Data Systems That Don’t Break at Scale
We design and rebuild data pipelines, APIs, and analytics foundations for operators who need speed, accuracy, and control.
BOOK A STRATEGY CALLWe design and rebuild data pipelines, APIs, and analytics foundations for operators who need speed, accuracy, and control.
BOOK A STRATEGY CALLReports don’t match reality
Pipelines fail silently
Manual fixes never end
Decisions lag behind data
High-throughput batch and streaming systems built for correctness and observability.
FastAPI-based services with strict validation and performance guarantees.
Clean, modeled datasets leadership can trust.
Architecture reviews and scaling roadmaps that prevent expensive rebuilds.
Designed and stabilized daily operational and financial data pipelines for a poultry farm business handling continuously updated production and finance data, including feed costs, mortality metrics, vendor transactions, and manual Excel inputs.
The existing setup relied on fragmented spreadsheets and nightly manual corrections, leading to inconsistencies between operations and finance and no reliable single source of truth at the daily level.
Implemented validation and reconciliation logic with automated daily rollups to align operational metrics with financial records and eliminate recurring manual fixes.
As a result, daily data became reliable for decision-making, feed conversion ratio improved by 24%, litter quality by 45%, and the profit-to-revenue ratio increased significantly through tighter operational control.
We identify structural failure points across pipelines, APIs, and data models.
We define architectures that scale without accumulating technical debt.
We build, fix, or guide execution with zero fluff and full accountability.
Shubham Singh
MSc Data Science — University of Nottingham
The vision is to build data systems that remain correct under pressure — systems that scale in volume, complexity, and decision impact without accumulating hidden failure modes. SrS Logics exists to eliminate fragile data foundations and replace them with architectures that leadership can trust as a single source of truth.