Prompt and task layer
Translate business questions into structured prompts, expected outputs, validation checks, and repeatable workflow steps.
AI workflows
AI-assisted analytics for structured, trustworthy business decisions.
This page frames AI as workflow infrastructure: prompt systems, grounded retrieval, SQL support, reporting automation, evaluation, and validation loops.
Featured working project
Jarvis turns AI conversation into a controlled workspace.
The Electron app connects realtime voice and typed requests to selected local files, web search, image generation, notes, visual artifacts, coding handoffs, and opt-in macOS computer control. Session-scoped access and confirmation gates keep the human in charge of sensitive actions.
Realtime voiceTool callingLocal contextHuman approvalReact + Electron
Retrieval and context layer
Use RAG-style patterns where trusted data, documents, SQL outputs, and project context are retrieved before generation.
Agent and orchestration layer
Route longer tasks through background workflows, tool calls, status updates, and human review loops instead of one-shot responses.
Evaluation and guardrail layer
Check generated outputs against source data, schemas, QA rules, and business intent before using them in reporting or planning.
Prompt and instruction files
Reusable prompt files and instruction files keep AI workflows consistent across analysis, documentation, product planning, and review tasks.
Markdown workflow docs
Markdown-based workflow docs can capture operating procedures, task steps, source assumptions, QA notes, and handoff instructions.
MCP-enabled integrations
MCP-style integrations connect agents to tools, data sources, files, and execution environments with clearer boundaries than ad hoc prompting.
Reusable skills
Skills package repeatable patterns: how to inspect a repo, summarize evidence, generate docs, validate links, or prepare deployment checks.
Automation triggers
Automation triggers move AI work from one-off chat into scheduled, event-driven, or workflow-based task execution.
QA and review checkpoints
Review checkpoints keep outputs grounded: verify source data, inspect assumptions, check links, and route sensitive decisions to humans.
Jarvis desktop operator
Voice or typed request -> grounded workspace context -> tool call -> visible artifact -> explicit confirmation for risky actions.Natural language to SQL
Business question -> schema grounding -> SQL draft -> validation query -> dashboard or executive summary.RAG workflow
User question -> retrieve relevant docs/data -> inject source context -> generate answer -> cite/check source assumptions.Ghost-AI orchestration
Prompt -> Trigger.dev task -> OpenRouter model -> structured canvas nodes/edges -> Liveblocks status and presence.BudgetDB insight workflow
Postgres view -> QA check -> dashboard signal -> AI-assisted summary -> human review before decision.