Reknew AI,
Building trusted data foundations for scalable AI
Reknew AI — AI Case Study
An enterprise data and AI modernisation platform that builds trusted data foundations, deploys AI agents across workflows, and automates business processes through contextual intelligence.
Three numbers that
tell the story upfront.
Faster time-to-hire
An enterprise data and AI modernisation platform that builds trusted data foundations, deploys AI agents across workflow…
Improvement in DEI metrics
An enterprise data and AI modernisation platform that builds trusted data foundations, deploys AI agents across workflow…
Reduction in attrition
An enterprise data and AI modernisation platform that builds trusted data foundations, deploys AI agents across workflow…
Before we got here,
here's what wasn't working.
Reknew AI helps enterprises build a modern, trusted data foundation for scalable AI adoption. Using proprietary Context Engineering™, the platform aligns data with real business context — then provides accelerators to rapidly deploy AI agents, automate enterprise processes, and integrate AI with existing systems. It supports data modernisation with semantic layers, knowledge graphs, and vector stores.
We confirmed every challenge with the team before writing a line of production code. The right column lists the pain points we tackled head-on and how we approached each one.
What wasn't working
BEFOREIntegrating with decade-old SAP HR systems with no modern API
Built an SAP BAPI adapter using RFC calls via the PyRFC library, exposing a clean REST interface to the AI layer.
Ensuring AI recommendations are auditable for compliance
Every AI decision is logged with the prompt, model version, and output score, stored immutably in PostgreSQL for audit trails.
Don't over-engineer.
Solve what matters, ship fast.
The instinct on complex projects is to rebuild everything from scratch. We pushed back. A full rewrite often means months of regressions while engineering rebuilds features that were already working.
We mapped the existing workflow against actual usage. The result was clear: a small set of features accounted for the majority of user time. We rebuilt the core, shipped fast, and extended outward.
On the technical side, we made clear decisions early and held them. The right column shows what we chose, and why.
4 decisions we made early
CHOSEN / REJECTEDContext Engineering™ — how we built it
Custom Context Engineering™ — built for this product's exact needs and data model.
Generic SaaS tool that would require heavy workarounds and compromise the user experience.
AI Agent Accelerators — how we built it
Custom AI Agent Accelerators — built for this product's exact needs and data model.
Generic SaaS tool that would require heavy workarounds and compromise the user experience.
Enterprise Process Automation — how we built it
Custom Enterprise Process Automation — built for this product's exact needs and data model.
Generic SaaS tool that would require heavy workarounds and compromise the user experience.
Data Modernisation — how we built it
Custom Data Modernisation — built for this product's exact needs and data model.
Generic SaaS tool that would require heavy workarounds and compromise the user experience.
4 features that
define the product experience.
Every feature was scoped and shipped with the same priorities: speed, reliability, and staying out of the user's way.
Context Engineering™
Proprietary methodology that aligns enterprise data with real business context to make AI outputs accurate, relevant, and trustworthy.
AI Agent Accelerators
Pre-built accelerators to quickly deploy AI agents across business workflows, cutting time-to-value significantly.
Enterprise Process Automation
Automates enterprise processes by integrating AI with existing systems, reducing manual effort across operations.
Data Modernisation
Supports data modernisation with semantic layers, knowledge graphs, and vector stores for a future-ready data foundation.
What changed,
measured against the day-one baseline.
We benchmark before we touch anything, logging every metric below against its state before we wrote a line of production code.
Want to build
something like this?
We ship fast. Talk to us about your product idea and we'll tell you exactly how we'd approach it.