Your result is always right - the week after it needed to be.
The signals move in real time. Your process moves in weeks. That gap is where the cost lives. We close it - and it's not the tooling that changes.
Abhishek - Founder, DataTheta
Enterprise Data & AI Architecture · 20+ yrs
"Every stalled AI programme I've reviewed had a brilliant team and a data foundation built for dashboards, not decisions. That's a fixable architecture problem - not a talent one."
40+
AI systems in production
8 wks
To first live outcome
68%
Avg. remediation time cut
4
Regulated industries served
The gap your reporting can't close.
The signals that drive outcomes move hourly. The process that acts on them moves weekly. That gap is where cost accumulates, silently.
WHAT ACTUALLY MOVES THE OUTCOME
- Live inventory levels
- Inbound demand signals
- Supplier lead-time shifts
- Production yield rates
- Logistics & freight data
DATATHETA
CLOSES THIS
GAP
WHAT YOUR PROCESS RUNS ON
- Monthly S&OP cycle
- Weekly consolidated report
- Quarterly budget review
- Manual ERP extraction
Outcomes leaders see – typically within a quarter. Not a new process. The signals your existing process is missing, delivered at the speed decisions actually need them.
- // THE ROOT CAUSE
The data gap hiding inside your system of record.
Your core system was designed to record what happened – not to tell you what’s about to. That’s where operational AI quietly fails.
- 01
Your S&OP runs on last month's reality
By the time the consolidated view reaches planning, the inventory position has already shifted. Decisions are made on data that describes where you were, not where you are.
- 02
Risk is visible only after it's already cost you
Supplier lead-time changes, demand spikes, and logistics delays are buried in system extracts that arrive days after the operational window to act has passed.
- 03
Forecasts optimise for the model, not the decision
Accuracy numbers look good in testing. But the model's training data reflects a reporting cadence - not the real-time environment where the cost decision is made.
- // PROOF
- Global Manufacturing · Supply Chain
A manufacturing client, one quarter in.
We aggregated live inventory positions, inbound supplier signals, and logistics delay feeds into a single decision layer – updated every four hours instead of every week. The insight was not a better algorithm. Getting the right data to the right decision at the right time is an engineering problem, not an algorithm problem. Within one quarter, the planning team was acting on real-time signals for the first time.
+34%
forecast accuracy improvement
−40%
reduction in excess inventory cost
−2 wks
removed from the planning cycle