Records.
ERP records transactions. B.I Visualises performance. Planning tools project scenarios. A.I explores patterns. But none of them owns the full cycle: forecast the exposure, assign the action, verify the outcome, score the decision, and carry the learning into the next cycle. Quantos was built for that missing layer.
The ERP recorded stock. BI showed movement. But no system converted falling velocity into a forward capital exposure with a date and owner. Quantos identifies the moment stock begins turning into financial risk.
Forward projection computes risk windows at 7, 14, 30, 60, and 90 days — from observed operational movement, with full provenance.Working-capital risk rarely announces itself as one obvious event. It forms across receivables, payables, inventory, vendor dependence, and sales momentum. Quantos reads the pattern as one exposure instead of five disconnected reports.
The output is not another chart. It is a named risk, priced in capital terms, linked to the source signals, and assigned before the gap becomes visible in monthly review.Most margin leakage is not caused by one bad decision. It is caused by unscored decisions repeated across cycles. Quantos records the decision, watches the outcome, and adjusts future scoring so the same mistake does not continue under a new label.
Every decision becomes part of institutional memory: what was predicted, who acted, what happened, whether the action worked, and what the system should do differently next time.A system that does not remember its own forecast cannot know whether it was right. A system that does not measure the action cannot know whether the decision worked. A system that does not score the outcome cannot improve. That is the open-loop problem.
Quantos closes the loop by connecting forecast, risk, action, outcome, score, learning, and self-correction into one auditable operating cycle.Exposure values should be read as operational exposure surfaced for decisioning, not as claimed recovered revenue.
Quantos ingests operational truth, rolls it into signals, forecasts future exposure, quantifies risk, assigns action, records outcome, scores the result, learns from the cycle, and self-corrects within governed boundaries.
The architecture is built as a forward pipeline and a feedback pipeline. Source systems feed trusted operational truth. Quantos converts that truth into signals, forecasts, risks, and owner-linked actions. When the horizon matures, the outcome is captured, scored, learned from, and carried into the next cycle under a governed control envelope. The SVG below explains the public operating principle without exposing private implementation mechanics.
Data enters through one disciplined door. Every write is schema-isolated per tenant. A contamination gate — a preflight check — refuses any write without a valid tenant binding, even if an application bug tried. The ingested fact lands in an immutable truth table, hashed on arrival.
For executive, financial, operational, and regulated decisions, a system must be reproducible. Quantos is designed so the same input and the same model state produce the same output, with provenance for audit and review.
Quantos records the forecast at the time it was made, then compares it with the realised outcome when the horizon matures. The result becomes part of the system’s institutional memory.
| Cycle | Industry | Signal | Forecast | Actual | Error |
|---|---|---|---|---|---|
| #4,702 . 21d ago | FMCG | Frozen-range write-off risk (SKU cluster) | ₹4.20 Cr . in 18d | ₹3.92 Cr . day 21 | -6.7% |
| #4,687 . 28d ago | Pharma | Batch-expiry institutional channel | ₹1.85 Cr . in 30d | ₹1.98 Cr . day 30 | +7.0% |
| #4,661 . 35d ago | EPC | Milestone slip . receivables ageing | ₹8.40 Cr . in 45d | ₹8.12 Cr . day 46 | -3.3% |
| #4,652 . 38d ago | Mfg | Margin drift — silent, 3 consecutive cycles | ₹2.10 Cr . in 60d | ₹2.41 Cr . day 60 | +14.8% |
| #4,640 . 42d ago | FMCG | Cashflow composite . AR × AP co-movement | ₹3.10 Cr . in 9d | ₹2.98 Cr . day 10 | -3.9% |
| #4,619 . 51d ago | Retail | Regional demand reversion . seasonal | ₹0.95 Cr . in 14d | ₹0.72 Cr . day 14 | -24.2% |
| #4,604 . 56d ago | Mfg | WIP drift . quality rework | ₹1.28 Cr . in 30d | ₹1.22 Cr . day 31 | -4.7% |
Choose an industry and revenue band to estimate the scale of exposure that often hides inside inventory, margin, working capital, supplier, and execution decisions.
These are Category-Published Averages of inventory write-offs, margin drift, working-capital drag and cash surprises — the things open-loop systems let through because nobody is priced-and-owned for them.
A Quantos decision is not a static alert. It is a recorded chain: signal, risk, owner, action, outcome, score, and learning. That chain becomes auditable institutional memory.
Every company runs on reports. Reports tell you what happened last month. They don't tell you what's about to go wrong, who owns it, or whether the last decision actually worked. So the same mistakes keep happening — a milestone slip that costs ₹40 Cr, a batch that fails spec three weeks before launch, a margin that drifts quietly for six months before anyone notices. Nobody connected it. Nobody remembered. Next quarter, the report looks almost the same. So does the mistake.
AI doesn't fix this. AI gives a different answer every time you ask it the same question. It's useful for exploring ideas — useless when a finance team needs the same number in January, March, and June to defend a decision to the board. The moment the answer is allowed to drift, the trust breaks. And once trust breaks, the system stops getting used. This is the documented failure mode of every enterprise analytics deployment.
Quantos closes the loop. It reads your operational data continuously, sees what's about to go wrong before it does, and prices it — not as a vague alert, but in rupees, with a date, with the name of the person who owns it. When that person takes action, Quantos watches. When the outcome arrives, Quantos measures it against the forecast. When the measurement shows the system was wrong, Quantos adjusts itself — automatically, within bounded discipline, before the next cycle begins. No one reconfigures it. No one retrains it. It gets sharper every cycle because the loop is closed.
Same question, same answer, every time — so a CFO can defend any number three years later with the same inputs that produced it. Your data is isolated at the database level, so what belongs to your tenant never touches another's. Every number carries its own provenance — where it came from, which engine produced it, which version of the system. This is not a better dashboard. It is not another planning tool. It is the category of system every business has needed and no one has built — until now.
The reason this category did not exist until now is structural. ERPs were built to record transactions. BI tools were built to visualise them. Planning systems were built to project them forward. AI platforms were built to explore patterns. None of them were built to check whether the decision that came out of the data was correct — because that would require the system to remember what it said, measure what happened, and adjust itself. A loop, not a line. The software industry spent forty years perfecting the line. Nobody closed the loop. Quantos did.
|
Enterprise Stack
SAP . Oracle . Microsoft
Tableau . Power BI . Qlik . Cognos |
Planning & AI
Anaplan . o9 . Blue Yonder . Kinaxis
IBM Watson . SAS . C3.ai |
Closed-Loop Intelligence System
|
|
|---|---|---|---|
| Ingest | Yes | Yes | Yes |
| Rollup | Partial | Yes | Yes |
| Forecast | No | Partial | Yes |
| Risk | No | No | Yes |
| Action | No | No | Yes |
| Outcome | No | No | Yes |
| Score | No | No | Yes |
| Learning | No | No | Yes |
| Self-Correction | No | No | Yes |
| Continuous Intelligence | No | No | 60s |
Native . Possible with engineering . Not the tool's job
This is not a criticism of any system or the people who built them. It is a structural reality: the category that closes the loop did not exist. What no system was designed to deliver is what the user deserves from their own data — a closed loop that turns output into intelligence, and intelligence into better judgement, every cycle.
Quantos is designed for senior operating, finance, technology, and governance leaders. Every output carries provenance. Every tenant is structurally isolated. Every cycle is reviewable after the fact.
Each tenant's data sits in a physically separate database schema. No shared tables, no shared rows. A missing filter cannot leak data — there is nothing to leak into.
Same input, same output, always. Every number carries a provenance structure: engine, table, method, version. Reproducible three years later, from the same inputs.
When integrity is compromised, the system refuses cognition with a defined response and discloses no internals. Uncertainty is treated as failure, not as permission.
Every mutation, every cognition event, every weight adjustment, every self-correction — recorded in an immutable event log. Queryable by time, tenant, function, or signal.
Quantos is presented as a working closed-loop system: fixed-scope deployment, operational-data ingestion, signal generation, decision ownership, outcome scoring, and audit review.
In the first six weeks, Quantos surfaced ₹42 Cr of distributor inventory exposure the monthly review had been reporting as "healthy." Eight SKUs were reclassified, three distributors were renegotiated, and the write-off that would have hit Q3 was pre-empted. Payback inside the pilot.
In a briefing, Quantos is evaluated on one simple question: can the system convert your operational data into forward risk, named action, measured outcome, and learning?