Article · AI & ML
AI-assisted programming needs an evidence trail
The valuable question is not how quickly a model writes code. It is how confidently a team can review the result.
Read insightINDEPENDENT LIFE SCIENCES CONSULTANCY
Complex data.
Clear thinking.
Confident decisions.
SAS, R and data science expertise, with the personal commitment of an independent consultant.
I’m Sai Jandhyala. I help teams build reliable analysis workflows, useful applications and a stronger evidence base for their AI.
STATISTICAL PROGRAMMINGSAS / RCDISCSHINY & APPLICATIONSAI / ML ASSURANCE
EXPERTISE, APPLIED
Specialist support.
Practical outcomes.
From a defined programming work package to a focused technical review, work directly with the person delivering it.
01 / EXPERTISE
SAS & CDISC programming
Hands-on SAS programming for clinical reporting teams that need clear specifications, reliable outputs and an experienced collaborator.
Explore service 02 / EXPERTISER & Shiny dashboards
Purpose-built R workflows and Shiny applications that bring clinical, operational and analytical questions into focus.
Explore service 03 / EXPERTISEApplication development
Small, focused applications that remove repetitive work and connect your analysis processes.
Explore service 04 / EXPERTISEReal-world evidence programming
Programming support for observational research, with explicit cohort definitions, provenance and reproducible transformations.
Explore service 05 / EXPERTISEData science & reproducibility
Practical data science with attention to the assumptions, evaluation and engineering around the model.
Explore service 06 / EXPERTISEAI & ML implementation reviews
Independent technical assessment of AI and machine-learning implementations, from data preparation to human oversight.
Explore serviceRESPONSIBLE INNOVATION
Moving AI forward.
With the right checks.
Speed matters. So do traceability, validation and human judgement. I help organisations examine the evidence behind their AI and ML implementations.
Explore AI assurance01
Define the intended use
02
Examine data and evaluation
03
Test failure and oversight
04
Prioritise practical improvements
IDEAS FROM THE WORKBENCH
Explore the latest thinking.
Article · R & Shiny
From a Shiny prototype to a trusted review tool
A dashboard is only useful when users understand the data, the calculations and the decisions it supports.
Read insightArticle · Real-world evidence
Real-world data: define the cohort before the model
Index dates, eligibility and follow-up can change the answer before a statistical model is fitted.
Read insight