Service
Keep models accurate in production
Data science, analytics, RPA, DevOps and MLOps that keep models accurate after go-live and take repetitive reporting work off your team.
What we deliver
Data, automation and pipelines around your models
We build the data work, automation and release pipelines that keep AI useful after launch, from dashboards to retraining.
Data science
Explore and model your data to find patterns worth acting on, such as promotions recommended from historical sales and promotions data.
Analytics and dashboards
Dashboards that turn operational data into KPIs, such as cross-branch KPIs and unified reporting for HQ.
Robotic process automation
Software robots for repetitive, rules-based steps, such as generating PowerDNA’s genetic test reports.
Agentic RPA
RPA combined with AI for document work that needs judgement, as in a law firm’s document handling.
DevOps pipelines
Automated build, test and release pipelines, so changes reach production safely and repeatably.
MLOps pipelines
Pipelines that retrain, evaluate and redeploy models, so accuracy holds up as your data changes.
Model lifecycle
What keeps a model accurate after go-live
Models learn from yesterday’s data. Monitoring, retraining and release pipelines keep them useful as the data changes.
ALPHV AI CRM dashboard, with features by user access level.
01
Data pipelines
Collect, clean and prepare the data your models and reports depend on.
02
Release through DevOps
Ship models and applications through tested, repeatable release pipelines.
03
Monitor in production
Dashboards track adoption and model performance, so drift shows up early.
04
Retrain with MLOps
Retrain, evaluate and redeploy models when performance slips, with improvement scoped into the build from day one.
Healthcare and genomics
Reports generated with RPA and GenAI
Report generation for genomics and medicolegal work, built for PowerDNA and a leading healthcare group.
PowerDNA, Malaysia and China, 2024
Genetic test reports, generated by RPA
RPA for genetic test report generation, alongside GenAI-personalised DNA health reports.
RPA
GenAI
Leading healthcare group, 2026
Medicolegal and insurance case reports
An intelligent engine assesses medicolegal and insurance cases and generates the report, so clinicians review a draft instead of writing from scratch.
Case assessment
Report generation
See healthcare work
How we deliver
From business case to models that stay accurate
The engineers who build your pipelines are the ones who monitor and improve them. No hand-offs.
See how we work
01
Discover
Start from the business case
Tech advisory, a data audit and AI strategy research show which data you hold, what it can support and where automation pays back.
02
Design
Design data, automation and pipelines
Choose models, hosting and guardrails, and design the data flows, RPA steps and release pipelines around your existing systems.
03
Implement
Automate and deploy
Build dashboards, RPA and MLOps pipelines into production, integrated with your core systems and third-party portals.
04
Improve
Improve continuously after go-live
Monitor adoption, review model performance and iterate, with improvement scoped into the build from day one.
FAQ
Questions about data, RPA and MLOps
Automation, dashboards and keeping models accurate after launch.
What is the difference between RPA and agentic RPA?
RPA follows fixed, rules-based steps, such as generating PowerDNA’s genetic test reports. Agentic RPA adds AI for document work that needs judgement, as in a law firm’s document handling.
How do you keep models accurate after launch?
Do you build analytics dashboards?
How do we get started?
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Let’s build what’s next
Tell us where your business needs to be. We’ll map the path to get there.