Answers

Look for a partner who starts from a specific, costed problem rather than a technology. The useful first question is not "what AI could we use" but "which decision are we making late, or making blind, and what does that cost us per week?" A partner who cannot answer what your first deployment will change operationally, in one sentence, is selling you a pilot rather than a system. Ask to see a deployment where the outcome was measured, not just delivered.

• See measured outcomes
• Book a call

Yes, and that is usually the strongest starting position. An archive of satellite, drone, CCTV or inspection imagery can be processed retrospectively to reconstruct how sites and assets changed over prior years — you are not limited to monitoring from today forward. On one regional energy engagement, processing existing imagery surfaced over 2,500 operational assets that did not appear in local regulatory records. The constraint is rarely data volume; it is that nobody has the capacity to look at it.

• Visual data to queryable intelligence
• Change detection & monitoring

Pricing is structured to the shape of the work rather than a fixed list: fixed-price contracts for well-defined scopes, time and materials where the scope will evolve, dedicated teams at a negotiated monthly rate for long-running programmes, and subscription pricing for ongoing monitoring, maintenance and support. Which one fits is decided in the first consultation, after the problem is scoped — quoting before that is guesswork. The honest answer to "what does it cost" is that it depends on the area you need covered, how often, and how accurate the output has to be.

• Book a free consultation

Weeks, not quarters, for a first working system on a scoped problem. Setup is deliberately quick and most teams can integrate agents into existing workflows in hours, with results visible within days. Larger geospatial builds take longer because they depend on imagery availability for your region — but a regional grid audit that was quoted at six months of helicopter flyovers and drone crews was completed in 72 hours.

• Read that case study

No. Everything we build connects to the tools you already run — CRMs, GIS platforms, asset registers, maintenance systems, databases and APIs. Detections are delivered as GIS-ready datasets, an API, alerts or a dashboard, whichever your team already works in. Requiring you to adopt a new system to read your own data defeats the point of the exercise.

• Agentic AI for data interaction

When the answer you need is not visible in imagery or recoverable from your data. We build detection, structuring and query systems on top of visual and operational data; if the question depends on something that cannot be seen or measured from the sources available, we will say so rather than deliver a system that reports confidently on things it cannot see. We are also the wrong fit if you want a licensed off-the-shelf product — we build systems on your data rather than selling a dataset.

The clearest returns come from closing the lag between what is happening in the field and what your schedule assumes. One E&P operator was losing $40,000 a day to idle rig standby because pad clearance data arrived five to seven days late; detecting pad state from recurring satellite passes removed the lag at its source. Other consistently profitable applications are refinery and loading-bay activity tracking, pipeline right-of-way monitoring, and flare or storage-tank activity as a supply signal.

• Automated Rig Tracker
• Energy asset intelligence

For territories too large to inspect on a useful cycle, satellite-based detection is usually the only approach that scales, because cost does not rise in proportion to area. Assets are detected and classified from imagery, converted into a GIS-ready dataset with coordinates, class and confidence, then compared across passes so change surfaces as dated events rather than as something noticed at the next audit. Drone and inspection imagery slot into the same pipeline for the sites that warrant closer attention — the two are complementary, not alternatives.

• Energy & resources
• Autonomous infrastructure intelligence

Yes — independent verification from imagery is one of the strongest uses of this technology, because it does not rely on the counterparty's data. On a portfolio of 47 solar sites across 12 countries, the built extent of each array was detected from current satellite imagery, converted to an estimated generation capacity per site, and reconciled against the seller's data room figures. That surfaced an $8.4M discrepancy before close, inside the deal window and without sending survey teams to twelve countries.

• Read the case study

For solar, yes, and from orbit. Structural parameters visible in imagery — panel array geometry, string configuration, physical tilt angle — are extracted and combined with localised historical irradiance data and seasonal solar angle models to produce a physics-grounded generation estimate per site. It is an estimate rather than a meter reading, but it is derived from what is physically installed rather than from what a registry says should be there.

• Energy asset intelligence

Yes. Detection works from what is physically visible rather than from registries, which is exactly why it surfaces infrastructure that regulatory data has missed, not yet recorded, or recorded incorrectly. On one multi-state grid mapping engagement this produced more than 2,500 operational assets absent from local records, at 93.8% inference precision.

• Mapping a regional grid from space

Roads, buildings, utility corridors, transmission towers, substations, solar arrays, storage tanks, well pads, pipelines, bridges, land use change, vegetation encroachment, flooding, erosion and construction progress — among others. What is achievable depends on the asset class and imagery resolution: road networks and building footprints are reliably detectable at sub-metre resolution, while individual utility poles need higher resolution or aerial and drone capture. We confirm what is achievable against your specific target classes before any commitment.

• Infrastructure detection & mapping

Precision varies by asset class, imagery quality and terrain, so a single headline number would be misleading. Every detection carries a confidence score, which lets you set a threshold appropriate to the decision — a planning study and an enforcement action do not need the same bar. We validate against a ground-truthed sample before rollout, and on a recent regional mapping engagement precision was 93.8% across the mapped territory.

No, and we do not need to own any. We build the intelligence layer on top of your own data, third-party data you license, or publicly available imagery. That keeps you free to change imagery supplier without rebuilding the system, and means we have no incentive to recommend more data than the question requires.

Cadence is governed by imagery availability for your region. Commercial satellite revisit is typically measured in days, drone capture runs on your own schedule, and camera feeds can be continuous. We set cadence against how fast the thing you are watching actually moves — monitoring a construction site weekly and a border corridor daily are different problems with different costs.

• Change detection & monitoring

Yes — that is the normal case. The system connects to existing CCTV, industrial and drone cameras and scales across hundreds of simultaneous feeds. Where a specific camera is positioned or specified poorly for the detection you need, we will say so rather than accept unreliable results. Where privacy, security or bandwidth make streaming footage off-site unacceptable, processing can run locally with only the resulting events leaving your network.

• Camera-based object detection

It does not have to, and usually should not. Systems can be scoped to detect conditions rather than identify individuals — missing PPE in a zone, presence in a restricted area, an unsafe interaction — with no individual tracking. We design to whatever your policy and local law require, and will tell you when a requested capability crosses a line you would rather not cross.

• Industrial & operational intelligence

It surfaces visible early indicators that typically precede failure — leaks, corrosion, misalignment, abnormal thermal signatures, unusual movement — rather than predicting from vibration or telemetry. It complements sensor-based condition monitoring rather than replacing it. The practical value is catching what a sensor cannot see and a human would only notice on a walkround that happens monthly.

By tuning against your specific scenes and defining triggers tightly before launch, and by treating false-alarm rate as a primary requirement rather than a refinement. Seasonal variation, shadow, cloud and capture angle all change an image without anything changing on the ground; suppressing that is most of the work in making change alerts trustworthy. An alerting system people stop trusting is worse than no alerting system.

A chatbot answers; an agent acts. An agent gathers data, executes multi-step tasks across your connected tools, and completes work without being prompted at each step — monitoring data streams, routing information between systems, generating and distributing reports on a schedule or trigger. The practical test is whether it can finish a task while nobody is watching.

• Agentic AI for data interaction

By defining the autonomy boundary explicitly before it touches production. Actions are scoped, higher-consequence steps require human confirmation, and everything the agent does is logged so behaviour can be reviewed and the boundary widened as trust is earned. Deploying an agent with unbounded permissions and hoping is the single most common way these projects go wrong.

Repetitive, multi-step work that spans several systems and currently consumes skilled time — gathering and enriching data, monitoring for conditions and reacting, moving information between tools, and producing recurring reports. One client's sales team was spending extraordinary cycles translating an intuitive sense of their ideal customer into spreadsheets that eroded the moment the market shifted; deriving that profile from their own pipeline data and recomputing it continuously removed the decay.

• Automated B2B lead generation

Yes. Over 80% of the data deal teams need sits in unstructured formats — multi-page filings, disparate data rooms, inconsistent transcripts — and agentic extraction pulls the figures, terms and disclosures analysts would otherwise transcribe by hand, normalising them into a consistent structure with a reference back to the source document and location. Every extracted figure stays traceable, which is what makes it usable in a decision that has to be defended.

• Autonomous financial intelligence

Edgec is a computer vision and geospatial AI company that turns satellite, drone and camera imagery into structured, queryable intelligence systems. The head office is in Lahore, Pakistan, with a US office in Lewes, Delaware, and we have been building AI systems for startups and Fortune 500 companies for around eight years. Work spans energy and resources, satellite and mapping, public sector and defense, security and monitoring, logistics, construction, industrial operations, and climate and agriculture.

• Contact us

Yes. We work with US clients from our Delaware office and deliver globally — because the primary input is satellite imagery rather than local infrastructure, geography is rarely a constraint on what can be built. Calls are booked directly on our calendar and we work across US time zones.

• Book a call

An area or asset portfolio you need visibility over, the classes of thing that matter to you, whatever data you already hold — imagery, GIS layers, asset registers, inspection photos, however incomplete — and a clear sense of which decision the output has to support. You do not need a cleaned dataset, a data science team, or a defined technical spec. If the imagery does not exist for your region, we will tell you that in the first call.

Ongoing support covers maintenance and security updates, dedicated technical support, proactive performance monitoring, continued optimisation as needs evolve, documentation and training so your team can operate the system, and strategic consultation on what to build next. The intent is a long-running relationship rather than a handover, because detection systems degrade if nobody retrains them as conditions change.

Against four groups of metrics agreed up front: performance (latency, throughput, model accuracy), operational (uptime, scalability, resource efficiency), business (return on investment, cost savings, user satisfaction) and security (data protection, regulatory compliance). Projects that only report model accuracy are usually avoiding the business question.