure-as-code scripts and clean staged deployment files for your approved cloud environment.
Handoff notes, observability guidelines, and post-pilot administrative controls.
Audit, secure, and harden internal AI tools with enterprise-grade controls and access boundaries before broadening use.
Audience: Security, compliance, and IT stakeholders responsible for internal AI safety.
Problem: Active AI deployments lack strict cost boundaries, rate-limiting rules, or prompt-injection defense walls.
Pricing: Starting at $10,000
Input validation layers and adversarial prompt screening to prevent system bypasses.
Validating user permission boundaries to prevent data leaks across authorization groups.
Adding preprocessing filters to redact sensitive, identifiable records before sending to LLM hosts.
Deploying token-bucket rate limits, semantic caching, and hard cost alert boundaries.
Setting up automated testing suites to log model outputs and verify regulatory compliance.
Eliminate pull request bottlenecks with custom AI code review pipelines integrated directly into GitHub Actions or Azure DevOps.
Audience: Engineering managers and platform teams with review bottlenecks.
Problem: Senior engineers spend hours reviewing routine syntax, styling, and standard architectural rules, causing PR turnaround delays and delivery drag.
Pricing: Starting at $15,000
Configuring GitHub Actions or Azure Pipelines to capture PR diffs and securely send context to the AI model on every commit.
Tailoring system prompts to enforce your team's exact coding conventions, architectural standards, and security checks.
Connecting your preferred API (Anthropic Claude, OpenAI, Azure OpenAI) or private local models to keep review data secure.
Posting actionable, inline feedback directly in the pull request conversation with suggested code fixes and summaries.
Setting strict rules so AI assists and accelerates the review, while human senior engineers retain final merge approval.
We work best with companies that match specific architectural and business profiles.
Start with a technical feasibility call to align the problem, constraints, and a fixed delivery scope.
Schedule a technical feasibility call