Consulting & Solutioning · Data Engineering · Network Integration · IOT
Getting a network onto YantrAI starts with connecting it. We start with your business priorities and environment, then unify your data into the canonical schema and integrate with vendor NMS and network functions — the engineering that makes the connection possible in the first place.
Before YantrAI can reason over your network, your network has to be readable by it. Adopt is the practice that makes that true — from business-priority solutioning to pipelines and protocol stacks.
No two networks arrive at Adopt the same way. Vendor mix, topology, legacy tooling and technology maturity vary CSP to CSP — and so do the business priorities behind the ask: one operator is chasing lower MTTR and fewer truck rolls, another is under pressure on OPEX and CAPEX, a third is racing time-to-market on a new 5G or enterprise service. Before a pipeline is built or a protocol stack is touched, Adopt starts with solutioning — understanding your environment and what your business is actually being measured on, then architecting the specific integration path that gets you connected in a way that serves that outcome, not a generic checklist.
Mapping current network topology, vendor landscape, data maturity and operational priorities to find the fastest, lowest-risk path onto YantrAI.
Translating those findings into a concrete integration blueprint — which data pipelines, which protocol layers, which network functions — before a line of code is written.
Every Adopt engagement runs into the same three decisions before a single pipeline goes live — where it runs, what it runs on, and how it ships. We help you make the call, then provision to it.
On-premises for data-sovereignty or regulatory needs, or a cloud provider — AWS, Azure or GCP — for elastic, managed scale.
CPU for pipelines and protocol stacks, GPU for model training and inference, memory for real-time throughput, and storage for the canonical data store, time-series and vector indexes.
YantrAI services — Orchestrator, RAG Engine, Text2CLI — alongside third-party components like InfluxDB, RabbitMQ and Apache Airflow, packaged as containers and shipped through the same CI/CD pipeline.
The practice of collecting, cleaning, joining and transforming inventory, performance, alarm and topology data into YantrAI's canonical schema — the same Unified Network Data Store YantrAI Netra runs on.
ETL, distributed data processing, NoSQL/RDBMS databases for data warehouse and lake, integrated with ML and visualization tools.
Distributed event processing with ML/visualization integration and storage for applications requiring real-time response.
We design, plan, deploy and integrate 5G networks so they're instrumented and AI-ready — not just built, but connected.
Private network design, planning and deployment for factory automation, mines, ports and logistics — wired into the platform from day one.
AI/ML-driven self-organizing network (SON) integration for RAN, Core Network and Telco Cloud.
We connect device fleets — embedded software through cloud telemetry — so connected-device data becomes another signal the platform can reason over.
Embedded software and BSP development, with MQTT/HTTP protocol integration for connected devices.
Scalable platform development, telemetry ingestion, and AI/ML-driven insights from device data.
From canonical data pipelines to protocol-level integration — let's talk about what Adopt looks like for your network.