AI for the network, built by people who ran one.

BitSwan turns RAN KPIs, alarms, power telemetry and supplier invoices into live operational intelligence — on-prem, open-source based, and in production at CETIN SK for years.

The operational gap

The cause lives in a layer no one is watching

RAN, transport, power, CPE and tickets each have their own console. A degrading backhaul link silently starves a cell; by the time KPIs visibly drop and calls arrive, the trail is cold. Meanwhile, the scripts that should glue it all together are one-off, unowned, and quietly talking to production systems.

• Per-layer tools, no cross-layer view — symptoms get fixed, causes survive

• Vibe-coded automations: no Git, no RBAC, no monitoring, no owner

• Vendor platforms that lock the roadmap to someone else's priorities

The talent gap

Data scientists who speak telco barely exist

AI on a network needs people who understand both KDE models and what PRB utilisation means at 17:00 on a village site. Hiring one side and training the other takes years a transformation roadmap doesn't have.

• “It is nearly impossible to find data scientists who also know telco. I could hire data scientists, but they would spend years learning telco — and I need to innovate now.”
  Group CIO · European telco group

Platform

Production-ready from minute one

BitSwan is an enterprise data & AI automation framework: real-time pipelines, Python automations, MCP servers and AI agents — with GitOps deployment, RBAC, secrets management and monitoring built in, not bolted on. Fully on-prem, under your control.

People

Telco DNA, not generic consulting

Decades of combined telco experience on one team: a former telco CIO, CTO and directors of network operations, alongside data scientists and programmers who have worked inside telcos for years. We don't need a workshop to learn what an S1AP alarm or an E-RAB failure means — we've carried the pager.

Pace

Weeks to value, not roadmap quarters

Use cases ship on rails that already exist: the battery-intelligence pilot pattern lands in one region inside eight weeks, and every new connector reuses hardened production modules. Heavy AI use on our side is part of why.

Network

RAN & transport anomaly detection

A dedicated ML model for every network element across key metrics in 2G/4G/5G and transport. Deviations are correlated across KPIs, filtered for persistence, and handed to operators with one-click feedback.

Energy

Power & invoice intelligence

AI reads every supplier invoice, reconciles invoiced energy against measured site consumption, and queues the discrepancies for review. Energy stops being a line item and becomes an observable system.

Customer & Ops

TESU — the technical-support cockpit

Everything about one customer's connection on one screen: antenna, router, signal, site and cell health with anomaly flags, 30 days of history, and remote actions — plus an AI assistant being built on top.

Network

Site status & network outages

Real-time site availability derived from core signalling: which sites are down, since when, how many customers are affected — with hierarchy, history and a resolution workflow.

Energy · Network

Power outage detection

Mains-power loss detected at the site level and mapped across the country in real time — so the NOC sees the grid event behind the network event.

Network & Ops

Planned outage management

Every planned work order resolved to the customers it will hit — with locations, contact numbers and a ready-to-send notification message. Maintenance stops being a surprise to the people it affects.

Energy

Battery intelligence

Every site battery scored from real outage behaviour, inventory age and load — producing a traffic-light replacement queue for the NOC and field maintenance instead of calendar-based guesswork.

Customer & Ops

Chatbots & AI agents

AI assistants over live operational data, served on private LLM infrastructure through governed MCP interfaces — every tool call authenticated, authorized and audited, and no tokens leaving the building.

“This is amazing. I have never seen data combined from such sources the way you did it.”

CFO · Telco operator

“It's amazing how fast you can create solutions. I couldn't believe it until I saw it.”

Head of Operations · Telco operator

Dark NOC

Anthena — the network intelligence layer

A layer above the alarm plane that joins every signal by time and site, detects what alarms miss, explains incidents as cause→effect chains an operator reviews — and captures each confirmed explanation as a reusable, named detection.

Energy

Power anomaly detection

Nine defined use cases that cross-check billing, ELTEK measurements, equipment load and temperature to find the energy losses invisible to any single system — from phantom loads to dying HVAC.

AI infrastructure

Private AI for telco

An LLMOps control plane for operators: pool GPUs wherever they live, host and version models, see exactly who calls what with which data at what cost — and keep every token inside the perimeter.