Introduction to KonveyN2AI
Have you ever been stuck trying to understand a system where the documentation is incomplete, inconsistent, or missing altogether? Whether it’s legacy COBOL code, Kubernetes manifests, MUMPS procedures, or IRS data layouts, knowledge gaps can grind productivity to a halt. What if we could detect these gaps automatically — surfacing missing links, semantic mismatches, and undocumented rules in real time? That’s the idea behind KonveyN2AI, a project built for the BigQuery AI Hackathon. Powered by BigQuery’s new vector capabilities, it’s a multi-agent architecture that spots knowledge gaps across diverse artifact types — without relying on an external vector database.
The Problem: Hidden Knowledge Gaps
Modern systems often span cloud-native configs like Kubernetes YAML, legacy codebases like COBOL and MUMPS, APIs and schemas like FastAPI, and regulatory layouts like IRS files. Each comes with implicit knowledge. Teams waste hours deciphering these “gaps,” leading to bugs, delays, and frustration. Existing solutions (LLMs + vector DBs) help, but they’re costly, complex, and often slow.
The Idea: KonveyN2AI
KonveyN2AI is designed to:
- Ingest heterogeneous artifacts (code, configs, layouts)
- Chunk and embed them using Google’s text-embedding-004
- Store embeddings directly in BigQuery (using VECTOR columns)
- Search, score, and rank gaps with BigQuery’s native vector functions (VECTOR_SEARCH)
- Surface results via agents orchestrated in a governance-inspired model
How It Works
1. Ingestion & Embedding
Artifacts are parsed, deduplicated, chunked, and hashed for idempotency. Each chunk is embedded (768-dim vector), reduced via PCA (3072 → 768 dims), and cached for efficiency.
2. Vector Search in BigQuery
Instead of paying for and maintaining a separate vector DB, embeddings live inside BigQuery. If BigQuery is temporarily unavailable, KonveyN2AI falls back to an in-memory index.
3. Hybrid Scoring
Deterministic SQL filters combine with AI confidence scores. This hybrid model reduces hallucinations while ranking gaps more reliably.
4. Results & Dashboard
Outputs include heatmaps of similarity scores, latency benchmarks (sub-second search), and an interactive dashboard showing artifact clusters and detected gaps.
Results
KonveyN2AI has been tested with 5+ artifact types, achieving sub-second latency for typical queries and cost savings by eliminating external vector DBs. It also improves reproducibility via caching, error handling, and BigQuery-native storage.
Extended Insights and Use Cases
While KonveyN2AI was born out of a hackathon experiment, its potential goes far beyond proof-of-concept. Let’s dive deeper into the challenges it addresses, and the practical impact it can have in enterprise environments.
1. Why Legacy + Modern Systems Clash
Organizations rarely get the luxury of starting fresh. Legacy systems often coexist with modern microservices. Each generation of technology brings its own terminology, encoding assumptions, and hidden defaults. KonveyN2AI can act as a semantic bridge — surfacing where assumptions misalign before they cause failures.
2. Case Study: Kubernetes + API Drift
Imagine a Kubernetes YAML defining a service on port 8080. The backend FastAPI service, however, listens on port 9090. Such mismatches typically emerge only at runtime, often in staging or production. With KonveyN2AI, both artifacts are chunked + embedded, and the system detects similarity between “service port” and “backend listen port” fields, flagging a semantic gap.
3. Technical Deep Dive
KonveyN2AI stores embeddings in BigQuery, reducing operational overhead and avoiding data silos. Dimensionality reduction via PCA compresses high-dimensional embeddings, retaining ~95% semantic variance. Hybrid scoring combines rules-based filters with LLM scoring to reduce false positives.
4. Future Directions
KonveyN2AI today is a strong proof-of-concept, but here’s where it could evolve: artifact expansion, feedback loops, CI/CD integration, visualization upgrades, and enterprise adoption.
5. Why This Matters
Semantic gaps aren’t trivial — they’re costly. Industry studies estimate that 60–70% of debugging time is spent on misaligned assumptions rather than pure logic errors. Bridging these gaps early not only reduces operational risk but also frees human engineers to focus on innovation, not archaeology.
Conclusion
KonveyN2AI is one attempt to make systems more self-aware — surfacing their hidden assumptions before those assumptions break. By leveraging BigQuery’s vector capabilities and a governance-inspired multi-agent model, KonveyN2AI offers a scalable, cost-effective solution to the age-old problem of knowledge gaps in complex systems.
FAQs
- Q: What is KonveyN2AI?
- A: KonveyN2AI is a project that uses BigQuery’s new vector capabilities to detect knowledge gaps in systems by analyzing diverse artifact types.
- Q: How does KonveyN2AI work?
- A: KonveyN2AI ingests artifacts, embeds them, stores the embeddings in BigQuery, and uses vector search and hybrid scoring to detect and rank gaps.
- Q: What are the benefits of using KonveyN2AI?
- A: KonveyN2AI offers sub-second latency, cost savings, improved reproducibility, and the ability to bridge semantic gaps between legacy and modern systems.
- Q: Can I try KonveyN2AI?
- A: Yes, the project is open on GitHub, and you can clone it, run the pipeline, and explore how BigQuery vector search can help surface hidden knowledge in your systems.
