Intelligent automation
Agentic workflows that execute multi-step processes with human oversight.
AnAIcompanybuiltforexecution
Enterprise AI consulting · Agentic systems · Gen AI at scale
Webnetis helps enterprises adopt agentic AI, generative AI, and machine learning with the engineering discipline required to run in production.
Agentic AIGen AIMLOps & governance
- Pilot to production
- Security & compliance by design
- ROI-led prioritization
- 3500+
- Enterprise clients
- 300+
- AI deployments
- 25
- Industry awards
Trusted by teams building with AI
FinTechHealth AIRetail AICloudScaleDataFlowInsureTechFinTechHealth AIRetail AICloudScaleDataFlowInsureTech
What we build for enterprises
End-to-end AI capabilities—from strategy through production systems.
Enterprise copilots
Gen AI assistants grounded in your docs, APIs, and business rules.
Predictive intelligence
ML models for forecasting, risk, personalization, and operations.
AI-ready data
Pipelines, feature stores, and governance for reliable model delivery.
Enterprise AI consulting
From strategy to shipped systems
We work alongside your leadership, product, and engineering teams to define what to build, how to govern it, and how to operate it after launch.
AI opportunity discovery
Prioritize use cases by ROI, feasibility, and data readiness—not hype.
- Stakeholder workshops and process mapping
- Technical feasibility and compliance screening
- Business case and KPI definition
Roadmaps that ship
Phased plans from proof-of-concept to platform scale.
- Quarterly delivery plans with clear dependencies
- Buy vs. build guidance for models and platforms
- Vendor and cloud architecture recommendations
Governed production AI
Security, observability, and responsible AI built into delivery.
- Access control, logging, and data residency options
- Model risk management aligned to your industry
- Runbooks for incidents, drift, and model updates
Next-generation AI
Agentic AI & Gen AI
Leading enterprises are deploying autonomous AI agents that execute complex workflows end-to-end—from research and drafting to approvals and system updates.
Generative AI only creates lasting value when it is grounded in your data, governed by your policies, and operated like any other production system. Webnetis helps you move from isolated experiments to agentic and Gen AI programs that scale with your business.
What we deliver
- Autonomous agents for multi-step business workflows across sales, support, finance, and operations
- RAG, fine-tuning, evaluation, and guardrails tailored to enterprise Gen AI
- Pilot-to-production playbooks with governance, MLOps, and change management
Business outcomes
- Faster cycle times on repetitive knowledge work
- Consistent answers aligned with approved policies and sources
- Measurable adoption, quality, and cost metrics for leadership
Built with modern AI stack
OpenAIAzure AIAWSPythonPyTorchTensorFlowLangChainKubernetesDatabricksSnowflake
Our services
Consulting and delivery across the AI lifecycle—strategy, data, models, and systems in production. Each engagement is scoped to your industry, compliance needs, and existing technology stack.
Agentic & generative AI
AI agents and generative applications designed for enterprise use—with identity controls, policy guardrails, observability, and a clear path from pilot to full rollout.
- Multi-step agents integrated with CRM, ERP, ticketing, and internal APIs
- RAG, prompt engineering, and fine-tuning grounded in your approved knowledge
- Human-in-the-loop review, audit logs, and role-based access for regulated teams
AI agentsLLMsRAG
Machine learning
Custom models and MLOps so teams can train, deploy, monitor, and improve models with confidence—on cloud or hybrid infrastructure.
- Forecasting, classification, recommendation, and anomaly detection use cases
- CI/CD for models, feature stores, drift detection, and automated retraining
- Documentation and handover so your team can own the platform after go-live
MLOpsModel trainingMonitoring
Data engineering
Data platforms and pipelines that keep AI workloads fast, reliable, and ready for compliance reviews—batch, streaming, and lakehouse patterns.
- Ingestion, cleansing, and feature pipelines for training and real-time inference
- Data quality checks, lineage, and cataloging for audit-ready AI programs
- Integration with Snowflake, Databricks, Azure, AWS, and on-prem sources
Data pipelinesCloudGovernance
Analytics & insights
Dashboards and reporting that connect model performance to business KPIs and executive decisions—so leaders see impact, not just accuracy scores.
- Executive and operational views for adoption, latency, cost, and ROI
- Experiment tracking, A/B tests, and model comparison for product teams
- Self-serve analytics layered on your warehouse or semantic models
Business intelligenceKPIsReporting
Built like an AI company
Research-grade thinking. Production-grade engineering.
Responsible AI
Safety, privacy, and governance are part of every architecture—not an afterthought.
Production-first
We ship systems that run in the real world, with SLAs your business can trust.
Partner, not vendor
We embed with your teams and leave you stronger after go-live.
Delivery process
A proven four-phase approach that moves from executive alignment to measurable outcomes in production.
01
Assess
AI readiness, use-case prioritization, and ROI modeling with your stakeholders.
- · Current-state architecture and data maturity review
- · Shortlist of high-impact use cases with effort and risk scores
- · Executive roadmap with milestones, owners, and success metrics
02
Prototype
Rapid pilots with real data—agents, models, or copilots you can test in weeks.
- · Working proof-of-concept on representative production data
- · Security and responsible-AI checklist applied from day one
- · User feedback sessions to validate value before scale-up
03
Deploy
Secure integration, observability, and change management for production AI.
- · Hardened APIs, SSO, secrets management, and environment promotion
- · Monitoring, alerting, and runbooks for operations teams
- · Training and documentation for business and IT adopters
04
Optimize
Continuous improvement, retraining, and scale across the organization.
- · Model performance reviews and cost optimization
- · Expansion to additional teams, regions, or product lines
- · Center of excellence support and capability transfer
AI in production
Representative systems we've designed, built, and support in live environments—spanning intelligent automation, agentic AI, and advanced ML. Details are summarized for client confidentiality where required.
Intelligent automationBFSI, retail & high-volume support
BridgeFlow WhatsAgent
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.
- 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
WhatsApp Business APILangGraphAI agents
Live · AWS / Azure
Stack: LangGraph, Python, FastAPI, WhatsApp Cloud API, Redis, PostgreSQL, OpenAI / Azure OpenAI
Intelligent automationEnterprise operations & IT
AutoGraph Nexus
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.
- 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
LangGraphMulti-agentWorkflow automation
Live · Private cloud / AWS
Stack: LangGraph, LangChain, Python, Kubernetes, OpenTelemetry, vector store (Pinecone / pgvector)
Intelligent operationsManufacturing & logistics
OpsVertex AI
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.
- 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
Agentic MLPythonTensorFlow
Live · AWS / Azure
Stack: Python, TensorFlow, MLOps on AWS
Enterprise copilotFinancial services & SaaS
Atlas Knowledge Copilot
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.
- 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
Gen AIRAGOpenAI
Live · Multi-platform
Stack: OpenAI GPT, Python, React, Node.js
Trusted by AI leaders
“Webnetis took us from Gen AI experiments to a governed copilot in production—our support team cut resolution time by 40%.”
“Their agentic workflow automation replaced hours of manual ops work. Clear roadmap, clean engineering, zero drama.”
“The ML platform they built still powers our forecasting two years later. Best AI partner we've worked with.”
Common questions
Quick answers before we jump on a call.
Both. We combine foundation models and APIs with custom fine-tuning, RAG, and classical ML when that's what your use case needs.
AI insights
Agentic AI, Gen AI, and MLOps from our engineering team.
Agentic AI · 12 Feb
Agentic AI in the Enterprise: What Actually Works
Lessons from deploying autonomous workflows beyond the pilot phase.
Gen AI · 28 Jan
Gen AI Guardrails That Scale
RAG, evaluation, and policy layers for trustworthy copilots.
MLOps · 15 Jan
MLOps for Mid-Market Teams
A practical stack without enterprise bloat.
Let's scope your next initiative
Tell us your goals—we'll map opportunities, architecture, timelines, and a practical path to production AI. Initial consultations are focused on clarity, not sales pressure.