From connectivity provider to intelligent, AI-native operator.
DaasLabs teams set your data and AI strategy, build the BSS, OSS and CDR data platforms, transform revenue assurance, fraud, customer and network operations, and run what we build — on our Data Fabric Framework, telecom accelerators and a supervised digital workforce of AI agents.
– supervised agents in the workforce · – fraud cases being worked on the demo platform
Seven forces are reshaping the operator — each one lands on data.
Traffic up, ARPU flat
Data demand keeps climbing; revenue per user doesn't. Unlimited plans and price competition push value to the apps that ride the network.
5G and fibre must pay back
Heavy network capex arrives years before the revenue. Boards want proof that each site, street and spectrum block earns its return.
Fraud and scams industrialise
SIM swap, account takeover, SIM box, IRSF and smishing hit revenue and customers at once — and regulators expect operators to act.
Churn decided by experience
A slow network, a disputed bill or a long wait in care: customers leave for reasons spread across systems no single team sees.
Networks too complex to run by hand
Multi-vendor, cloud-native 5G multiplies alarms and configuration; energy is now a top operating cost. Autonomy has to be earned safely.
Legacy BSS/OSS and data silos
Billing, CRM, mediation, inventory and network data live in separate stacks with different IDs. Every new use case starts with another extract.
AI rules, privacy and scrutiny
Regulators expect lineage behind every return, consent behind every campaign and supervised AI behind every automated decision.
Every pressure lands somewhere on the value chain.
Operators pulling ahead treat data as a product and AI as an operating capability. The response isn't one platform or one model — it is data and AI applied domain by domain, on a shared, governed foundation.
Operator data and AI programmes stall where data, meaning, control and people aren't designed together.
The four situations we are usually called into — and what changes when the programme starts from a governed foundation.
BSS, OSS and the warehouse disagree
“Active subscriber”, “revenue” and “site” mean different things in billing, CRM, OSS and the warehouse, so every KPI is reconciled by hand.
Sources connected once through the data fabric, with one meaning in a telecom ontology your teams own.
AI pilots don't reach production
Churn models and GenAI care assistants impress in a demo, then stall on data access, privacy review and the question of who will run them.
The pilot is built on the target architecture from day one — gateway, evaluation and release gates included — so it becomes the first production release.
Leakage and fraud are found too late
Usage is checked against the bill by sampling, fraud alerts queue for days, and every block or credit is keyed in by a person.
Agents reconcile and investigate continuously, then act through typed, limited actions with policy checks and human approval.
Regulators ask for lineage and evidence
Regulators, privacy authorities and auditors want proof of where a reported number came from and who accessed subscriber data.
Lineage, policy-as-code and a complete run log make evidence a by-product of daily operations, not a quarterly scramble.
The technology rarely fails on its own. A DaasLabs pilot on the framework and an accelerator reaches a production-ready capability in 30–45 days because it doesn't start from zero.
Operators are moving from copilots to supervised digital workforces — and data decides who gets there.
So what for an operator: once agents work the cases, governed data, autonomy limits and audit trails become the deciding capabilities — not the model.
Third-party figures as summarised in DaasLabs Telecom Insights — not DaasLabs results.
Eight domains of the operator value chain — and where data and AI pay back in each.
Select a domain to see the question it faces, what data and AI do there, and what DaasLabs brings.
Network planning & build
Every 5G site and fibre street is a capital bet. The question is no longer only where coverage is weakest, but where capex will earn a return in revenue, retention or experience.
Data & AI do
- Traffic and demand forecasting by cell and cluster
- Capex prioritisation scored on revenue, churn risk and experience
- Fibre roll-out targeting: where homes passed will connect
DaasLabs brings
- Network, subscriber and revenue data on one governed geo model
- Investment cases the CFO and CTO both sign, with traceable assumptions
- Planning data products reused by build, marketing and finance
Network operations & assurance
Multi-vendor RAN, core, transport and cloud generate more alarms than any NOC can read. Automation, energy and service quality depend on turning that noise into decisions.
Data & AI do
- Alarm correlation and root-cause suggestion across domains
- Predictive maintenance for sites, links and power
- Supervised NOC agents that open, enrich and route tickets
DaasLabs brings
- OSS data products with lineage from alarm to customer impact
- Agents draft the action; engineers approve it
- AgentOps: overrides, limits and drift watched under SLA
Product, pricing & monetisation
Unlimited plans and price wars flatten ARPU while traffic keeps rising. Operators need to monetise 5G, fibre and network APIs with offers priced on evidence.
Data & AI do
- Price and plan elasticity; cannibalisation before launch
- Bundle and convergence design (mobile, fibre, content)
- 5G, FWA and fibre upgrade propensity
DaasLabs brings
- One product and usage view across prepaid, postpaid and fixed
- Offer simulations finance can audit
- Monetisation tied to the network investment behind it
Sales, channels & marketing
Acquisition is shifting to digital and eSIM, while dealer commissions, onboarding fraud and generic campaigns quietly erode margin.
Data & AI do
- Next-best-offer and propensity models in every channel
- Subscription and identity fraud screening at onboarding
- Dealer commission leakage detection
DaasLabs brings
- A single subscriber view shared by marketing, sales and care
- Onboarding fraud handled by the same controls as RA and fraud
- Consent enforced in the data, not in a policy document
Customer care & experience
Churn is decided by experience (dropped calls, slow data, a disputed bill) long before the customer calls. Care teams see the complaint; they rarely see the cause.
Data & AI do
- Churn prediction linked to network experience and billing events
- Billing-dispute resolution from one correlated timeline
- GenAI agent-assist with approved answers
DaasLabs brings
- Network, billing and care data joined per subscriber
- Retention triggered by the cause, not the cancellation
- Care agents that draft resolutions with the evidence attached
Billing, revenue assurance & fraud
Revenue leaks between the network event and the invoice, while scams, SIM swap, SIM box and IRSF move faster than rule sets. Analysts spend their day gathering evidence instead of deciding.
Data & AI do
- CDR-to-bill reconciliation and leakage anomaly detection
- SIM swap, account takeover and wallet fraud scoring in real time
- OODA agents that gather evidence and draft the decision
DaasLabs brings
- Accelerators that start from working controls, tuned to your rules
- Straight-through only within policy; people own the exceptions
- Every block, credit and escalation logged for audit
Wholesale, roaming & enterprise
Roaming and interconnect settle on partner statements reconciled in spreadsheets; enterprise accounts span mobile, fixed, cloud and ICT with no single view of usage, SLA or margin.
Data & AI do
- Pre-settlement reconciliation and dispute evidence at record level
- Route margin and partner anomaly detection
- Enterprise account 360 and SLA-breach prediction
DaasLabs brings
- Settlement assurance that finds the mismatch before the invoice
- Wholesale and enterprise data on the same governed fabric
- Disputes backed by evidence partners can verify
Regulatory, finance & corporate
Regulators, auditors and boards want to know where a number came from, and now how an AI model reached a decision. Privacy, scam-prevention duties and AI rules keep widening the scope.
Data & AI do
- Lineage and data-quality controls behind every regulator report
- AI and agent governance: inventory, evaluation, approval, audit
- Revenue-to-GL reconciliation for a cleaner close
DaasLabs brings
- A working data office with owners for subscriber, usage and network data
- Explainability and audit trails in every model and agent
- A maturity baseline and roadmap the board can track
A services firm that arrives with its own IP — so operators pay for outcomes, not reinvention.
Services
Nine service lines across advise, build, transform and run — delivered by teams who know BSS, OSS, CDRs, revenue assurance and fraud.
Data Fabric Framework
The governed foundation: the 4C method, 167+ pre-built connectors, telecom data models, metadata, lineage, data quality and security.
Accelerators
Seven telecom accelerators — revenue assurance, fraud, disputes, data normalisation, AI processing and compliance — each a configurable starting point.
Supervised digital workforce
AI agents that do the routine work end to end, with people approving exceptions and every step logged.
Nine service lines, organised the way operators buy them.
Select a stage on the wheel, or start from your role.
Set direction and the rules the data must meet.
Data & AI StrategyMaturity, AI operating model, business case Data Governance & RegulatoryOwnership, lineage, privacy, regulator dataEngineer the platforms and the AI that runs on them.
Data Engineering & PlatformBSS, OSS and CDR pipelines, lakehouse AI & Agentic EngineeringML, GenAI and supervised agent squadsChange how a business function works, end to end.
Revenue Assurance & FraudCDR-to-bill, leakage, SIM swap, IRSF Customer, Churn & GrowthSubscriber 360, churn, disputes Network & Service OperationsKPIs, service quality, maintenance Enterprise, Wholesale & InterconnectB2B, roaming, settlement assuranceKeep it healthy and improving after go-live.
Managed ServicesDataOps, MLOps & AgentOps under SLAsEvery service line runs on the same framework, accelerators and digital workforce. All services in detail →
A repeatable 4C method turns raw BSS, OSS and CDR data into production-ready capabilities in 30–45 days.
- Step 1 · Week 1–2Connect167+ pre-built connectors to BSS, CRM, OSS, CDR/xDR and mediation, interconnect and roaming, care and wallet; real-time and batch.
- Step 2 · Week 2–4CurateCleanse and de-duplicate CDRs; GenAI-assisted master data for subscribers, products and sites.
- Step 3 · Week 3–5ContextualizeMetadata catalogue, lineage from network event to invoice, RBAC, policy and automated data quality.
- Step 4 · Week 4–6ConsumeData products, APIs, BI, NLQ GenAI studio and the agents behind each accelerator.
Twelve capability layers give every operator one blueprint — and one way to measure progress.
The same layers we build and score in the maturity assessment. Hover a layer to see what it does.
Runs in your tenancy or on-prem. Code, ontology and agents are handed over — you own what we build. Explore the blueprint →
MIT CISR found enterprises at stages 3–4 perform well above their industry average financially, while those at stages 1–2 perform below it. Source
Seven working accelerators mean no service line starts from a blank page.
Pre-built, configurable starting points on the framework, each staffed by an agent squad. Hover or tap a tile; every one has a working demo.
Revenue Assurance & Fraud CFO · RA / Fraud
Customer, Churn & Growth CMO · CCO
Data & AI Engineering CIO · CTO
Governance & Regulatory CDO
Delivered for telecom operators — and running today on the demo platform.
Selected engagements from the DaasLabs team in telecom. Client names are withheld; where results are not published, we describe the capability rather than quote figures.
“Telco of the future” data platform
Unified data platform across the group's three business units.
Revenue assurance platform
CDR reconciliation and revenue-leakage detection across voice, data and VAS, with automated billing-accuracy verification.
Fraud prevention system
Real-time detection blocking SIM box operations, subscription fraud and spam/scam messaging at network scale.
A supervised digital workforce, by line of business — your people own the exceptions.
Policy decides what goes straight through; a named owner approves the rest.
Click a bar to see the agents in it.
Agents plan, call tools and gather evidence. Policy decides what goes straight through; a named owner approves everything else; a kill switch pauses all agents.
Case figures are live from the OODA fraud agents on the DaasLabs Telecom demo platform — not client results. Agents marked blueprint are configured per engagement.
Agents do the legwork; an analyst makes the call when policy says so.
Replay: a SIM swap, a burst of one-time passwords, then a wallet login from a new device.
- 1Source systemsProvisioning records a SIM swap; the SMS centre delivers a burst of OTPs; the wallet sees a login from an unknown device.
- 2Data fabricStreaming ingestion lands the swap, the SMS records and the wallet events within seconds, quality-checked, as governed data products.
- 3Metadata & ontologyThe events resolve to the same subscriber, MSISDN, SIM, device and wallet account, with lineage back to each source.
- 4ContextPast swaps, device history, recent complaints and the account-takeover playbook are pulled together — only what this analyst may see.
- 5AgentsObserve and Orient agents call
sim_swap_eventsrisk_score, classify an account takeover and propose a wallet freeze and a callback, with evidence. - 6PolicyKill switch, autonomy level, risk tier and exposure limits decide: straight through, or to a person.
- 7Human gateA fraud analyst reviews the evidence on one screen in the fraud command centre and approves, edits or rejects the proposal.
- 8ActionA governed, typed action freezes the wallet for a cooling period and opens a callback task — limited, idempotent and reversible.
- 9AuditEvery step, tool call and decision is recorded; outcomes feed the evaluation that decides whether autonomy can be raised.
Autonomy is set per agent and raised only on evidence — inside hard guardrails.
Risk tier & exposure limits
Straight-through only when risk tier, exposure and evidence checks pass; limits are configuration agreed with each operator's risk team.
Named human owner
Approves, edits or rejects every exception. Credits, partner claims and blocks on enterprise lines always need a person.
Typed, reversible actions
Agents act only through the Action Fabric — never free-form changes to billing, network or subscriber records.
Everything logged
Every signal, tool call and decision, with lineage to the source record. Subscriber identifiers masked in prompts.
Kill switch
One control pauses an agent, a squad or the whole workforce; work returns to the human queue.
What could better revenue protection be worth to your operation?
Move the sliders to your own figures. The estimate applies your leakage and fraud assumptions, and compares manual alert handling with agents working the cases.
Estimate only, based entirely on the figures entered — not a quote, a benchmark or a DaasLabs result. Leakage recovered = revenue × leakage % × share recovered. Fraud avoided = revenue × fraud-loss % × reduction. Manual hours = alerts × minutes ÷ 60. Supervised hours = alerts × (1 − STP share) × review minutes ÷ 60. FTE = hours freed ÷ 150. Excludes platform and run costs.
Faster than services alone, a better fit than software alone.
How the DaasLabs model compares with the usual ways operators deliver data, AI and agentic automation.
| Criterion | Large SIservices only | Point productssoftware only | In-house build | DaasLabsservices + framework + accelerators |
|---|---|---|---|---|
| Starting point | Blank page per project | One use case, vendor's model | Blank page | Working accelerator, 30–45 day pilot |
| Telecom data models (BSS/OSS/CDR) | Generic methods | Product-specific | Build | Pre-built, with a telecom ontology |
| Reusable data foundation | Rebuilt per project | Vendor-specific | Build | Data Fabric Framework |
| Ready-made accelerators | Varies | Single product | None | 7 for telecom |
| Supervised AI agents in operations | Pilots / PoCs | Copilot features | Build & govern | Agent squads with guardrails & AgentOps |
| Process change & adoption | Yes | Left to the operator | Partial | Yes |
| Run & continuous improvement | Separate contract | Product support | Internal team | Managed services |
| Who owns what is built | Varies | Licensed | You | You: code, ontology and agents |
Large SI, point-product and in-house columns are compared on larger screens.
Qualitative comparison of delivery models, not a benchmark.
Four phases, each ending with something you keep — and three ways to buy it.
Discovery & Planning
Maturity assessment and target blueprint across BSS, OSS and network, with a prioritised roadmap.
Gate: pilot scope signed offAnalysis & Design
Target-state design and telecom ontology, mapped to your controls; agent autonomy agreed with risk, privacy and security.
Gate: design authorityBuild & Deploy
Landing zone as code, pipelines on real CDR and billing data, accelerators and agents configured and tested.
Gate: go-live readinessSupport & Embed
Runbooks, evaluation suites, pairing and supervision training for your team.
Gate: handover sign-offStaff Augmentation
Data engineers, architects, analysts and AI specialists embedded in your teams, under your delivery lead.
Project Delivery
Outcome-based delivery of an accelerator or platform build, owned end to end by DaasLabs with phase gates.
Managed Services
We run and improve your data platforms, models and agents — DataOps, MLOps and AgentOps under agreed SLAs.
A 30–45 day accelerator pilot proves value on one use case before you commit to scale.
Fixed scope, fixed timeline, success criteria agreed up front — and a scale-up business case at the end.
One accelerator & its agent squad
Typically SHIELD (revenue assurance) or SENTINEL (fraud), agents starting at “act with approval”.
2–3 source systems
e.g. CDR/mediation, rating & billing, and CRM or SIM-swap events.
One business unit
Named business owner and RA or fraud SMEs for rules and UAT.
DaasLabs pod
Engagement lead, data engineer, telecom domain SME, AI / agent engineer.
Success-criteria targets are agreed with the operator in week 1.
Three steps from this conversation to an AI-native operation in production.
Start with a baseline and one accelerator, prove it, then scale on the same foundation.
Maturity baseline & scoping
Score your twelve capability layers, walk through your data landscape and pain points, and pick the pilot use case.
1–2 weeks to scopeAccelerator pilot
Deploy one accelerator and its agent squad on the framework against live CDR and billing data, and measure the result.
30–45 daysScale & run
Roll out across business units and further accelerators through project delivery or managed services.
Project or managed serviceinfo@daaslabs.ai · Talk to us · Back to the site · © 2026 DaasLabs