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Telecom & Communications

Network Quality Intelligence API

Coverage maps published by operators describe predicted signal. What a device actually gets in a particular building at a particular hour is a different number.

Mobile operators
MVNOs
Field service and logistics platforms
IoT and connected device fleets
Comparison and review sites

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What your customers would see

example interface

MockupIllustrative mockup of the interface we would build. Figures shown are examples, not results from a live deployment.

The problem this solves

Published coverage maps are propagation models. They are produced from transmitter locations, terrain and antenna patterns, and they answer the question an engineer asked — where should signal reach — rather than the question a customer has, which is whether their phone will work where they are standing. The gap between the two is largest exactly where it matters: indoors, in dense urban cells, on transport corridors and at the edge of coverage.

Measured data closes that gap but is awkward to obtain. It comes from devices, which means consent, battery cost and sampling bias: measurements cluster where people already have working connections, so the data is thinnest precisely where coverage is worst. Crowd-sourced datasets carry the same bias plus uncertainty about how they were collected.

The useful product therefore combines modelled and measured data and is explicit about which is which. A coverage estimate backed by two thousand local measurements and one backed by a propagation model should not look identical in the response, because they support very different decisions.

Who buys this

Mobile operators, MVNOs, Field service and logistics platforms, IoT and connected device fleets, Comparison and review sites

Category: Telecom & Communications

What the API returns

What comes back

Coverage estimates by location and technology generation, with the basis stated
Measured connection quality: throughput, latency and reliability distributions
Indoor versus outdoor differentiation where measurement density supports it
Outage and degradation signals detected from measurement volume and quality changes
Route and corridor coverage profiles for transport and field operations
Operator comparison at location level where permitted measurement data allows
Measurement density and confidence per cell, so thin data is visible
Historical quality series for trend and remediation tracking

Endpoints

Method	Path	Returns
GET	/v1/coverage	Coverage estimate for a location and technology
POST	/v1/coverage/area	Coverage grid for a bounding box or polygon
POST	/v1/route/profile	Coverage and quality profile along a route
GET	/v1/outages	Detected degradation and outage signals
POST	/v1/measurements	Ingest consented measurements from your own fleet or app
GET	/v1/quality/{cell_id}/history	Historical quality series for an area

Request and response

illustrative

Request

{
  "location": { "lat": 44.8125, "lon": 20.4612 },
  "radius_m": 500,
  "technologies": ["4g", "5g"],
  "operators": ["op_a", "op_b", "op_c"],
  "include": ["measured", "modelled", "indoor"]
}

Response

{
  "location": { "lat": 44.8125, "lon": 20.4612 },
  "as_of": "2026-10-22T14:08:00Z",
  "operators": [
    {
      "operator": "op_a",
      "coverage": { "4g": "good", "5g": "partial" },
      "basis": "measured",
      "measurement_count_90d": 2184,
      "quality": {
        "throughput_mbps_p50": 42.6,
        "throughput_mbps_p10": 6.1,
        "latency_ms_p50": 31,
        "connection_failure_rate": 0.011
      },
      "indoor": { "estimate": "fair", "measurement_count_90d": 318 }
    },
    {
      "operator": "op_c",
      "coverage": { "4g": "good", "5g": "none" },
      "basis": "modelled",
      "measurement_count_90d": 11,
      "confidence": "low",
      "note": "Insufficient measurements in this cell; estimate from propagation model only"
    }
  ],
  "degradation_signals": [
    { "operator": "op_b", "detected_at": "2026-10-22T11:40:00Z",
      "type": "throughput_drop", "severity": "warning",
      "basis": "p50 throughput down 68% vs 28-day baseline, 140 measurements" }
  ]
}

Where the data comes from

Your own device measurements	Consented measurements from your app or device fleet — by far the best source if you have the install base to collect them
Operator-published coverage data	Modelled coverage where operators publish it, useful as a baseline and for areas with no measurements
Regulatory coverage datasets	Several regulators publish coverage obligations and measured audits, openly licensed in some markets
Licensed crowd-sourced measurement data	Commercial measurement datasets, with terms that often permit internal analysis but restrict redistribution
Cell site registries	Published transmitter locations where available, which anchor the propagation modelling
Terrain and building data	Elevation and building footprints, needed for any propagation modelling that is better than flat-earth

On data licensing. Being allowed to read a dataset does not automatically mean you may redistribute it, resell it through an API, or train a model on it. Before any build starts we check the licence, terms and derived-data rules of every source, and we design around the ones that do not permit your intended use. Where a commercial licence is required, that cost is identified during discovery rather than discovered later.

The intelligence layer

The difference between a demo and a service is mostly in this section. These are the parts that keep the answer trustworthy once real traffic is hitting it.

Measured and modelled estimates kept distinct throughout, never blended into one unlabelled number — the basis is part of the answer
Spatial aggregation onto a grid with per-cell measurement counts, so density is reported rather than hidden by smoothing
Bias correction for the fact that measurements cluster where connections already work, which systematically flatters coverage
Quality reported as distributions, since a median throughput tells you nothing about whether a video call will hold
Indoor and outdoor separation inferred from measurement characteristics where density supports it, and withheld where it does not
Outage detection from simultaneous changes in measurement volume and quality, which distinguishes a network problem from everyone going home
Propagation modelling for unmeasured areas, clearly labelled and with wider uncertainty attached
Monitoring of measurement inflow per region, since a collection failure looks exactly like a coverage collapse

What it changes for the business

Honest coverage claims

Estimates with a stated basis are defensible to customers and regulators in a way a smoothed map is not.

Field operations planning

Route and site profiles let field teams plan around connectivity instead of discovering it on arrival.

Faster degradation response

Measurement-derived signals surface local problems that aggregate network dashboards average away.

Remediation evidence

Historical series show whether a site upgrade actually changed what devices experience.

How it fits into your product

How it embeds

REST point lookups for in-product coverage checks, with area and route endpoints for planning use
Measurement ingestion SDK or endpoint for collecting from your own app or device fleet, with consent handling built into the flow
Map tile output where you want to render coverage directly, with confidence rendered alongside
Deployment inside your own infrastructure, which is usual when measurements come from your own users
Webhooks for detected degradation into your existing network or operations alerting
Licence tags per field so data you may analyse but not republish cannot leak into a public-facing response

Included in every build

Authentication, scoped API keys and per-customer rate limiting
Input validation with predictable, documented error responses
Versioned endpoints so your integration never breaks without notice
Request and response logging for audit and dispute resolution
Latency, error-rate and usage monitoring with alerting
OpenAPI specification, sandbox environment and client examples
Backups, failover design and a documented recovery procedure
An agreed support arrangement once the service is live

How we would build it

about 20 weeks

Discovery and feasibility

Weeks 1–3

This one depends heavily on data access, so we start by proving the sources exist, can be licensed for your use, and actually carry the signal the model needs. If they do not, you find out here.

Architecture and baseline model

Weeks 4–6

A written architecture and a baseline model measured against a holdout period, so you know what accuracy you are buying before the full build starts.

Production build

Weeks 7–16

Data pipelines, model training and serving, historical archive, validation harness, API, security, monitoring and documentation.

Validation and integration

Weeks 17–20

Backtesting against held-out history, accuracy reporting your team can audit, integration support and an agreed retraining cadence.

Commercial models

There is no fixed price list, because the cost is driven by data complexity, integration count, model requirements, request volume and the support level you need. Scope is agreed in writing before development starts.

Fixed-scope prototype

A defined slice of the service, built and priced as one piece of work. The usual first step when the business case needs proving internally.

Fixed-price build

An agreed scope against a written architecture, delivered for an agreed price. Best when the requirements are already clear.

Build then retainer

We build the service, then operate and improve it under a monthly agreement covering monitoring, updates and model maintenance.

White-label product

The service runs under your brand, your domain and your pricing, with MLPivot invisible to your customers.

Questions buyers ask

Can you tell us coverage anywhere?

We can return an estimate anywhere and tell you honestly how much to trust it. Where measurements are dense the estimate is good. Where they are thin it falls back to a propagation model with low confidence stated, and in genuinely unmeasured rural areas that is a weak answer. We would rather expose the confidence than smooth a map until every square looks equally authoritative.

Where do the measurements come from?

Ideally your own app or device fleet, with user consent and a clear disclosure — that gives you the cleanest rights position and the best data. Licensed commercial measurement datasets can supplement it, though their terms frequently allow internal analysis while prohibiting redistribution, which constrains what can appear in a customer-facing response.

Is collecting measurements from our users lawful?

It depends on what you collect and how you ask. Network measurements tied to location are personal data in most jurisdictions, requiring a lawful basis, clear disclosure and usually consent. We would design collection to be minimal and aggregated early, and we would not build a flow that collects location continuously because the coverage map would look nicer for it.

Can we compare operators publicly?

Technically yes, legally it depends. Comparative claims about named operators attract scrutiny and sometimes challenge, and some licensed measurement datasets specifically prohibit using their data for public comparison. We would establish the position per data source before you build a feature that depends on it.

Does this detect outages before the operator does?

Sometimes, for localised problems. Device-side measurement can catch a degraded cell that an operator's aggregate monitoring averages out. For a core network failure the operator knows first. This is a complement to network-side monitoring, not a replacement, and we would present it that way.

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