Murad had an idea: Start connecting civil defense organizations in different towns so they could better communicate about impending attacks. He mentioned the idea to Jaeger’s friend. Jaeger and Murad soon met for coffee, and Jaeger offered him a job. It came with low pay, long hours, and no job security. Murad was all in.

With a team in place, the group was ready for the most arduous startup task: fund-­raising. Jaeger went to VCs, who told him the idea was great—but would never generate billions. They pointed him toward social-impact investors, who told him the idea was great—but they didn’t invest in the “conflict space.” They suggested foundations—which said they didn’t invest in for-profit businesses and sent him to VCs.

Screw it, thought Jaeger. In late 2015, the cofounders put together what they could from their personal bank accounts and managed to get some funding from an angel investor Levin knew. It was time for their startup, which Jaeger had named Hala Systems, to try to make a business out of saving lives.

Murad holds a Syria civil defense warning sign that reads “DANGER! UNEXPLODED ORDNANCE.” Rena Effendi Once Sentry went live and proved effective, no one on the Hala staff was willing to take a break. Dave Levin remembers putting in 90- and 100-hour workweeks. Rena Effendi

During World War II, British farmers and pub owners in rural areas along the flight paths of German warplanes would phone ahead to big cities, warning them when the Luftwaffe was on the way. Seventy years later, Syrian civilians set up a similar ad hoc system. People who lived near military bases kept watch; when they saw a warplane take off, they used walkie-talkies to notify other people, who would contact others, spreading the word up the chain. Many of the participants were members of Syria Civil Defense, known as the White Helmets, who also served as rescue workers. But the process was spotty, unreliable. There was no systematic way for observations to come in and warnings to go out.

Jaeger thought that with the right technology it should be possible to design a better system. People were already watching for planes. If Hala could capture that information and connect it with reports of where those planes dropped their bombs, it would have the foundation of a prediction system. That data could be plugged into a formula that could calculate where the warplanes were most likely headed, taking into account the type of plane, trajectory, previous flight patterns, and other factors.

The Hala team started reaching out to the people who were monitoring the planes, including the White Helmets. At the same time, the team hacked together the first iteration of a system that would analyze data from the aircraft monitors, predict where the planes were headed, and broadcast alerts to people under threat of attack. Jaeger and Murad sketched it out, eventually filling up a notebook and using napkins to get the rest down. Jaeger says at first the system was just a bunch of if/then statements, a logic tree, and an Android app.

Basically, if someone saw, for example, a Russian-built MIG-23 Syrian warplane take off from Hama air base, then entered that information into the system—now called Sentry—it would issue a warning via social media with predictions about when an attack could be expected to hit a targeted area. It might estimate that the jet could be headed for the town of, say, Darkush with an ETA of 14 minutes, or Jisr ­al-Shughur in 13. When more people reported a specific plane as it flew over different locations, Sentry could then send more specific and accurate warnings directly to people in threatened areas.

How the Sentry System Works Hala’s warning system relies on both human observers and remote sensors to collect data on potential air strikes. The startup is working toward making its network more autonomous, the better to save lives. —­ Andrea Powell — 1. When observers near government air bases spot warplanes taking off, they enter the type of aircraft, heading, and coordinates into an Android app, which sends the info to Hala’s servers. — 2. Sensor modules placed in trees or atop buildings collect acoustic data, which helps Sentry confirm the type of plane, its location, and flight path. — 3. Software crunches all the data and compares it to past attacks, predicting the likelihood of an air raid, as well as when and where it might occur. — 4. If the potential for an air strike is high enough, the system generates an alert that’s broadcast via social media. Hala has also set up air raid sirens that Sentry can activate remotely. The warning system now gives people an average of eight minutes to seek shelter. — 5. Using a neural network, an automated system continuously scans Facebook, Twitter, and Telegram for posts that might indicate air strikes.

As the team gathered data, they constantly tweaked the formula. Everything was trial and error. “One of the things we learned early on was that our model for predicting arrival times was super aggressive,” Jaeger says of Sentry before it was released to the public. “It had planes arriving much faster than they actually did.” They couldn’t figure out what was wrong. Then they talked to a pilot who had defected from the Syrian air force. “Oh, that’s not how we fly that plane,” the pilot told Jaeger when the team showed him the system. The program assumed jets would always fly at maximum cruising speed, but the actual speeds were much lower, most likely to conserve fuel. “When we fly that plane, we fly it at exactly these altitudes and speeds at these intervals, using these waypoints,” the pilot said. With that information, the Hala team was able to fine-tune Sentry’s predictions to be accurate to within 30 seconds of the warplane’s arrival.

Precision was essential, Murad says. If Sentry went live too early and was inaccurate, civilians wouldn’t trust it, and it would fail to catch on. But Murad was eager to get it out there. Every day it was in development was another day people could be dying. At this point, part of his job was to watch videos of air strikes and look for eyewitness accounts on social media and in news reports to verify the information they received from people on the ground. Day after day, from Hala’s office, he monitored the aftermath of the strikes—the dead, the wounded and the dying, the bodies, the blood, and the maimed limbs. “You cannot stop crying, you can’t stop yourself,” he says, “and you can’t get used to it.”

Even though the Hala team was still getting by on scant funding, they managed to hire three more Syrians to help Murad look at the video and social media evidence and match it against Sentry’s predictions. But it took hours to verify the trajectory of a specific plane from air base to bombing site. And some days there were dozens of strikes. The new staffers couldn’t keep up. So the team figured they needed to automate the process. Jaeger hired engineers and researchers to develop software that, with the help of a neural network, could search Arabic language media for keywords that would help confirm the location and timing of an air strike. More data on more air strikes meant better information and better predictions.

As they were working to get accurate data, they also needed a way to get the warnings out to civilians. Murad wrote scripts for Telegram, Facebook, and Twitter, as well as the walkie-talkie app Zello.

Day after day, from Hala’s office, Murad monitored the aftermath of the air strikes—the dead, the wounded and the dying, the bodies, the blood, and the maimed limbs. “You cannot stop crying. And you can’t get used to it.”

On August 1, 2016, Sentry was ready to go live. The team started small, launching it in part of Idlib Province, which was getting hit hard by air strikes. They reached out to Syrian contacts and shared the news on social media. Volunteers passed out flyers. “Within a day and a half,” Jaeger says, “we got a testimonial video from someone who said, ‘My family is alive because I logged in and I got this message and I moved from my house. The house got blown up, my neighbors got killed.’ ”