AI coding sessions are powerful and fragile. They lose context. They repeat mistakes. They leave no audit trail. SMRTI Suite is the infrastructure layer that fixes that — giving developers transparency, continuity, and control over AI-assisted development.
Every developer who does serious AI-assisted development hits the same walls. New sessions start blank. Context built over hours disappears overnight. Decisions made two sessions ago get repeated or reversed with no memory they were made.
You end up carrying the context yourself — in your head, in scattered notes, in sessions that start with ten minutes of re-briefing. User fatigue compounds.
SMRTI is the infrastructure that eliminates that. Each module solves one layer of the problem.
Each module is independently useful. Together, they give you complete infrastructure for AI-assisted development.
ARIA packages your codebase into a single structured document the AI can fully comprehend in one upload. It walks your source directory, embeds provenance into filenames, and produces a Markdown document with a full table of contents.
Eliminates the root cause of most AI hallucinations on medium and large codebases: incomplete context.
SAKSHI manages session continuity. A structured project_continuity.md is read at the start of every session and updated at the end — giving the next session a complete handover: decisions made, context live, what's next.
The principle: memory needs curation, not accumulation. Stale history is archived, not carried by default.
AXIOM is a structured knowledge graph for your project. Every fact carries a timestamp, confidence score, and verification status. Trust degrades over time — AXIOM makes that visible, surfacing stale context before it misleads.
Inspired by the Sanskrit epistemological principle: you cannot remember what you don't know.
DRISHTI watches AI coding sessions in real time. A three-tier stack: CLI hooks (PreToolUse, PostToolUse, SessionEnd), CCWatcher (conversation JSONL, file accesses), and API Interceptor (full request/response capture). Everything is stored and surfaced through a React dashboard.
Eight stages. One stubborn developer. What it actually takes to make AI-assisted development work.
Small things shipped. Anything bigger collapsed under the weight of context I had to carry myself. The AI reset every session. So did hope, eventually. I didn't quit. I just kept opening new tabs.
Two scripts. Flatten every source file, rename with its parent folder, merge into one Markdown doc with a TOC. Give the AI a complete picture in one upload. Hallucinations dropped. Medium projects started finishing.
VS Code + Claude Code. Short prompts. The AI could read files itself. Structure docs — CLAUDE.md, ARCHITECTURE.md — gave each session a brief. Large projects became possible. But every new session still felt like a first day on the job.
I created project_continuity.md. Read it at the start of every session. Update it at the end. The next morning, a new session picked up exactly where we left off. Quiet satisfaction. The system worked like I designed it to.
A bloated continuity doc became its own noise. Keep it lean. Push stale history to session_history.md. Reference when needed. This single insight later became the philosophical core of AXIOM's provenance model.
A real-time observer for AI coding sessions — API interception, hook telemetry, session intelligence, a full quality pipeline. The project_continuity.md is now on v2.2, session 22+. The architecture held. I couldn't have built this at Stage 2. I wouldn't have known what to build.
This isn't a product pitch. It's an invitation. If you've been in Stage 2 for six months wondering why medium projects keep breaking — you're not doing it wrong. You're just missing infrastructure that doesn't exist yet. That's what we're building.
If your medium projects keep breaking on context, you're not doing it wrong — you're missing infrastructure. Let's talk.