Selected work
The work behind the method.
About these cases
Client names are withheld. The cases run from a recent enterprise programme back to the first back-office systems in 2012.
Energy company, Technology Leadership Services
A stalled predictive maintenance programme, live in 90 days
Situation
A $12 million predictive maintenance programme had been stalled for 14 months. The company was 11 engineers short across machine learning, DevOps and full-stack development, after a year of its own recruitment and three agency engagements.
What was done
The Solution Architect Stage was completed in four weeks, with a one-year roadmap and a fixed budget. By week eight a 14-person team was in place: six specialists and eight full-stack and back-end engineers.
Result
The first predictive maintenance model was live in production within 90 days, with no budget overrun.
- 4 weeksto an approved roadmap and budget
- 8 weeksto a 14-person team in place
- 90 daysto the first model in production
Earlier work
Four earlier pieces of work. Each one added something to the standard the teams use today.
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A durian plantation run on data
As IT & Big Data Director at Sri Walis, Chris turned a large durian plantation into a data-driven operation with a plantation management system, Internet-of-Things sensors and big data analytics. When the 2020 movement control order interrupted exports, the company had an online delivery site running within a day and was delivering in the Klang Valley two days after the decision.
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From buying traffic to keeping customers
As Facebook Ads made traffic easy to buy, the harder problem became conversion and retention. Chris built omnichannel marketing CRM and membership CRM systems that helped businesses earn more from the same number of visitors.
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A cryptocurrency exchange
In its founding year Webist was awarded the contract to build a cryptocurrency exchange, a system where security and the custody of funds were the core requirement.
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The back office behind growing companies
The first clients were direct-selling companies that needed commission, referral and membership platforms. As those clients grew, the platforms grew into the ERP back office behind them: procurement, warehouse, transportation, delivery and finance.
What they have in common
Each case began with a system the business depended on, and each one left something in the standard: the back office, security, retention, data.