Brightfield distinguishes itself with four special characteristics.
Business experience is combined with data science.
Understanding business models before applying deep learning analytics.
Open source analytical programs for low cost initial investment.
Engaging your staff in the analytical development and training.
Brightfield is made up of people who have business, data science and analytics experience. We have developed deep learning forecasts for application to business plans and to valuations for mergers and acquistions as well as day-to-day operations capability. We apply proven, best-in-class, deep learning analytics together with traditional statistical analysis to deliver you accurate, actionable forecasts.
We begin with a documented understanding of your business model and its critical components. Without a model to test, analytics is just manipulating data without respect for its usefulness. It is our first principle that we understand your business.
We use open source analytical programs maintained by large corporations and international independent consortiums. We do not burden you with large licensing commitments and long term support requirements. The code can be maintained by any Python adept programmer on your staff. We can provide a graphical user interface to the analytical programs you retain. We do recommend a commercial visualization program since it seriously reduces programming investment.
Using open source programs enables a simple and less formal on-site training regimen. Nothing in our solutions require special knowledge of a unique vendor program. Your staff is encouraged to work side-by-side with us in all phases of the forecasting solution.
The initial proof of concept estimates the potential return on investment ("ROI"). The ROI is easier to justify with the use of open source programs. Having cleared this relatively low-cost hurdle, the ROI is potentially a high multiple of the development costs.
Our ideal clients include vendors in technical, industrial and construction industries that deploy active field operations and multiple tier distribution channels. We are experienced with products requiring continuous engineering investment to maintain competitiveness and in-depth customer support to maintain customer relationships.
Forecasting errors injects large risks into both the financial statements and operating budgets. These risks can be mitigated by using business modeling together with deep learning analytics for better forecasts.