Become information rich
Correlate customer and care data, inside-plant network data, regulation and compliance data, order-to-cash and inventory data, and drift and change data into one governed view.
Data as reliableas the network.
Operators run some of the largest data estates in any industry, yet subscriber, network and billing data rarely agree with one another. We fix that first, then put agents to work on it.
Telemetry and BSSUsage, alarms, billing
Asset and subscriber twinsOne governed record
Triage agentsGroup, explain, propose
Engineer approvesNamed owner signs off
Ticket resolvedOutcome logged
01 — Problems and how we solve them
Every tower, every subscriber is an object.
Each solution is measured on the operational number that matters to the team that owns it.
The problemWhat we doWhat changes
Churn stays high and ARPU flat in a price-competitive, prepaid-heavy market; offers are designed for the average subscriber.
What we do
Twyn Subscriber builds synthetic subscriber populations to test tariffs, bundles and loyalty offers before launch, then churn and next-best-offer agents act on consented signals for each segment.
What changes
Measured on churn, ARPU, offer take-up and campaign cost.
Revenue leaks between network usage, mediation, rating, billing and partner settlement.
What we do
Data contracts between OSS and BSS feeds, plus reconciliation agents that trace usage to invoice and flag gaps in interconnect, roaming and content settlements.
What changes
Measured on recovered revenue and time to detect leakage.
Network inventory says one thing and the field finds another.
What we do
Twyn Network Asset: one governed taxonomy for sites, towers, fibre routes, active and passive equipment and power, with site MDM and a field copilot that validates audit photos against the record.
What changes
Measured on inventory accuracy and field rework.
Subscriber verification and SIM fraud carry regulatory and reputational risk.
What we do
Identity resolution across channels, document intelligence for verification and anomaly detection on SIM issuance patterns, with every case routed to a compliance reviewer.
What changes
Measured on fraud caught before activation and audit findings.
Customer data is split across prepaid, postpaid, broadband, enterprise and digital services.
What we do
Customer MDM and a business glossary aligned to TM Forum's information framework create one subscriber golden record shared by marketing, care and billing.
What changes
Measured on duplicate rates and single-view coverage.
Network operations and care teams drown in tickets.
What we do
Triage agents group related alarms and tickets, draft root-cause notes and suggested actions, and hand them to engineers for approval.
What changes
Measured on mean time to resolve and repeat tickets.
Data protection laws demand provable consent and purpose limits.
What we do
Sensitive-data discovery, consent records, masking and purpose-based access across the data estate, aligned to laws such as India's DPDP Act.
What changes
Measured on consent coverage and privacy incidents.
Configuration drift and unplanned changes break services, and compliance audits lag behind policy.
What we do
Configuration management agents compare running configurations with golden templates, simulate the impact of a change before it is made, and enforce policy in real time, with an engineer approving every correction.
What changes
Measured on change failure rate, rollbacks, policy violations and mean time to detect.
Planned and live network data disagree, and finding out why means manual SQL across several OSS systems.
What we do
Twyn Network Inventory Assistant: plain-language questions, AI-generated and validated queries, standards checks and self-healing fixes that an engineer reviews before any record changes.
What changes
Measured on discrepancy backlog, triage time and inventory data quality.
Engineers and field technicians lose hours hunting through manuals, designs and tickets in many formats.
What we do
Twyn Network Knowledge Copilot retrieves text, diagrams and tables from operator and vendor documents and summarises exactly what the user needs, with sources.
What changes
Measured on time to information, mean resolution time and first-time fix.
Truck rolls go out for faults that could be fixed remotely, and technicians rely on static manuals.
What we do
Twyn Field Assist runs remote diagnostics before dispatch, bundles related jobs into one visit, and guides the technician step by step on site.
What changes
Measured on first-time fix rate, truck rolls avoided and job completion time.
02 — Network operations
A secure, AI-powered network operations experience, with data at its core.
Three themes shape every network operations programme we run.
01
02
Accelerate the AI-powered journey
Agentic operations: network monitoring, data acquisition and anomaly detection agents working together, each with a clear job and a named human owner.
03
Build trust along the way
Zero trust by design: no implicit trust, least-privilege access, micro-segmentation and real-time monitoring of every agent and every user.
03 — Autonomous NOC
From reactive NOC to autonomous NOC.
We move a network operations centre up five levels of maturity, from topology aware to service aware to business aware. Data integrity and a unified view of experience, infrastructure, application and network data are the foundation for every step.
ReactiveManual operations. Teams respond to alarms after customers feel the impact.
ProactiveIssues identified early through event de-duplication, grouping and near-real-time anomaly detection.
PredictivePredictive automation: models forecast failures and congestion before they happen.
PrescriptiveAI-assisted automation: agents recommend the fix, with root cause and business impact attached.
AutonomousSelf-organising operations: observe, automate and self-learn, with people approving the exceptions.
Along the way we add end-to-end service visibility, mapping each business service to the network, applications and infrastructure beneath it, plus real-user monitoring and value-stream insight that tracks each transaction across the business.
04 — Autonomous NOC
Autonomous NOC use cases.
Use cases grouped by theme, from process excellence to generative AI. Each one can start on its own and plug into the same data foundation.
| Theme | Use cases |
|---|---|
| Process transformation | Process automation, analytics, standardisation and harmonisation; incident, cycle-time and ticket reduction; ITIL process maturity uplift. |
| Workflow automation | General outage automation, field dispatch automation, digitised shift management, routine maintenance automation, on-call management and service request automation. |
| Data-driven automation | A unified data model; automated root-cause analysis; recovery-time clustering; automated reporting; workforce, services and automation-effectiveness analytics. |
| Business process automation | Repetitive digital tasks, business process automation and compliance automation. |
| AIOps | Dynamic thresholds, intelligent correlation, auto-remediation and self-healing, anomaly detection with noise reduction, and predictive maintenance. |
| Generative AI | Automated incident response, tiered escalation summaries, guided remediation, smart ticket creation, support case synopses and a multilingual assistant. |
05 — AI in operations
Where AI changes network operations.
What each domain looks like today, what it looks like with governed agents, and how we measure the difference.
| Domain | Today | With Twyngentic | What we measure |
|---|---|---|---|
| Service assurance | AI anomaly detection in silos; rule-based monitoring; quality of service tuned after degradation. | End-to-end predictive assurance, self-learning models, real-time traffic optimisation and pre-emptive action. | MTTR, uptime, SLA adherence, false-alarm rate, quality of experience, complaints. |
| Incident management | AI-assisted triage but human-led root cause; runbooks executed manually; impact analysed after the incident. | Root cause with confidence scores, auto-remediation with approvals, real-time impact prediction and adaptive playbooks. | MTTR, RCA accuracy, auto-fix rate, affected customers, downtime. |
| Drift and change management | Reactive drift detection; change checks that are automated but not predictive; policy drift that lags audits. | Drift caught before failure, change impact simulated in advance, real-time policy enforcement and change-event correlation. | Configuration drift, change failure rate, rollbacks, policy violations, mean time to detect. |
| Traffic engineering | Rule-based routing; traffic shifts analysed after the event. | Optimised network utilisation and proactive capacity planning. | Congestion, latency, bandwidth utilisation, packet loss. |
| Capacity planning | Forecasts from historical trends; manual expansion decisions. | AI demand forecasting and AI-assisted scaling of network resources. | Forecast accuracy, over-provisioning, infrastructure cost, SLA compliance. |
| Remote troubleshooting | Manual diagnostics through NOC support. | AI-driven, real-time remote diagnostics. | First-time fix rate, truck rolls. |
| Field guidance | Technicians rely on static manuals and experience. | Real-time AI guidance on site, including augmented-reality support. | Job completion time, technician productivity. |
06 — Agent flow
How the network operations agents work.
From raw telemetry to an approved fix, in six steps.
AcquireThe data acquisition agent collects device health, uptime, latency and packet-loss data over SNMP, gNMI, NetFlow, syslog and proprietary APIs.
StandardiseRaw telemetry is parsed, decoded and normalised. Schema differences go to a model that returns a normalised schema, so new devices do not break the pipeline.
MapA knowledge graph holds every network element, its configuration and its past faults. Each incident is stored as an embedding for similarity search.
WatchThe monitoring agent tracks latency, packet loss, jitter and link stability against a time-series baseline of network health.
Detect and rankReal-time anomalies are compared with historical failure patterns; incidents are logged, classified and ranked by urgency.
Act under supervisionThe agent supervisor routes each incident to troubleshooting, root-cause, optimisation, security, escalation, configuration, service-quality or customer-impact agents. Engineers approve the fix, and the network state is written back to shared memory.
07 — Network inventory
Network inventory discrepancy root cause and self-healing.
Network OSS systems report in network parameters, and the trouble starts when they disagree: a cell active in the live network but planned as inactive, or a site that exists in one system and not another.
Today, finding the cause means logging in to several OSS systems and running complex SQL by hand, with no single place for a 360-degree investigation or a regulatory report. Off-the-shelf language models know nothing about an operator's proprietary OSS data, fine-tuning is costly and never finished, and simply uploading files to a vector store tends to produce confident but wrong answers. Our Twyn Network Inventory Assistant takes a governed, layered approach instead.
| Layer | What it does |
|---|---|
| Ask | NOC teams, network planners and designers, and non-technical stakeholders ask questions about inventory data in plain language. |
| Ground | A retrieval workspace indexes the database schema, column mappings, business rules, OSS inventory rule sets and example rows. |
| Generate | A model writes the query using that context, never guessing at table or column names. |
| Validate and evaluate | A second step checks the query is safe and correct, and a third confirms it actually answers the question asked. |
| Check against standards | Records are validated against the relevant 3GPP specifications. |
| Recommend | The assistant summarises the discrepancy and recommends a fix based on business and configuration rules. |
| Heal | With an engineer's approval, a corrective update is applied and the healed records are read back to confirm the fix. |
| Learn | Feedback at every layer improves the next answer and reduces hallucination. |
Proven on a synthetic twin first
Because real OSS inventory data is proprietary, we build and test the assistant on a synthetic inventory dataset modelled on the operator's own schema, then switch to live data once the approach is proven and approved.
08 — Network lifecycle
Across the network lifecycle.
Agents support five workflows that every telecom company runs, each with a clear objective and outcome.
| Workflow | Where agents help | Objective and outcome |
|---|---|---|
| Security and compliance | Risk assessment and management; policy development and enforcement; threat detection and response; data protection and privacy; compliance monitoring and reporting. | Use AI to enhance threat detection and automate compliance monitoring, for a proactive approach to security and risk. |
| Planning and optimisation | Analysing traffic and performance for managed Wi-Fi, Ethernet, voice, 5G and wavelength services; capacity planning for residential and managed services; evaluating new technologies; long-term growth strategy. | Use AI for real-time data analysis, to inform strategic network planning. |
| Network design and build | Needs assessments and feasibility studies; equipment layouts; fibre-to-the-premises and hybrid fibre-coax design; small cells and large fibre builds; street cabinet and power upgrades; microwave links; wavelengths for data-intensive transport. | Use AI for design simulation, so designs optimise resource allocation before anything is built. |
| Network operations | Monitoring and troubleshooting; configuration updates; preventive maintenance; SLA compliance for Wi-Fi, Ethernet, 5G, voice and wavelength services; incident response and escalation. | Use AI to predict network demand and find issues before they escalate. |
| Field services | Site acquisition and permits; installing routers, switches, Wi-Fi and voice equipment; 5G equipment and antennas; power and fibre backhaul; in-building cabling and street works; site surveys. | Use AI for predictive maintenance, to anticipate equipment failures and optimise scheduling. |
09 — Order to cash
Connecting the dots from order to cash.
Across mobile, cable and broadband, and enterprise services, most revenue problems start where one system hands data to the next.
| Step | Where data breaks | What we add |
|---|---|---|
| Contact centre | Agents lack a single view of the customer and the network they are on. | Customer MDM, agent assist and next-best offer with consent controls. |
| Order entry and orchestration | Orders carry incomplete or inconsistent product and address data. | Product-catalogue taxonomy, reference data and data contracts on every order feed. |
| Provisioning and activation | Devices and services activated against stale inventory. | Inventory reconciliation and activation checks against the network twin. |
| Billing | Usage, rating and billing disagree. | Reconciliation agents that trace usage to invoice. |
| Receivables and payables | Partner and interconnect settlements drift from contracts. | Contract-aware settlement checks and exception routing. |
| Reporting and data management | Every team reports its own version of the truth. | A governed semantic layer and Twyn Ask, with one KPI definition. |
10 — Knowledge and support
Knowledge and support copilots.
Generative AI that changes the support experience for engineers, technicians, procurement teams and care agents.
01
Twyn Network Knowledge Copilot
Technical documentation is spread across PDFs, spreadsheets, slides and documents from the operator and its equipment makers. The copilot retrieves text, diagrams and tables, summarises what matters, and later extends to network applications and support tickets.
02
Twyn Telecom Contract Assistant
Procurement and site teams work with contracts made of a main body, appendices and amendments. Structure-aware chunking, fusion retrieval and an evaluation pipeline that measures accuracy and recall keep answers precise.
03
Support experience agents
A voice and chat agent that resolves routine requests and creates tickets automatically; real-time agent assist with sentiment and quality checks; and knowledge management that spots gaps from incidents and changes.
11 — Security
Zero trust for network operations.
Cloud hosting, remote work and data protection law have moved the perimeter. We secure operations on the airport model, not the castle model.
A borderless perimeter needs adaptive security, a decentralised information system model and data that stays protected on any device and in any location. Every Twyngentic deployment applies three principles and four practices.
- Never trust, always verify; least privilege; assume breach.
- Governance across identity, device, network, workload and data.
- Protection against unauthorised access, data loss and manipulation, including lateral movement and privilege escalation.
- Visibility into shadow IT, and fewer, better-integrated security tools to manage.
12 — Architecture
Built for the realities of a network.
A good solution has to answer architectural concerns as well as business ones.
| Concern | How we address it |
|---|---|
| Cost of building, training and running AI | Small language models, open-source components, resource-aware scheduling and auto-scaling that downsizes off-peak. |
| Disruption to what already works | Existing applications become agent tools; nothing that works is replaced. |
| Dependency on cloud providers and third parties | Cloud, platform and model independent; runs in local data centres, on any cloud or at the edge. |
| Reliability under uncertainty and ambiguity | Grounding in the network knowledge graph and incident history, confidence scores and human approval for every material action. |
| Maintainability across many apps and services | Each agent is a standalone service with versioned APIs, deployed through automated pipelines with canary releases. |
13 — Outcomes
What changes for the network.
Potential outcomes from the multi-agent, knowledge-graph-grounded approach.
01
Up to 40% less downtime*
Network downtime reduced through earlier detection and automated troubleshooting.
02
Up to 50% lower MTTR*
Mean time to repair cut with automated root-cause analysis and troubleshooting.
03
About 20% faster resolution*
Issues resolved faster because engineers start with the cause, not the alarm.
04
About 25% lower cloud cost*
Small models and edge deployment reduce cloud spend.
05
Up to 35% better efficiency*
Network efficiency improved through intelligent rerouting.
06
Up to 20% better QoS*
Quality of service enhanced for the services customers actually use.
* Indicative figures drawn from Twyngentic network operations blueprints and reference architectures. Every figure is confirmed for each client during discovery.
14 — Where telecom meets finance
The richest signal a first-time borrower has.
With explicit subscriber consent, Twyngentic can build governed features from recharge, usage and payment behaviour for lenders and digital-wallet partners — extending credit to people traditional scoring cannot see, while keeping the operator in control of what is shared and why.
Talk to our telecom teamPower to act