feat: ai automation
AI automation
Eliminate repetitive operations with autonomous, event-driven AI agents and API pipelines.
Start a projectOverview
Modern businesses lose hundreds of hours every month to manual data re-entry, customer triage, report consolidation, and multi-system syncing.
What's included.
- featAutonomous Workflow Agents
- featIntelligent Document Processing (IDP)
- featCustomer Support & Lead Triage Engines
- featData Reconciliation & ETL Pipelines
What you get.
- Custom LangGraph / Temporal workflow orchestrator
- Secure webhook listener & queue management infrastructure (Kafka / RabbitMQ)
- Role-based human-in-the-loop approval dashboard
- Full integration with Slack, CRM (HubSpot/Salesforce), and ERPs
- Comprehensive telemetry, error logging, and retry circuits
- Full source code ownership with CI/CD deployment scripts
Built with
Python FastAPI TypeScript LangChain / LangGraph Temporal Docker PostgreSQL OpenAI / Claude API
Related work.
All workQuestions.
How long does it take to deploy an AI automation pipeline?
Most focused automation workflows (e.g. document extraction or CRM triage) take 2 to 3 weeks from architectural scoping to production deployment. Complex multi-agent systems typically require 4 to 6 weeks.
Is our private business data shared with AI model providers?
No. We enforce enterprise zero-data-retention agreements or deploy self-hosted open-weight models (e.g., Llama 3, Mistral) on private VPC infrastructure so your proprietary data never leaves your perimeter.
How do you handle edge cases and prevent AI hallucinations?
We structure all outputs via strict Pydantic/Zod schemas, cross-reference against deterministic business databases, and establish confidence scoring thresholds where any ambiguous transaction is automatically routed to a human reviewer.
Start with one conversation.
Schedule a technical scoping session to identify your highest-ROI automation opportunities.