Empowering visionaries to turn ideas into thriving enterprises.

Embarking on the entrepreneurial journey can be formidable, even for the most determined founders. An exceptional idea alone may falter without a robust execution strategy. This is where a seasoned mentor, adept at building businesses from the ground up, becomes invaluable, steering the client towards a triumphant launch.

The Challenge

Client have ideas and initial interest from general public to build a new edutech start-up, but do not have the capacity to establish one.

The Solution

We create a comprehensive, step-by-step blueprint that guides the client through every aspect of launching their startup, from beginning to end.

The Outcome:

• The start-up was awarded with the best start-up ideas during the incubation process

Seamlessly weaving diverse data into actionable insights.

Traceable data ingestion process enables user to quickly and confidently rely on their data to support decision-making process. We helped one of the government institutions, to make informed decision concerning national security by enhancing their data ingestion.

The Challenge

Ingest unstructured data from other parties and integrate with their existing data warehouse to enrich their analysis capabilitie

The Solution

a system that reliably ingest, transform, and enrich data while continuously monitor the pipeline and infrastructure

The Outcome:

• User can quickly rely on their data to support decision making process

The ability to distinguish the risks enables the ability to better manage them.

Credit risk is one of the major risks faced by banking and financial institutions which leads to the possibility of a loss resulting from a borrower’s failure to repay a loan or meet contractual obligations. In 2018, we piloted a Machine Learning Algorithm in the biggest microfinance institution that helped improve their credit decision.

The Challenge

Since this institution is a bank that specialized in micro credit and prone to high NPL since there is no system to accurately analyze the default risks for the unbankable market segment.

The Solution

As a pilot project, we developed a machine learning powered credit scoring system and psychometric scoring to capture ‘the unbankables’.

The Outcome:

• Reduce credit failure by 50%.
• Increase potential revenue by 10%.

Time is money; timely distribution means cost-efficiency.

Choosing the most optimum route in the distribution process is critical for a logistic company. With one of the biggest logistic companies in Indonesia, we created a machine learning model aimed to improve this process and reduce inefficiency.

The Challenge

The delivery assignment process to the truck drivers in a big logistic company was done manually and takes approximately 6 hours per day. The delivery route is then determined by the drivers themselves using their knowledge which often leads to long delivery time due to traffic congestion.

The Solution

We developed an assignment management system with Vehicle Routing Problem (VRP) model to make delivery assignment more efficient as well as to estimate the distances of logistic route options and select the most optimal one.

The Outcome

• Automate delivery assignment for 200 trucks and 140.000 customers
• Reduce delivery total distance by 15%

Using computer vision to broaden your vision

One of the biggest logistic companies teamed up with Valiance to build a computer vision service to detect people in the warehouse and create a more convenient way to make an inventory of goods.

The Challenge

The company stores a wide variety of products and has a lot of staff in their warehouses. They experienced frequent goods damage and goods loss every month. Thus, they have a security concern to better manage their warehouses.

The Solution:

We built a computer vision solution using image recognition model, face recognition model, and SKU classification model to automate warehouse surveillance and inventories management.

The Outcome

• Reduced goods damage and good loss by 5% per month.

If the media control the mind of the masses, then AI can help them to read the mind too.

Understanding the reader’s sentiment towards certain topics may help the media to produce better news. We worked with national mainstream media to create an information extraction and sentiment classification system using Natural Language Processing.

The Challenge

Media produce textual data (news) on a daily basis that potentially form public opinion towards certain topics. The ability to classify the sentiment towards news produced becomes a valuable asset for the company that helps them maintain the quality of the news and set a suitable tone.

The Solution

We developed an information extraction model and applied sentiment analysis with the latest technique called Natural Language Processing to automatically analyze various news stories and turn client’s textual data into digestible insights.

The Outcome

• Correctly classify 70% whether a certain news stories have neutral, positive or negative sentiment
• Cluster and summarize multiple news automatically.

Prevent bogus transaction, minimize the loss

Fraudulent transactions are common causes for cash leakage in retail businesses. In 2020, we worked with the biggest tobacco companies in Indonesia to prevent such issues from happening through AI.

The Challenge

A leading tobacco company in Indonesia wanted to mitigate monetary loss from B2B and B2C transactions in retail businesses.

The Solution

We developed automated anomaly detection and consistency monitoring to check every transaction.

The Outcome:

• Detect 90% of anomalous transactions
• Detect 25% of possible fraudulent transactions.

Leverage big data in designing a tourism strategy that works.

As Indonesia became one of the most popular destinations for tourism, We partnered up with a Big Data company to help one of government institutions to effectively bring more visitors to Indonesia.

The Challenge

The government institution did various marketing campaigns to maximize the number of foreign tourists in Indonesia. They also conducted analysis on historical data of tourist arrivals in approximately 150 gates, public holidays, and social media sentiments towards Indonesia every month. This process required so much time and effort if done manually.

The Solution

We developed an AI-powered tourism management system to make the data analysis process more efficient and to better optimize the marketing outcome.

The Outcome:

• Automate predictive analysis for number of tourists and marketing outcome with improved accuracy.
• Reduce data processing and data analysis time from days to 3 hours.

Harnessing science & technology to improve farming productivity

Agriculture provides a livelihood to approximately 40 percent of Indonesia’s population, however challenges persist in ensuring agriculture productivity and quality towards higher-value-added commodities. In 2017, we built a machine learning model for a farmer co-op to improve farming productivity using the latest technology.

The Challenge

Identifying the crop diseases is the key to prevent the loss in the yield and quantity of the agricultural product. However, conducting manual monitoring requires a tremendous amount of work, expertise in the plant diseases, and also requires excessive processing time. Additionally, most farmers don’t understand specific medication for each plant disease they encounter.

The Solution

We built a machine learning-powered disease detection using image recognition, classification, and segmentation technique.

The Outcome:

• 95% of Paddy Crop diseases are predicted.
• Distributed in pilot project to more than 500 rice fields.
• Featured as a public showcase and reviewed by the President of Indonesia – Joko Widodo.

Optimal operations decision for optimal sales performance

The biggest car manufacturer and distributor company in Indonesia aimed to improve their sales for thousands of their dealers across Indonesia.

The Challenge

The company found they often lose potential buyers due to misallocated cars within their distribution channels.

The Solution

Valiance developed a car allocation system that predicts car demand and ensures supply availability in each dealership.

The Outcome

• Increase supply availability by 20% in aggregated distribution channel
• Prevent the loss of potential buyer by 10% across the company’s dealers