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Home / Solutions / Noosphere
AI & Subscriber Intelligence · v2

Noosphere

You already know which subscribers are about to leave. What you lack is a way to act on it before they do, and to prove the action worked. Noosphere is one platform with one loop: a model finds the subscriber whose needs and tariff are drifting apart, an agent workstation turns that signal into an action by your employee, and the outcome returns to tune the model. It runs inside your own perimeter, and every pilot is measured against a baseline.

Plan a pilotSee the problem we solve
Data Labs AI product lead reviewing a subscriber intelligence dashboard
The loop · inside your perimeter
01SignalFinds the subscriber whose needs and tariff drift apart
02WorkstationTurns it into a prepared action with history and an offer
03Human decidesYour specialist makes the call and talks to the subscriber
04OutcomeChecked after 60–90 days, returned to the model
Every outcome retunes the model
The problem

You can see the churn coming. Your teams still can't act on it in time.

Operators hold more data about their subscribers than almost anyone. Four things stop it from becoming results, and each one has a specific answer.

What goes wrong
What Noosphere does
How you verify it

Pain 1The signal dies in a report.

Analytics flags the at-risk subscriber, but nobody owns the next step. The Agent Workstation hands the signal to a specialist with a history review and a proposed offer, inside the CRM they already use.

ProofGap delta after 60–90 days.

Pain 2Experts burn days on lookups.

Finding, matching, checking and filling in eats the day of a presales, retention or fraud analyst. The agent does the lookups and drafts with sources and a confidence level. The human reviews and decides.

ProofTime per task against a control group.

Pain 3Security says no to AI.

Subscriber data cannot go to a public model, and a tool with broad access is a bigger risk than the work it replaces. Everything runs in your perimeter, with the permissions of the person it works for and writes only after confirmation.

ProofSecurity package before the first CISO meeting.

Pain 4Pilots never show a number.

A copilot bought per seat delivers a good feeling and no baseline, and a quarter later the effect dissolves. We agree a baseline and a control group before day one and log every step and outcome.

ProofSuccess criteria written into the pilot contract.
Noosphere v2 · One platform, one loop

From a signal about the subscriber to an action by your employee, and back into the model.

Without action, a signal stays a report. Without a signal, an agent workstation stays a handy assistant. Noosphere is both halves of one cycle.

leaves only by contract NOOSPHERE shared core 01 02 03 04 Signal Workstation Human decides Outcome inside the operator's perimeter
STEP 01 · SIGNAL

Finds the gap

Subscribers whose needs and tariff are drifting apart.

STEP 02 · WORKSTATION

Prepares the action

The signal arrives with a history review and a proposed offer.

STEP 03 · HUMAN

Decides and talks

The retention specialist makes the call and speaks to the subscriber.

STEP 04 · OUTCOME

Feeds the model

After 60–90 days we check whether the gap closed. The result tunes the model and the playbook.

Product 1

Signal

Subscriber intelligence. Detects the growing gap between what a subscriber needs and what they pay for.

Product 2

Agent Workstation

A workstation for each role. It closes the distance between "we have the signal" and "we have a process".

Shared coreProtected perimeterOpen modelsAgent runtimeTuning for each client

The subscriber consent layer remains a separate circuit alongside the core.

Agent Workstation

A human plus an agent trained for their role, inside the windows they already use.

Not a chatbot on the side. A panel in your CRM or ticketing system, or a voice assistant, that automates the work itself and not only the form. Six components, shown for a retention role.

The agent prepares, the human decidesIn the first wave the agent changes nothing in your systems without confirmation.
Inside familiar windowsA panel in the CRM or ticketing system. A new app is the one nobody opens.
Review is the new bottleneckEvery answer carries sources, highlighted changes and a confidence level. Out of scope, the agent says "I don't know".
Every edit is a training exampleCorrections and rejections feed adapter tuning each cycle.
The unit is the pairWe measure, train and sell the human-plus-agent pair, not the agent and not the seat.
First wave

Start where the data is least sensitive. Measure everything.

Entry order: B2B presales first, then retention together with Signal, then revenue assurance. Each pilot is one role with a baseline measured before and after.

1role per pilot
20–50workstations
8–12weeks
60–90days to verify the outcome
One roleOne department.
Baseline firstControl group or phased rollout.
Operator commitmentsRead access to named systems by week two, a pilot owner on your side.
Agreed success criteriaCycle time, quality no worse than baseline, share of tasks done with the agent.
Production contractWith a share of the measured effect.
Why this is the best way

Other routes solve a part. This one closes the whole loop.

A comparison of approaches by what a telecom buyer needs. These are typical characteristics of each category, not statements about any specific product.

We say "orders of magnitude" only about operations: finding, matching, checking and filling in can be several times faster, sometimes ten times. A whole role is limited by the rest of the work and by human review, so we sell the measured effect and not a multiplier. The more operators we work with, the better the role library gets, so your tenth role launches faster and cheaper than your first.

Perimeter

Two deployment modes, one stack. Only the GPU layer differs.

Code, models, policies and logs are identical in both modes. The operator chooses where it runs.

Mode A

At the operator

Agents · connectors · policies · audit log
Models · adapters · vLLM / SGLang
Operator GPUs, or our appliance in your data center
Operated by
the operator; we support remotely under your access rules
Fits
GCC and anyone who cannot move data out of the country
Start
slower: hardware delivery and installation
Mode B

Our dedicated environment

Agents · connectors · policies · audit log
Models · adapters · vLLM / SGLang
Single-tenant racks in CHWY1, Cheyenne
Operated by
Data Labs, under an SLA
Fits
US; Canada if data can be held in the US; pilots
Start
faster: capacity is already in place
About our Cheyenne data center →

Never leaves the perimeter

  • Source data
  • Agent requests and responses
  • Action logs
  • Indexes and embeddings
  • Adapters and weights trained on client data

Leaves only by contract, with consent

  • Role templates and playbooks without client data
  • Anonymized aggregates of outcomes
  • Quality metrics on our own test sets

An adapter trained on client data is client data, because a model can memorize fragments of its training set. What we keep is the tuning recipe, not its result. Telemetry to Data Labs is off by default. In Mode B data does reach our facility, so the perimeter rests on contract and engineering: dedicated hardware, client-held encryption keys, and every Data Labs access written to the log.

Agent security inside the perimeter
Data never leaves the perimeter. Inside it, the agent sees no more than the human it works for.

The log works twice: it is the audit trail for your security team and the raw material for the outcome loop. A security package precedes the first CISO meeting: perimeter architecture, an OWASP risk map, the agent permission policy, a red-team report and role quality checks.

Questions buyers ask

The objections we hear most, answered directly.

We already bought an office copilot. Why add this?
An office copilot is built for office tasks. We go where a cloud copilot is not allowed, or lacks the depth of a telecom role: subscriber data, billing, CDR, tender documents. We do not compete for email and slides.
Will our security team let an agent near our systems?
That is the first thing we designed for. Data, logs and tuned weights stay in your perimeter, telemetry to us is off by default, the agent has no more rights than the person it works for, and it writes only after confirmation. We start with presales because it needs no subscriber data, which is usually the fastest approval path.
What if the agent is wrong?
In the first wave it changes nothing without a human pressing the button. Every answer carries sources, highlighted changes and a confidence level, and outside its competence the agent says "I don't know" and hands the task back. Errors are cheap and reversible by design.
Does this replace our staff?
No. The unit of the product is the pair: a person plus an agent trained for their role. The person decides and talks to the subscriber or customer. We measure team output, not headcount.
How fast will we see a result, and how do we know it is real?
A pilot is one role, 20–50 workstations, 8–12 weeks. We measure before the start and compare against a control group or a phased rollout. Success criteria are agreed in writing before day one.
How do we pay?
By work, not by seat, because a seat price shrinks exactly when people get more productive. A platform fee covers the perimeter, a role launch is a project, a unit of work (a handled contact, a tender response, a closed fraud case) is priced in a volume corridor, and a share of the measured effect applies only after baseline and control are in place.
On our hardware or in your facility?
Your choice, one stack either way. Mode A runs in your data center on your GPUs or our appliance. Mode B runs in our dedicated single-tenant environment in Cheyenne, Wyoming, with client-held encryption keys.

Customer Value + Operator LTV + Trust − Intrusiveness − Risk. Not: maximize ARPU.

The objective function Noosphere is optimized against — a subscriber-first tradeoff, not a sales script.
Signal · Why a different objective

The same data, pointed at trust instead of extraction.

An operator sees more about a subscriber's real situation than almost any other company they deal with — billing, location, device, usage, support history. Pointed at maximizing ARPU, that turns into more aggressive upsell. Pointed at the subscriber's actual interest, the same data becomes a reason to trust the operator with more of their spend voluntarily. Noosphere is built for the second path — it's the more durable one.

Customer Twin

A continuously updated model of the subscriber's real state and needs — not a static CRM record.

Subscriber Agent

Acts on the Twin's behalf first — surfaces the cheaper plan, the unused subscription, the "do nothing" option.

Policy & Ethics Engine

Arbitrates between the two agents — blocks any action where operator interest crosses a line past subscriber interest.

Operator Agent

Pursues commercial and operational goals — churn, ARPU, network cost — within whatever the Policy Engine allows.

Exploded isometric diagram of four stacked layers connected by a central light beam, representing the Customer Twin, Subscriber Agent, Policy and Ethics Engine, and Operator Agent

Fig. 1 — The arbitration stack. Customer Twin at the base, Subscriber Agent and Operator Agent on either side of the Policy & Ethics Engine, one shared signal running through all four.

For the subscriber

An AI that saves money and catches problems, not just sells.

Financial Angel

"You've overpaid ~70 AED/month for three months — this plan actually fits your usage." Told before the bill, not buried in a statement.

Unusual Spend & Fraud Guardian

Flags roaming spikes, premium-SMS charges, subscription creep, and scam-calling patterns — signals only the operator's network can see.

Telecom Concierge

"I'm flying to Japan with my family for two weeks" becomes a concrete plan built from real usage — not a 40-tariff catalog.

Subscription Optimizer

Surfaces the streaming, cloud, and AI bundles a subscriber is paying for and barely using — including ones to cancel.

Family Intelligence

Shared data budgets, spend alerts, eSIM management, and parental controls — without turning the product into surveillance.

Digital Safety for Seniors

Consent-based pattern alerts before a risky transfer — "a few unusual events in the last 20 minutes, worth checking the recipient" — never an automatic block.

For the network experience

Noticing the problem before the complaint call.

Quality-of-Experience AI

"Video calls at home have been degrading for five evenings — it's coverage, not your device. Already optimized, or here's a Wi-Fi Calling fix."

Predictive Customer Care

Opens the support app already knowing what's wrong and when it clears — no menu tree, no re-explaining the problem.

Network Personalization

Temporary QoS shifts for what's actually happening: a gaming boost in the evening, video-call priority, a latency-sensitive profile — built directly on the DPI/PCEF layer.

Growth built on trust

The commercial upside of not being pushy.

Life-Event Assistance

Weak signals — new device, travel pattern, a second SIM — trigger one useful action, never a headline like "we noticed you're moving."

Understanding-Based Loyalty

A long-tenure subscriber's one-off bill-shock month gets a partial waiver automatically — the AI decides where a small gesture has the largest trust payoff.

Personal Digital Assistant

"Why did my bill go up?" and "What plan actually fits my family?" get specific answers built from real account data — not a generic FAQ link.

Operators also get what the subscriber never sees: churn prediction, demand forecasting, tariff-grid optimization, collections prioritization, and NPS root-cause analysis — the same Customer Twin, read from the operator's side.

How it's built

A private, sovereign AI brain — not another SaaS API call.

Noosphere isn't one fine-tuned LLM. It's a hybrid stack, deployed entirely inside the operator's own environment, built to turn billing, network, and CRM history into a continuously updated understanding of every subscriber.

Isometric diagram of six translucent platforms arranged in an ascending staircase, connected by a glowing light trail, representing the six-layer model stack

Fig. 2 — Six layers, one ascending pipeline. Each layer hands a sharper signal to the next — from raw language up to live policy context.

01

Foundation LLM

Language, reasoning, and explainability — adapted from a strong open-weight 30–70B model via domain adaptation and LoRA/SFT. Not trained from scratch, so it ships in weeks and stays cost-sane to run.

02

Customer Embedding Model

Builds a continuously updated digital profile per subscriber from thousands of behavioral features.

03

Time-Series & Behavioral Models

Reads the trend, not the snapshot — falling spend, changing usage, degrading coverage, a new device on the line.

04

Propensity Models

Churn, upsell, credit risk, next-best-action — scored per subscriber, updated as behavior shifts, not on a monthly batch.

05

Recommendation Engine

Turns propensity scores into one specific offer or action — not a tariff list.

06

RAG Layer

Current tariffs, policies, and CRM rules stay live in context without retraining the model every time a plan changes.

Why this is the efficient way to build it

Billions of events, not a training team the size of a hyperscaler's.

Adapt, don't retrain from scratch

An open-weight 30–70B foundation model reaches production in weeks. The operator's own event history goes where it actually moves accuracy — embeddings and feature training, not pretraining a base model.

The event history is the asset

Even a 5–20M subscriber base generates tens of billions of CDR, data, payment, and session events — more signal than most operators are currently using for anything.

One closed loop, not a new stack

Network + Billing + CRM → Customer Intelligence Model → Decision Engine → PCRF/CRM/Campaign → outcome → retrain. Sits directly on the DPI, PCEF/PCRF, and SDMS+DSP data Data Labs already routes for you.

Privacy & sovereignty

Built to never become a surveillance product.

The same depth of data that makes Noosphere useful is exactly what makes the guardrails non-negotiable. These aren't policy promises — they're architectural constraints.

Isometric illustration of a glowing translucent shield with a lock, representing Noosphere's privacy and data-sovereignty guardrails

Private, sovereign deployment

Runs on the operator's own GPU cluster or a dedicated sovereign cloud tenant — see our own Cheyenne colocation footprint. No data leaves the environment, no external API calls.

Pseudonymized by design

Behavior is stored as categories — Travel, Gaming, Streaming, Finance — not URL-level browsing history. Customer IDs are tokenized throughout.

RBAC, audit, DLP

Every access logged, every export controlled, encrypted data lakehouse — standard telecom-grade compliance posture, not bolted on after launch.

Model governance

The model is architecturally barred from surfacing raw subscriber data — a hard constraint, not a prompt instruction.

What Noosphere never does

The line the Policy & Ethics Engine won't let the Operator Agent cross.

1

No frightening insights

It will act on a signal like "usage pattern changed," it will never say "we think you're getting divorced" or "we noticed you lost your job."

2

No dark patterns

If the honest best answer is "don't buy anything," Noosphere is required to say so — a Fair AI / Subscriber Advocate mode, not an optional setting.

3

Consent-first for sensitive alerts

Elderly safety alerts and family controls run opt-in, with the subscriber or family deciding — never a silent background block on their behalf.

Illustrative scenarios

What the loop looks like in practice.

Bill-shock prevention

A postpaid subscriber's bill runs 34% over normal. Noosphere identifies the cause — a roaming session outside the usual pattern — and explains it in-app before the complaint call, with a one-tap fix for next time.

Travel concierge

Usage pattern shows an upcoming trip. The subscriber gets one message: the right roaming pack, pre-purchased data, and a temporary spend cap — not a push notification for an unrelated upsell.

Coverage fix before the complaint

Five consecutive evenings of degraded video calls at one address get flagged as a network issue, not a device issue — routed to network ops with the pattern attached, resolved before the subscriber calls in.

Bring subscriber-centric AI to your network.

Talk to us about deploying Noosphere as a private layer on top of your existing DPI, PCRF, and CRM data.

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