Automations
This pillar covers radio planning workflows that simulate propagation, predict interference, and optimize antenna or spectrum decisions across urban and private wireless networks. The content should explore digital twin modeling, autonomous replanning, and network engineering workflows for carriers and enterprise connectivity teams.
This foundational page details a custom, end-to-end agentic workflow that automates the core RF planning and spectrum optimization lifecycle. It covers how specialized agents ingest geospatial and network data, run propagation simulations, predict interference, and generate optimized site and parameter configurations, delivering significant reductions in planning cycle times and improved network performance for carriers and large enterprises.
This page explains a custom workflow where coordinated AI agents autonomously build and maintain high-fidelity digital twins of radio networks. It details how agents fuse GIS, BIM, live network telemetry, and equipment data to create a dynamic simulation environment, enabling rapid what-if analysis and cutting weeks from traditional planning and upgrade validation processes.
This page outlines a custom AI workflow that predicts and maps RF interference hotspots before they impact service. By analyzing 3D building data, traffic patterns, and spectrum usage, the system proactively identifies co-channel and adjacent-channel conflicts, allowing network engineers to mitigate issues during the design phase and avoid costly post-deployment fixes.
This page describes a custom agentic system that automates the complex site selection and configuration process. It evaluates terrain, coverage goals, zoning constraints, and backhaul availability to recommend optimal locations and initial parameters, dramatically accelerating network rollout and improving capital efficiency for greenfield and expansion projects.
This page details a custom orchestration workflow for designing and optimizing networks where multiple wireless technologies must share spectrum and infrastructure. It explains how AI agents model interactions, allocate resources, and configure parameters to minimize mutual interference and maximize aggregate capacity, a critical capability for neutral hosts and complex enterprise campuses.
This page covers a custom predictive workflow that identifies potential coverage gaps using simulation, historical performance data, and planned urban development. It then automatically generates remediation plans, such as new small cell placements or antenna adjustments, enabling proactive network improvement and reducing customer churn due to poor service.
This page explains a custom AI workflow that forecasts future network capacity demands and pinpoints traffic hotspots. By analyzing trends, event schedules, and demographic shifts, the system triggers pre-emptive capacity upgrades or traffic steering actions, preventing congestion and maintaining quality of service without manual intervention.
This page outlines a custom simulation and validation workflow for implementing Dynamic Spectrum Sharing. It details how AI agents model the real-time sharing of spectrum between 4G and 5G technologies, testing configurations and predicting performance impacts to ensure efficient utilization and smooth migration paths for operators.
This page describes a custom automated workflow for performing and validating complex link budget calculations across thousands of potential links. AI agents ingest equipment specs, environmental data, and regulatory limits to ensure signal viability, replacing error-prone manual spreadsheets and accelerating backhaul and access network design.
This page details a specialized AI workflow for planning high-frequency mmWave networks. It automates the analysis of signal propagation and susceptibility to blockage from foliage, buildings, and weather, generating precise deployment strategies and redundancy plans to ensure reliable service in dense urban and fixed wireless access scenarios.
This page covers a custom decision-support workflow for spectrum auctions. It explains how AI agents analyze auction rules, competitor behavior, and valuation models in real-time to recommend optimal bidding strategies, helping operators secure critical spectrum assets at the best possible price and strategic fit.
This page outlines a custom workflow for implementing and managing Dynamic Spectrum Access systems. It details how AI agents monitor spectrum occupancy, enforce priority rules (e.g., for public safety), and dynamically grant access to secondary users, maximizing spectral efficiency and creating new revenue streams for spectrum holders.
This page describes a closed-loop automation workflow for handling RF interference. AI agents continuously monitor spectrum, detect anomalies, classify the source (e.g., jammer, faulty equipment), and execute predefined mitigation actions like parameter changes or alerts, reducing mean-time-to-repair and protecting network performance.
This page explains a custom regulatory workflow for operators with networks near national borders. It automates the ingestion of international spectrum regulations, models signal spillover, and generates compliant configuration plans and reporting documentation, reducing legal risk and manual coordination overhead.
This page details a custom predictive analytics workflow that forecasts spectrum congestion in specific geographic areas. By analyzing usage patterns, scheduled events, and IoT deployment plans, the system alerts planners to future bottlenecks, enabling proactive spectrum refarming or technology upgrades.
This page covers a custom RAN optimization workflow that automatically configures and tunes Carrier Aggregation (CA) combinations. AI agents analyze device capabilities, cell load, and spectrum holdings to dynamically activate optimal CA pairs, maximizing user throughput and network efficiency without manual engineering.
This page outlines a complex planning workflow for migrating spectrum from legacy technologies (e.g., 2G/3G) to modern ones like 4G/5G. AI agents model customer migration, predict coverage impact, and generate phased refarming plans that minimize service disruption and maximize the value of reclaimed spectrum.
This page describes a continuous optimization workflow for RAN performance. AI agents analyze KPIs, traffic patterns, and interference maps to autonomously adjust antenna mechanical tilt, azimuth, and beamforming parameters, improving coverage and capacity while reducing manual drive-testing and parameter tuning labor.
This page details a custom workflow for optimizing complex Massive MIMO systems. It explains how AI agents simulate and test countless beamforming and scheduling parameter combinations to find optimal settings for different traffic scenarios, unlocking the full performance potential of advanced antenna systems.
This page covers a custom workflow for the AI-driven creation and testing of RIC xApps and rApps. It details how agents translate high-level network goals (e.g., improve energy efficiency) into executable, validated control policies for Open RAN systems, accelerating the adoption of programmable, intelligent RAN architectures.
This page outlines a predictive maintenance workflow for RAN infrastructure. AI agents analyze telemetry from base stations and radios to forecast hardware failures or performance degradation, triggering maintenance work orders and parts logistics before outages occur, thus improving network reliability and reducing operational costs.
This page explains a custom automation workflow for planning and managing Physical Cell Identifiers. AI agents analyze network topology and automatically assign conflict-free PCI values during initial design and network expansion, and continuously monitor for and resolve new conflicts caused by changes, preventing call drops and handover failures.
This page describes a dynamic optimization workflow for uplink power control and Random-Access Channel (RACH) parameters. AI agents adjust settings based on real-time load and interference conditions to improve access success rates, reduce uplink noise, and enhance overall RAN stability, particularly in dense user environments.
This page details a custom workflow for designing and assuring network slices in private 5G/4G deployments. AI agents translate enterprise SLAs (for latency, bandwidth) into slice configurations, continuously monitor performance against guarantees, and autonomously reallocate resources to maintain compliance for critical industrial applications.
This page outlines a specialized workflow for designing industrial private 5G networks. AI agents ingest factory floorplans, machine locations, and application requirements (ultra-reliable low latency, massive IoT) to automatically generate coverage, capacity, and slicing plans that meet operational needs, drastically shortening design cycles for system integrators.
This page explains a custom workflow for planning university, corporate, or government campus networks. It details how AI agents model diverse traffic profiles (video, IoT, research data), apply QoS policies, and design the network to meet strict SLAs for different user groups and applications, ensuring optimal performance from day one.
This page covers a targeted workflow for designing wireless networks in logistics hubs. AI agents model the unique challenges of metal racks, moving vehicles, and dense IoT sensor deployments to plan coverage and capacity that supports inventory management systems, autonomous guided vehicles, and worker devices without dead zones.
This page describes a custom workflow for automating the design of in-building wireless systems. AI agents use BIM models and material properties to simulate propagation, optimally place DAS antennas or small cells, and specify equipment, reducing the manual labor and expertise required for complex venue coverage projects.
This page details a high-stakes workflow for planning networks in stadiums and arenas. AI agents simulate extreme user density, predict traffic spikes during events, and design cell layouts and capacity solutions (including mmWave) to prevent congestion, enabling reliable social media sharing and concession sales for venue operators.
This page outlines a ruggedized workflow for designing resilient networks in harsh industrial environments. AI agents model terrain, account for machinery interference, and plan for redundancy and failover to ensure continuous connectivity for safety systems, autonomous haulers, and operational data in mining, port, and offshore operations.
This page explains a business intelligence workflow that automates the initial site acquisition process. AI agents score potential sites based on zoning data, landowner history, structural suitability, and coverage contribution, prioritizing the most viable locations and reducing the time and cost of the real estate and permitting phase.
This page describes a workflow that extends planning into physical deployment. AI agents take approved site designs and automatically generate civil works plans, including foundation specs, cable trench routes, and power requirements, and coordinate with construction management systems to streamline the build process.
This page covers a critical post-deployment workflow for maintaining network accuracy. AI agents compare planned designs against data from site audits and installation reports, automatically updating the network inventory and digital twin with as-built information, ensuring planning and operations systems reflect reality.
This page details an operational assurance workflow where AI agents continuously monitor network KPIs and telemetry. Using advanced anomaly detection, they identify subtle performance degradations or emerging faults faster than traditional threshold alarms, triggering diagnostics and reducing mean-time-to-identification for NOC teams.
This page outlines a workflow that tackles alarm storms in the RAN. AI agents ingest and correlate thousands of alerts from different network elements, identify the underlying root cause (e.g., a faulty sector card), and present a single, actionable incident to engineers, drastically reducing troubleshooting time and improving MTTR.
This page describes a workflow that moves from reactive to predictive maintenance. AI agents analyze equipment health indicators, weather forecasts, and site access logistics to generate optimized maintenance schedules, preventing failures during peak traffic periods and improving workforce efficiency for field operations.
This page covers a strategic financial workflow for network planning. AI agents model the TCO of different technology choices, site configurations, and vendor options over a multi-year horizon, identifying the most cost-effective network evolution path and providing data-driven justification for CAPEX and OPEX decisions.
This page explains a financial planning workflow that automates the creation of detailed CAPEX forecasts. AI agents translate network design plans into itemized cost estimates for equipment, site builds, and licensing, and can re-forecast dynamically as designs change, improving budget accuracy and financial control.
This page details a sustainability-focused workflow for reducing network energy use. AI agents model the power draw of different network configurations and traffic loads, and automatically activate energy-saving features (like cell sleep modes) or recommend hardware swaps to lower operational costs and carbon footprint.
This page outlines a strategic workflow for managing a spectrum portfolio as a financial asset. AI agents analyze usage data, market trends, and regulatory developments to continuously value different spectrum bands, identify underutilized assets, and recommend acquisition, leasing, or divestment strategies to maximize portfolio value.
This page covers a forward-looking workflow for integrating satellite (NTN) and terrestrial networks. AI agents model satellite coverage footprints, handover scenarios, and spectrum sharing considerations to design complementary networks that provide seamless coverage, a critical capability for mobile operators and remote industry.
This page describes an emerging technology workflow for planning and operating RIS units. AI agents simulate the impact of these passive reflectors on network coverage, determine optimal placement on buildings or poles, and dynamically control their phase shifts to steer signals around obstacles, enhancing capacity in challenging environments.
This page details a workflow for the practical adoption of Open RAN. AI agents automate the compatibility testing and integration of multi-vendor RAN components (RU, DU, CU), generate configuration baselines, and validate end-to-end performance, reducing the integration risk and time-to-market for disaggregated networks.
This page explains an advanced workflow for the full lifecycle management of network slices tailored to verticals like automotive or healthcare. AI agents handle slice instantiation, continuous SLA monitoring, dynamic resource scaling, and secure slice retirement, providing a turnkey automation layer for slicing-as-a-service business models.
This page outlines a privacy-preserving AI workflow for optimizing large, distributed RANs. It details how a central orchestrator coordinates federated learning cycles across thousands of base stations, allowing them to collaboratively improve local models (e.g., for handover prediction) without sharing raw user data, enhancing performance while complying with data regulations.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
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We understand the task, the users, and where AI can actually help.
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We define what needs search, automation, or product integration.
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We implement the part that proves the value first.
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We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
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