We define the overall vision of the product. All products start with a vision and whether that’s born from a market need or business need, we need to define what the product will do and who our users are. At this stage, we’re making assumptions about the market and product.
Our assumptions are just that, assumptions. We need to test and validate them against actual users. At this stage, we start user testing to validate our ideas. We could conduct online polls, user interviews, and general market research. We want to gather as much data as possible to see if users actually need this product.
Once we’ve established a market need backed by the data we’ve collected, we need to determine the business case. The main questions we want answered are: Does this meet our business needs? What are the success metrics? What’s the resource cost?
Once we have the go-ahead, we need an execution plan. The first step is to determine the minimum viable product features. This will involve collaboration between all engineering and product teams to determine timeline, resource cost, and allocation.
The execution phase is where the work gets done by all team members. With our plan in place, product managers ensure that development is smooth by hosting status updates and revising expectations if needed.
Our work doesn’t stop after product launch. We need to constantly analyze our user data to figure out pain points and enhancements. Then we restart the whole process.
HCEP is a declarative, zero-coding Flink based general-purpose analytics platform aimed at supporting subject-matter-experts. The application allows a user to build a blueprint of the data-pattern they’re looking for, and our system will continuously monitor streaming data to look for the desired events in real-time.
The Speech to Text platform supports detectives and law enforcement personnel. Partnering with Circinus, we’ve provided auto transcription capabilities to major law enforcement agencies to analyze transcriptions and detect threats before they happen. Multiple violent incidents have been detected and mitigated with this platform.
Poqeta is an automated vending machine platform currently in development. The main use of our platform works similarly to Amazon Lockers, but with a dedicated mobile application that allows simple ordering / pickup by the user. The platform is targeted towards restricted items such as alcohol and cannabis with possible applications in the medical industry. The lockers themselves employ biometric hardware as well as machine learning for image and facial recognition for age verification.
Big Data Analytics Suite (BDAS) provides recruiters and other HR professionals advanced analytics about candidates. The goal is to reduce the time to hire for job positions and ensure that the candidate selection and filtering is streamlined to minimize cost to hire.
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