Systems we've shipped

A production-grade conversational automation platform on WhatsApp—routing customer intent, executing multi-step workflows, and escalating to humans with full context.

Webnetis implemented a LangGraph-orchestrated agent layer on top of WhatsApp Business API, CRM, and knowledge bases. Customers receive instant, policy-aligned responses while complex cases are handed off with transcripts, entities, and recommended next actions.

Scope & delivery

• · WhatsApp template flows, session management, and opt-in compliance for regional messaging rules
• · LangGraph state machines for intent detection, tool calls, RAG retrieval, and human-in-the-loop approval
• · Integration with ticketing, payments status APIs, and supervisor dashboards for live monitoring

Impact

• · Higher self-serve resolution on routine inquiries without sacrificing compliance
• · Reduced average handle time when chats escalate to live agents
• · Auditable conversation traces for quality and regulatory review

Stack

LangGraph, Python, FastAPI, WhatsApp Cloud API, Redis, PostgreSQL, OpenAI / Azure OpenAI

Deployment

AWS / Azure

Status

Live

An advanced multi-agent automation hub that coordinates research, validation, and system updates across ERP, HRIS, and internal APIs.

AutoGraph Nexus uses LangGraph to decompose enterprise requests into specialized agents—data retrieval, policy check, action execution, and verification—so automation stays controllable at production scale.

Scope & delivery

• · Supervisor agent with explicit checkpoints before writes to production systems
• · Tool registry for SAP, ServiceNow, SharePoint, and custom REST services
• · Observability with step-level traces, latency budgets, and failure replay for operations teams

Impact

• · Automated end-to-end handling of repeatable back-office workflows
• · Lower manual effort on cross-system updates with clear accountability per agent step
• · Foundation for expanding agent coverage without rebuilding orchestration

Stack

LangGraph, LangChain, Python, Kubernetes, OpenTelemetry, vector store (Pinecone / pgvector)

Deployment

Private cloud / AWS

Status

Live

An intelligent process optimization engine that uses predictive models and automated recommendations across operations workflows.

Webnetis partnered with operations and data teams to unify sensor, ERP, and manual process data, then deployed models that flag bottlenecks and suggest next-best actions for floor supervisors.

Scope & delivery

• · Real-time scoring and recommendation APIs integrated with existing MES tools
• · Human approval steps before automated changes hit production lines
• · Dashboards for plant managers with drill-down by site and shift

Impact

• · Reduced unplanned downtime through earlier anomaly detection
• · Standardized decision support across multiple facilities

Stack

Python, TensorFlow, MLOps on AWS

Deployment

AWS / Azure

Status

Live

Enterprise conversational AI with RAG, intent routing, and seamless escalation—deployed across web and messaging channels.

The platform grounds answers in approved policy documents and product catalogs, routes complex cases to human agents with full context, and logs interactions for compliance review.

Scope & delivery

• · Multi-channel widget and API for web, mobile, and internal portals
• · Guardrails for PII, tone, and out-of-scope requests
• · Analytics on resolution rate, handoff rate, and content gaps

Impact

• · Faster first-response times on high-volume support queues
• · Consistent answers aligned with official documentation

Stack

OpenAI GPT, Python, React, Node.js

Deployment

Multi-platform

Status

Live

Production forecasting models integrated with ERP and inventory systems for a national retail chain.

We built demand models at SKU and store level, orchestrated retraining on a schedule, and exposed forecasts to planning teams through existing BI tools and automated replenishment jobs.

Scope & delivery

• · Hierarchical forecasting with promotions and seasonality features
• · Azure ML pipelines with monitoring for drift and data quality
• · Integration hooks into ERP for purchase and allocation workflows

Impact

• · Improved forecast accuracy on seasonal and promotional SKUs
• · Planners spend less time on spreadsheet consolidation

Stack

Python, Prophet, Azure ML

Deployment

Azure

Status

Live