DataDack

Consumer automation tools treat LLM calls, API webhooks, and hardware telemetry as three separate problems. DataDack treats them as one. The core workflow is a visual graph editor where you drag AI agents, REST endpoints, database connectors, and MQTT IoT streams onto a canvas and wire them together — the engine executes each step deterministically, logs every result, and the vendor describes execution as cryptographically signed. Supported connectors include OpenAI, Anthropic, Kafka, PostgreSQL, Redis, MongoDB, Stripe, Salesforce, Datadog, InfluxDB, Twilio, Slack, AWS Lambda, and gRPC, among others.

The clearest differentiator is the AI Gateway with regional routing. DataDack proxies all LLM inference through local nodes — Mumbai and Hyderabad for India, EU nodes for GDPR compliance — with mTLS encryption across every boundary and a zero-log mode that routes prompts straight to VRAM without touching the vendor’s database. For teams operating under India’s DPDP framework or building supply-chain and fintech applications where prompts contain sensitive data, this is the architecture decision that removes the compliance conversation entirely.

The platform fits teams that need IoT telemetry feeding directly into agent logic — factory vibration sensors triggering repair tickets, freight disruption detected and rerouted by agent swarms, compliance PDFs reconciled against live Stripe ledgers. The visual canvas works well for linear and moderately branched pipelines. When your workflow logic becomes deeply conditional — branching on what an agent returned three steps back, with parallel agent paths that rejoin — the canvas model reaches a ceiling that the vendor’s documentation does not fully address. Teams hitting that ceiling add custom logic layers, at which point they are effectively maintaining two systems.