More and more, companies are learning how to use data to their advantage. Never has it been more important to capture, analyze, and utilize your businesses data — doing so can increase your competitive advantage, retain corporate knowledge, and optimize performance, creating new opportunities for growth.

Safire is a revolutionary platform that uses accumulated project information to inform critical decision-making in your next project — modernizing and stabilizing your acquisition process. Safire collects massive amounts of data from past projects, then uses Machine Learning (ML) to access, manage, and translate it into actionable insights and prompts that inform your new projects at key stages.

Through this cutting-edge technology, Safire collects past project information to compare to current and future projects, recognizing mistakes and providing the intelligence necessary to make faster, better-informed decisions incorporating lessons learned. This not only saves time and money, but also retains your corporate knowledge, minimizing the effects of turnover and attrition.

Safire’s ML and NLP creates unique relationships from your requirements. By comparing the relationships it generates from your requirements to the data and relationships generated from previous efforts, Safire can provide recommendations that fill gaps and guide users to complete project requirements with greater accuracy and speed.

In addition to reducing the risk of cost and schedule overruns from faulty, incomplete requirements, Safire also facilitates the creation of a Minimum Viable Product (MVP) that can be tested and changed early, mitigating and managing risk. Future risk is mitigated as well, as Safire continually updates its ML and NLP, getting smarter with every use.

I. Requirement Production

Safire uses a Requirement Analysis Component to provide an organic interface that compliments the contractors approach by guiding users to produce a complete and accurate work package or requirement. To do this, users enter requirements into Safire, where they are checked against a database of previous requirements.

II. Analysis & Recommendations

Safire analyzes and translates those requirements, and makes recommendations based on its ML database if there is any incomplete or missing information. The user can choose to add or complete requirements based on these valuable ML-generated recommendations. This guides the user to complete requirements and generate accurate work packages.

III. Agile Sprint Package Delivery & Updates

Once requirements are complete, Safire then creates agile sprint packages that can be developed quickly and inexpensively. This allows users to create a minimally viable product that can be tested and changed early and before proceeding, embodying the "Fail fast, Fail Early" concept.

IV. Correlation Engine Updates

Upon completion and validation of the requirements, Safire learns from the whole experience and updates its correlation engine so that it maintains best recommendations for future applications. With each use, Safire becomes a more powerful tool.