The AI debate engine that validates answers before they reach you — combining multiple LLMs, real-time web intelligence, and enterprise-grade encryption.
| Category | Details |
|---|---|
| Status | Production Ready — automated CI/CD, hardened dependencies, Dockerized deployment |
| Tech Stack | Python 3.10+, Flask, Terraform |
| Cloud Stack | AWS (S3, RDS, DynamoDB, Glacier, ECS, KMS), Azure (Blob Storage, Key Vault, Cosmos DB, AKS), GCP (GCS, Cloud SQL/Spanner, BigQuery, GKE, KMS) |
| Core Focus | Multi-cloud AI orchestration, secure data flow, scalable low-latency architecture |
| License | CC BY-NC 4.0 — Non-commercial use only without written consent |
Nexus is a security-focused AI engine that aggregates and analyzes responses from multiple AI models plus traditional search engines and media to deliver a comprehensive and nuanced answer to user queries. It pairs live web retrieval and scraping with strict schema guarantees and cryptographic isolation.
Nexus integrates 256-bit AES-GCM encryption for data at rest and in motion (with tenant/instance/user-scoped AAD), and uses response aggregation policies—including model "debate" and consensus—to improve reliability. It’s horizontally scalable and easily extensible: new models and data sources can be onboarded without rewriting the core engine.
Built for developers and researchers yet friendly to end users, Nexus focuses on clear, actionable insights verified against the web.
Nexus was developed by Akshith Konda.
Unlike most AI systems that simply generate output, Nexus:
- Forces multi-model debate to reduce bias and hallucinations.
- Validates answers against real-time, authoritative sources.
- Embeds production-grade security from the first line of code.
- Ships with release-pipeline automation so teams can promote builds confidently.
- Integrates seamlessly into existing enterprise ecosystems without lock-in.
Unchecked AI can:
- Hallucinate or fabricate facts.
- Miss critical real-time updates.
- Expose sensitive information (especially critical in Government Environments).
- Introduce costly operational errors.
Nexus mitigates these risks by:
- Cross-verifying results across multiple models.
- Adding context from live search and web intelligence.
- Applying AES-256 encryption end-to-end.
- Scaling for millions of secure, low-latency requests.
- Multi-Model AI Debate — Truth-prioritized, bias-resistant outputs that converge on a consensus answer with ≥2 independent sources.
- Live Context Injection — Always relevant, always current.
- Encryption by Default — AES-256, KMS integration, TLS 1.3 enforcement.
- Intelligent Ranking — Noise-filtering algorithms to surface the best answer.
- Horizontally Scalable — Operates across AWS, Azure, and GCP without single-region bottlenecks.
- Release automation included — GitHub Actions builds, tests, and signs off artefacts on every change.
- Compliance-Grade Logging — Full audit trails with configurable TTL.
InfraOps Companion is a machine learning & LLM-powered operations manager for real-time infrastructure observability and automated remediation across multi-cloud environments.
- Predictive Anomaly Detection — Anticipates failures before they impact workloads.
- Automated Incident Resolution — Executes pre-defined or LLM-assisted remediation workflows to reduce MTTR.
- Multi-Cloud Operational Dashboards — Unified visibility across AWS, Azure, and GCP resources.
- SIEM Integration Ready — Hooks into enterprise security stacks for compliance and incident correlation.
- Mission-Critical Resilience — Designed for zero-downtime environments and distributed systems.
InfraOps Companion continuously ingests telemetry from distributed systems, applies ML-based anomaly detection and semantic analysis, then triggers auto-remediation playbooks or human-in-the-loop responses. The result:
- Lower operational overhead
- Faster root-cause analysis
- Higher uptime and resilience
- Reduced costs from outages and escalations
LogAnalyzer.AI is an LLM-powered log intelligence platform purpose-built for AI and multi-cloud ecosystems. It transforms raw logs into actionable insights with natural language, machine learning, and anomaly detection.
- Natural Language Queries — Interrogate logs conversationally without complex syntax.
- AI Hallucination & Drift Detection — Monitor and surface unusual model behaviors in real time.
- API Call Pattern Analysis — Spot emerging systemic risks before they propagate.
- Root Cause AI Insights — Accelerate mean time to resolution (MTTR) with automated context and recommendations.
- Verify CI/CD status — The
CIGitHub Actions workflow executesruff,black,mypy,pytest,bandit, andpip-audit. Branch protection should block merges unless the workflow is green. - Tag the stable build —
git tag -a v1.0.0 -m "Stable build: Nexus engine passes CI" && git push origin v1.0.0. - Promote artefacts — Build the multi-stage container with
docker compose buildor via your CI registry publishing job. The providedDockerfileemits a slim, non-root runtime image ready for production orchestrators. - Run load validation — Execute
k6 run tests/load/k6-smoke.jsagainst a staging environment and capture the latency histogram in your release notes.
Automated linting, security scanning, and dependency audits prevent regressions from landing in production. The container build paired with the load test script ensures parity between CI artefacts and deployed workloads while proving the performance envelope before every release.
- Security policy — disclosure process, hardening checklist, and dependency management expectations.
- Operations guide — deployment workflow, health probes, observability hooks, and backup guidance.
- Health monitor runbook — how to respond to failed background snapshots.
- Rate limiter runbook — steps to diagnose 429 storms or Redis exhaustion.
These documents are the canonical source for SRE and security teams preparing a Nexus production rollout.
Design Principles: Safety, Scalability, Security
Infrastructure Highlights:
- Security: AES-256, RBAC, multi-cloud KMS, network segmentation, audit logging.
- Containerization: Isolated microservices for each tool, orchestrated via Kubernetes or ECS/AKS/GKE.
- Storage Strategy:
- Hot: Low-latency object storage, distributed NoSQL.
- Warm: Relational DBs for transactional workloads.
- Cold: Long-term archival in cost-optimized storage.
- Compute: Auto-scaling containerized workloads and VM clusters across all major clouds.
- Observability: Cloud-native metrics, automated backups, tiered storage rollovers.
- No hardcoded secrets — uses
.envor cloud secrets managers. - Data encryption — at rest and in transit.
- Granular API scopes — least-privilege integrations.
- Attack surface reduction — DoS mitigation, HTTPS-only endpoints.
- Controlled log retention — compliance-aligned TTL.
Designed, architected, and implemented end-to-end by a single engineer, showcasing the ability to:
- Deliver multi-cloud, production-grade AI platforms.
- Integrate security-first principles into every layer.
- Build modular systems for scalable enterprise adoption.
# 1. Clone repository
git clone https://github.com/akshithkonda1/nexus-secure-ai-engine.git
cd nexus-secure-ai-engine
# 2. Create a local environment file
cp .env.example .env
# Edit .env with production values (API keys, trusted origins, Redis, DynamoDB, etc.).
# At minimum set:
# AUTHORIZED_API_KEYS=dev-key
# TRUSTED_ORIGINS=https://localhost:8443
# REDIS_URL=redis://localhost:6379/0
# 3. Run the stack locally (Redis + Nexus gateway)
docker compose up --build
# 4. Run the contributor quality gates (optional)
pip install -r requirements-dev.txt
ruff check .
black --check .
pytest
# 5. Execute the load smoke test against a running stack
NEXUS_API_KEY=replace-with-key \
NEXUS_BASE_URL=https://localhost:8443 \
k6 run tests/load/k6-smoke.jsFair-use QoS: "Unlimited" refers to the absence of a fixed monthly request quota. The platform still enforces fair-use rat e/concurrency/token shaping so shared capacity stays healthy.
| Tier | Label | Highlights | Frontier | QoS (rpm/concurrency) |
|---|---|---|---|---|
| Student | Student | Unlimited • Fast/Efficient • EDU UI • Annual verification | ❌ | 20 / 2 |
| Premium | Premium | Unlimited • Fast/Efficient • Standard Priority | ❌ | 30 / 2 |
| Pro | Pro | Unlimited • Priority Lane • Frontier Models • Early Access | ✅ | 120 / 8 |
entitlements.json in nexus/config/ drives these controls; update the file to change concurrency, token allo
wances, and flags consumed by the gateway/frontend.
Two environment variables control the Founders Month experience:
PRICING_MODE=founders_month
FOUNDERS_END=2025-11-18T00:00:00ZSwitch PRICING_MODE to ga_with_trials (or any future launch flag) to revert to the generally available behavior without rede
ploying the stack.
Note: Leave
NEXUS_ALLOW_TEST_FALLBACKSunset in production deployments. Setting it to1enables lightweight HTTP/crypto stubs that exist solely for offline unit tests.
Nexus modules are deployable to any major cloud using Terraform for infrastructure and Kubernetes for orchestration. Below are minimal, production-oriented examples that you can adapt to your environment.
- Terraform ≥ 1.5 installed
- kubectl ≥ 1.27 installed
- Docker or compatible OCI builder
- Cloud CLI configured (one or more):
aws/az/gcloud - A container registry (ECR/ACR/GCR/Artifact Registry) and a repository created
# From the module directory (e.g., ./nexus)
docker build -t <REGISTRY>/<PROJECT>/nexus:<TAG> .
docker push <REGISTRY>/<PROJECT>/nexus:<TAG>
The Nexus chat frontend lives in Frontend/. To interact with it:
- Full app (Vite dev server): Follow the steps in
Frontend/README.mdto install dependencies and runnpm run dev. - Static snapshot: Serve
Frontend/preview.htmlover HTTP (for examplepython3 -m http.server 4173) and openhttp://localhost:4173/preview.htmlin your browser to explore the lazy-loaded UI without building the project.
Both options exercise the same React components, so you can verify layout, drawers, modals, and other surfaces even without network access to install dependencies.