Vendor Newsmaker Q&A Tilak Kasturi

Feb. 10, 2017
With the help of predictive analytics, Predii’s software is designed to determine which parts and equipment will fail, as well as when and why. Technicians can then address these issues before they become problems, said CEO Tilak Kasturi.

Established by CEO Tilak Kasturi and headquartered in Palo Alto, Calif., Predii, (pronounced pred-ee) has a branch in Boulder, Colo. and offices in Belgium and India.

He describes the company’s Predii Repair Intelligence as a proprietary system for guided repairs and preemptive maintenance. “We’re committed to negating the need for unplanned repairs by working hand-in-glove with service networks, fleets and dealers,” Kasturi says.

With the help of predictive analytics, Predii’s software is designed to determine which parts and equipment will fail, as well as when and why. Technicians can then address these issues before they become problems, he said.

“In collaboration with Snap-on, we’ll integrate our software and knowledge base with your systems and data so that you can start to accrue and apply new insights without having to learn a new interface. Predii customers report a measurable return on investment of 20 times their initial investment upon implementation of our solution. They achieve these results by reducing their labor costs, accelerating their diagnoses and streamlining their repair processes,” says Kasturi, who recently answered a series of questions posed by Aftermarket Business World:

Q: What are the training requirements, if any, for business owners and technicians using your system?

A: The end-user often doesn’t need any special training because our goal is to make their lives less complicated and reduce the need for them to retain otherwise useless, voluminous information in their brains. We’re not saying we don’t want the user to think, we just want them to be focused on what humans are good at and, from what we understand, the parts they enjoy – fixing things.

Q: How can a repair shop owner benefit by using your system?

A: The benefits are reduced time per-repair-case and increased accuracy. The knock-on effect of this is that they get more work done in less time and at better margins. We’re squeezing all corners of the iron-triangle at once – the exact thing conventional wisdom tells us can’t be done.

Q: Does a shop owner have to add additional equipment to utilize this?

A: It’s a product that Snap-on sells in the form of hardware diagnostic tools and/or shop management software. Many shops will already have the right hardware to make use of it, so it’s just a matter of licensing the tools from Snap-on.

We’re just beginning with the ability to quickly diagnose and form prognostic predictions, but the sky is now the limit with that data – we believe that in the next few years we’re going to see wearable diagnostic equipment (AR glasses, etc.) as well as natural-language based AI assistants that technicians (and even consumers) can speak repair and maintenance questions to and receive voice guidance – and this will then start to lead to dynamic ordering systems and such.  

Q: How does your collaboration with Snap-on improve your system?

A: Snap-on is a dream partner and customer to work with because they have an unprecedented database of vehicle service knowledge. There’s nothing else like it. How does it affect our system? It helps us train our platform. Any form of Artificial Intelligence is only as good as the training it receives. As our systems gets smarter, technicians are able to continuously get the latest and greatest repair strategies for ever changing vehicle year, makes, model and engines types.

We are very proud to be powering products for one of the leaders in the automotive market.

Q: How does your system assist technicians in diagnosing problems and completing repairs in a timelier manner?

A: Up until recently a technician has relied on a few years of trade school and/or apprenticeship experience, and perhaps a few years of workshop experience. Sometimes they will get easy problems (e.g. bulb not working). However, in most cases there is a process of diagnostic problem solving – testing systems, sub-systems and components systematically to find a fault, followed by testing fixes and replacements to find a solution. If they aren’t sure what to do they might pore over OEM manuals or call other mechanic buddies for ideas. So each time they see a new problem they are doing guesswork, process of elimination, and using various available diagnostic tools.

Now, imagine if we took the collected wisdom of millions of technicians, who have documented every labor operation, every part and vehicle they ever worked on since modern cars existed. We are basically taking all this data and making it usable by the technician. We’re not aiming to replace the technician; we’re just making their tools smarter.

Q: Does your system link to parts distributors for ordering parts?

A: Our product doesn’t have a “UI” (user interface) per-se. It’s simply a powerful system processing platform for big data and making that data usable in real world scenarios. Our software makes it possible to know what is failing or likely to fail, then predict the most likely solution (either labor operation or part replacement). Now having said that, this could easily be extended to include a feature that obtained quotes and brokered the orders for parts.

To implement this kind of feature, we have development partners that can seamlessly extend the functionality of an existing application, or build an application and launch it on top of our foundational data. We actually really think this is a no-brainer of a feature and think it’s ripe for a forward-thinking manufacturer to launch.

Q: Should parts distributors be using your system to streamline parts-fulfillment? Can it track inventory, etc.?

A: We don’t have an inventory management feature in production (yet), however we see a very good use-case for it. It would take the right kind of customer, with the right vision, but we believe that in any scenario where a distributor has uncertain demand, our software would make it much easier to predict what parts will be useful and in what quantities. This would be based on known parameters like the types/numbers of vehicles in service, season, etc. We need those parameters to analyze for insights – so again it all comes back to having the data.

Q: Is your system also applicable to retail parts stores for marketing parts to do-it-yourselfers?

A: We could make recommendations for repair operations and parts based on the fault description, or fault codes if known. This kind of data could also potentially be licensed by someone interested in building this kind of application, so they would just need to come up with a good UI and then create some hooks into an e-Commerce system that would handle the order and fulfill the process.

Q: How does your system improve operations for fleet managers?

Fleets are a slightly different beast from aftermarket service. A fleet is mission-critical for the business that runs it and having vehicles off the road is simply sunk cost. Our goal working with a fleet is to keep all vehicles on the road and 100 percent productive at all times – or as close as “machinely” possible.

A: A fleet takes their service history in whatever shape or form it exists; normally a huge database of both structured and unstructured data, natural and unnatural language. We use our repair intelligence software to ingest all of this data and make it usable to solve problems. These would be improved further by additional data incoming from the fleet in real-time, vehicle sensor data.

Our software makes it possible to predict, with a high degree of accuracy, when and what issues will occur with every individual vehicle. We can then circumvent the faults by repair or replacement in advance – a far more economical outcome than having breakdowns on the road, unexpected downtime of assets, missed deliveries or rental opportunities, etc.

Q: Does your system assist with processing warranty claims and insurance claims?

A: Insurance is a slightly different beast because you’re dealing with accidents, theft and other non-repair issues. We would only touch this peripherally (i.e. in the repair process) but we see warranty as our strong suit. We know that warranty costs are huge, and a large part of this is simply because you need to throw bodies at the process: High-value bodies doing relatively low-value work.

We can simply improve the ratio. Let’s say 75 percent of warranty claims are, statistically, uninteresting. On any given year, make, model and engine there will be inevitable “standard” faults that it will experience within certain age/mileage parameters. The first economy we can create is to eliminate most of the human resource cost to deal with these. Next, we come back to the fault resolution. We can fast-forward the repair process and get the vehicle back in service. It’s not just the time that’s important here – we’re hearing from OEMs that the customer experience and satisfaction is super-important to them. The lifetime value of a customer is much higher than the one-time warranty fix. So using our repair intelligence system here is another win.

Q: How does your system addresses telemetry issues?

A: In the case of telemetry, let’s say we’re talking about connected vehicles or fleets. These cars can currently report certain information to the “cloud.” Currently, manufacturers and fleet operators are collecting data such as speed, mileage, sometimes braking and acceleration, and a few basic vehicle sensor inputs in some cases. However, the question is where is all this data going and why? In fact, a lot of it is not really being used effectively yet. And up to now a lot of valuable data also isn’t being collected because there hasn’t been a use for it.

By bringing in a wider range of sensor inputs, we can create a much richer set of outcomes for the consumer and appropriately guiding the consumer to a repair shop. As with fleets, we have a lot of scope around predictive and preventative maintenance. Benefits include reliability, performance and of course safety. The more data we collect the better our predictions get

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