Failure 01
Contextual amnesia
Every AI session starts from zero. Developers re-explain architecture, API schemas, and past bug fixes into prompt windows — quietly eroding the productivity gains AI was supposed to deliver.
Introducing the AI Engineering Brain
AI writes code.Synra remembers.
The persistent, evidence-backed memory layer that gives every AI coding tool a shared understanding of how your company builds software.
Our thesis
Software generation has accelerated faster than software understanding.
For thirty years, engineering governance was pre-code. Requirements were written, reviewed, and approved before a single line was shipped. Generative AI inverted that sequence. Now, code arrives in seconds — and verification is the bottleneck.
Chapter One
Your AI is writing code faster than you can review it.
Failure 02
Review as bottleneck
A 17-file AI-generated pull request forces a human to manually reconstruct dependency chains. Review defaults to skim-reading — which is how silent logic bugs enter production.
Failure 03
Tool fragmentation
Knowledge generated in Cursor is invisible to Claude. What Codex learns, ChatGPT never knows. The company's shared engineering memory fractures across a dozen tools.
The scale of the shift
35%
of pull requests in high-adoption engineering organizations are now AI-authored. GitHub commit volume is projected to grow fourteen-fold.
Chapter Two
One memory graph.
Every AI tool.
synra — evidence card
// Pull Request #482 · refactor(auth)
› AST diff parsed — 17 files, 4 modules affected
› Relevant tests identified — 3/3 passing · test_auth.py
› Semgrep + CodeQL scan — 0 critical · 1 medium
› Architecture impact — no breaking API change
› Memory graph queried — 6 historical decisions surfaced
✓ Security Passport generated — SARIF artifact attached▍
01 · Parsing
AST-first, not text-first.
Tree-sitter parses your codebase into Concrete Syntax Trees. We resolve cross-file types and symbol hierarchies deterministically — never through fragile vector embeddings.
02 · Memory
Persistent property graph.
Architecture, APIs, past PRs, team decisions, incident history — all stored in a queryable graph that deepens with every merged commit. Memory that compounds.
03 · Evidence
SARIF Security Passports.
We never claim code is safe. We prove it. Every AI-generated commit produces a machine-readable audit artifact formatted for SOC 2 compliance conversations.
The Context Brain · Live product
Your memory, live.
A live view of Synra’s project context graph. Explore real memory nodes, filter by source, or add a memory to see the graph refresh.
Loading 3D renderer…
Memory Graph
Connecting to memory service…
Select a node to inspect its source, project, timestamp, and connected memories.
Chapter Three
Four versions. One continuous memory.
Nothing built in V1 is thrown away. Each version deepens the same underlying graph.
01
GitHub Wedge
Months 1 — 3
Zero-retention GitHub App. Parses PR diffs with Tree-sitter, runs Semgrep scans, and posts AST-anchored evidence comments — inside the workflow developers already use.
02
Repository Memory
Months 4 — 6
Persistent property graph stores architecture, APIs, past PRs, and team decisions. Exposed through an open Model Context Protocol (MCP) server — the AI stops forgetting.
03
Multi-AI Brain
Months 7 — 12
Model-agnostic routing between Claude, GPT-4o, and Gemini. AST symbol isolation reduces token consumption by 90%+ on large-repo changes. Model changes. Memory doesn't.
04
Security Passport
Months 13 — 18
SARIF-compliant audit artifacts. Policy validation. Human Trust Layer for enterprise governance. Turning code review from a subjective opinion into an audit trail.
Chapter Four
Evidence, not assumptions.
We never claim code is safe. We show proof. Every AI-generated commit produces a machine-readable Security Passport that auditors can trust.
01Model provenance & prompt hash
02Call-graph blast radius mapping
03Static analysis results — Semgrep + CodeQL
04Policy compliance & test execution proof
security_passport.json
{
"passport_id": "pass_sec_98421a_2026",
"commit_sha": "e3b0c44298fc1c149af...",
"ai_attribution": {
"agent": "Cursor / Claude 3.5",
"timestamp": "2026-03-31T10:14:22Z"
},
"blast_radius": {
"affected_files": 4,
"impacted_symbols": ["authService.verifyToken"],
"graph_depth": 3
},
"verification_evidence": {
"sarif_scan": { "high_severity": 0, "status": "PASSED" },
"codeql_analysis": { "rules_checked": 42, "status": "PASSED" },
"test_suite": { "tests_run": 18, "coverage_delta": "+0.4%" }
},
"policy_compliance": { "owasp_top_10": "VERIFIED" }
}Chapter Five
Simple. Transparent.
Priced per author.
Community
$0
Forever free
- V1 PR Verification
- AST Diff Parsing
- Basic Evidence Cards
- Community Support
Most Popular
Pro
$24
Per active author / month
- Full Evidence Cards
- V2 Repository Memory
- V3 Multi-AI Router (MCP)
- 90%+ Token Reduction
- Standard Retention
Enterprise
$45–65
Per active author / month
- V4 Security Passports
- On-Prem / Private Cloud
- SOC 2 Ready Audit Trail
- SSO & Granular Permissions
- Dedicated Support SLA
Frequently asked
Straight answers.
What exactly is Synra?
Synra is an AI Engineering Brain operating between code repositories and AI coding tools. It maintains a persistent repository memory graph, verifies pull requests using AST-level evidence, and provides a unified context layer across AI coding assistants like Cursor, Claude, and Codex.
Is context amnesia a real enterprise problem?
Yes. Autonomous coding tools generate code rapidly, but human developers spend significant time re-explaining repository architecture, conventions, and past decisions across separate chat sessions. The primary bottleneck has shifted from code generation to code verification and contextual alignment.
How does Synra handle code vulnerability detection?
By orchestrating deterministic static security analysis tools (Semgrep and GitHub CodeQL) locally before querying LLM modules. We never claim code is “100% safe.” Instead, Synra delivers evidence-driven verification backed by static analysis outputs and test execution data, compiled into a SARIF-compliant Security Passport.
What if OpenAI or Anthropic copies this?
Foundational LLM vendors build general reasoning models. Synra builds enterprise-specific structural memory graphs that work across heterogeneous tools. Our persistent, enterprise-specific Code Knowledge Graph deepens with every merged PR — a defensive data moat that neither OpenAI nor Anthropic has any incentive to build inside their own ecosystems.
What if GitHub builds native repository memory?
GitHub owns its own platform, but engineering teams actively avoid single-vendor lock-in. A model-agnostic memory layer that bridges Cursor, Claude, and local CLIs remains valuable even if GitHub ships a native competitor. Memory lock-in compounds; it doesn't reset when a vendor ships a feature.
The founder
Built by an operator who ships.
NABEEL KHAN
Founder & Chief Technologist — Synra OS / Orbits Mind AI
Age 21
Self-taught AI systems builder combining field engineering discipline with software architecture. Architected Synra OS from first principles — a persistent organizational memory layer built around a multi-entity knowledge graph and intent-aware context compaction.
Filed 7 provisional Indian patent claims for SAINAARA AI covering temporal decision mapping, redaction conflict resolution, and permission freshness defense. Shipping production systems on a bootstrapped budget — from architecture to demo to enterprise conversation, end to end.
Previously shipped multiple production MVPs across agent orchestration, retrieval, and workflow automation — from prototype to shipped service.
01 / Synra OS
Founded 2025
Bootstrapped, memory-first architecture
02 / 7 patents
Provisional Indian claims
SAINAARA AI — attorney review
“Truth is one; the wise call it by many names.”
Rig Veda 1.164.46 · Advaita Vedanta
One more thing
Memory is the product.
Everything else feeds it.
Twenty years ago, every company needed a website. Today, every company needs AI. Tomorrow, every company will need memory.
Early access
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