Deployment Architecture and Scalability

DAKSH is designed for scalable, secure, and highly modular deployment across diverse environments — from cloud-native infrastructures to edge devices and private data centers. Its serverless-first design philosophy minimizes operational overhead while ensuring elastic resource usage, automated failure recovery, and seamless horizontal scaling. Whether deployed in a Smart City dashboard, an enterprise CRM, or a kiosk-based citizen service portal, DAKSH remains performant, responsive, and resource-efficient.

1. Modular Cloud-Native Architecture

At its core, DAKSH is orchestrated using a microservices-based, event-driven architecture. The system is split into independent functional layers — including input processing, embedding search, inference, speech handling, and response rendering — each of which can be deployed independently or as part of a monolithic flow.

The default deployment stack leverages AWS services, including:

• API Gateway: Entry point for all text/voice queries, with built-in throttling, request validation, and CORS support.

• Lambda Functions: Stateless compute containers handle preprocessing, context assembly, embedding search, and model inference. This ensures scalability without persistent infrastructure.

• S3 Buckets: Store uploaded knowledgebase files and their corresponding processed chunks, with versioning enabled for rollback and audits.

• RDS or DynamoDB: Metadata store for user sessions, audit logs, schema references, and knowledgebase mappings.

• FAISS (via containerized service) or Pinecone: High-performance vector search engine for retrieval-augmented generation.

• CloudWatch and Prometheus: Logging, tracing, and metrics aggregation to monitor latency, usage trends, and performance bottlenecks.

All services are container-friendly, allowing easy deployment via Docker, Kubernetes, or ECS in private or hybrid cloud environments.

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2. Serverless and Auto-Scaling Characteristics

DAKSH leverages serverless compute (AWS Lambda or equivalent) for most components, including:

• Query preprocessing and language detection

• Embedding vectorization

• Retrieval orchestration

• Response generation

• Audit logging and session state handling

Each Lambda function is invoked on demand, scales instantly with concurrent queries, and terminates after task completion — ensuring cost-efficiency and horizontal elasticity. There are no idle resource costs, making this design particularly well-suited for citizen services and seasonal query spikes.

In high-traffic settings, provisioned concurrency and event queues (like AWS SQS or Kafka) are used to prevent cold starts and provide predictable latency.

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3. Deployment Models

DAKSH supports multiple deployment strategies to fit diverse operational contexts:

• Public Cloud (Multi-Tenant): SaaS model where tenants access the platform via subdomains or APIs. Tenant isolation is enforced through scoped IAM and virtual vector indexes.

• Private Cloud (Single-Tenant): For large organizations requiring dedicated resources, DAKSH can be deployed as a fully managed stack in a customer’s AWS/GCP/Azure account.

• Hybrid On-Prem + Cloud: Embedding and vector search operate on-premise while inference runs in the cloud — ideal for compliance-sensitive sectors (e.g., healthcare, defense).

• Edge/Kiosk Deployment: Light-weight variant of DAKSH with offline STT/TTS support and cached Q&A capabilities, suitable for voice kiosks in low-connectivity areas.

Each model includes automated provisioning scripts, CI/CD hooks via GitHub Actions or AWS CodePipeline, and Helm charts for Kubernetes-based clusters.

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4. Scalability Benchmarks

DAKSH has been benchmarked for both latency and throughput under varied deployment modes:

Vector search latency remains under 100ms for ~1M documents using FAISS or Pinecone, and caching accelerates repeated queries or similar intent clusters.

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5. DevOps and Monitoring

DAKSH offers a fully observable deployment experience:

• Grafana dashboards: For real-time traffic, error rates, retrieval time, and TTS latency.

• Prometheus alerts: For anomalous spikes in usage or inference failures.

• Slack/Webhook Integrations: For notifying DevOps teams on deployment errors or usage threshold breaches.

• Versioned Model Registry: Ensures backward-compatible model upgrades and A/B testing.

Automated deployments are supported via:

• GitOps workflows with rollback capability

• Staging and production environment separation

• Canary and blue-green deployment pipelines for zero-downtime model updates