Everything you need to operate Preemptify AI.
A complete operator's guide — feature walkthroughs, math behind every metric, an exhaustive glossary, and an FAQ. Stuck? Press the Ask Preemptify button bottom-right at any time.
Overview
Preemptify AI is the AI Predictive Risk & Balance Sheet Intelligence platform for retail wealth, corporate treasury, trade finance and mutual fund books. Instead of waiting for market events to materialise, the Neural Engine maps the current financial environment directly against the client balance sheet, identifying structural vulnerabilities and capital mismatches before they materialize — and fuses central-bank + treasury signals with proprietary balance-sheet math to surface preempted product recommendations, each one citing the source headline and the local math rationale.
It ships with two operating tracks (Retail Wealth and Corporate Treasury), an admin console (Policy Compiler, Signal Calibration, Schema Aligner, Product Catalog), and a built-in backtest engine for evaluating strategy variants.
Getting Started
1. Sign in at /login using one of the demo identities below. Clicking the demo card auto-fills + auto-submits.
# Demo identities executive_demo / executive_demo → Retail + Corporate access compliance_admin / compliance_admin → All access including /admin*
2. Land on the dashboard at /sandbox. Switch between Retail Wealth and Corporate Treasury in the top bar.
3. Click any row in the customer/corporate list to open the detail panel — metrics, decay clocks, NBA cards, AI Analyst chat, Smart Analysis, and the goal/life-event triggers.
4. Drill into admin tools via the top-right links: /admin (Policy Compiler), /admin/signals (Signal Calibration), /admin/aligner (Schema Aligner), /admin/products (Product Catalog).
The Four OS Modules
Four standalone Industry Operating Systems. Each is licensable independently. All four interoperate on one cryptographic audit spine with AsyncLocalStorage tenant isolation and a unified PII-redaction boundary.
Banking OS
The flagship balance-sheet module. Retail and corporate exposures on one chassis, wired to the cryptographic audit ledger.
Lending OS
End-to-end loan lifecycle. AI from application to recovery with a prompt-driven product catalog and JSON rules engine.
Insurance OS
Cat-aware, IRDAI-aligned. AI from quote to claim to revival with persistency cohort dashboard and NatCat stress simulator.
Wealth OS
Mandate-locked, drift-free. AI from suitability to allocation to rebalance with a live What-If rebalance simulator.
The sections below dive deeper into Banking OS internals — retail wealth, corporate treasury, NBA mechanics, signal preemption, goals, backtests, schema aligner, audit ledger and admin tools. Lending / Insurance / Wealth deep references live inside each module's /help tab.
Retail Wealth Pipeline
The retail pipeline tokenizes individual customers and surfaces preemptive recommendations across savings, lending, insurance, and investment products.
Key metrics
- Liquid Cushion
- Checking + savings + short-tenor term deposits. Foundation of the decay clock.
- Decay Clock
- Days until the cushion depletes at current burn. Emerald > 180d · amber 90–180d · red < 90d.
- Risk Score
- Composite 0–100 across liquidity, leverage, coverage, sector exposure, macro context.
- Insurance Gaps
- Unhedged exposure tags (LIFE, HEALTH, HOME, CREDIT_LIFE, DISABILITY) with severity + exposure amount.
- Goals
- Auto-generated from profile (Emergency Fund, Retirement, Home, Education, etc.) — see Goals & Life Events.
Corporate Treasury
The corporate track adds sector-aware treasury intelligence, working-capital diagnostics, and trade-finance product recommendations.
- Cash Conversion Cycle (CCC)
- DIO + DSO − DPO, in days. > 90d is amber.
- Counterparty Concentration
- % revenue from the top counterparty. > 45 % amber, > 60 % critical.
- FX Mismatch
- % of revenue/cost in a foreign currency vs reporting currency. Drives FX-hedge NBAs.
- Trade Finance / LC Utilisation
- % of available line drawn. Surfaces credit-tightening preempts.
- Cash Burn / Runway
- Monthly burn vs available liquidity → months of runway.
AI Predictive Recommendations (NBA)
The NBA (Next-Best-Action) engine is the heart of the product. Each entity detail returns a ranked list of recommendation cards. Three kinds:
- Base NBA — derived purely from the entity's own metrics (low cushion, unhedged exposure, idle deposit).
- Goal-driven NBA — generated when a goal projects below target. Shows the shortfall and required monthly contribution.
- Preempted NBA — injected by the Signal Preemption engine. Carries the ⚡ amber badge, the source headline, and the math rationale (rates_pressure +0.43 · source weight 1.40× · confidence 0.91 · …).
The order is: Preempted cards first (sorted by priority), then base/goal cards, capped at 10 per entity.
Signal Preemption Engine
The four-stage pipeline:
1. Ingest → RSS pull from 24+ central banks + treasuries
2. Classify → Preemptify Neural extracts:
policy_direction, magnitude, time_horizon,
asset_impact[], affected_sectors[], confidence
3. Aggregate → weighted preempt-vector across rates / fx / credit
/ equity / commodities + per-sector pressure
(source × confidence × magnitude × time-decay)
4. Recommend → pressure > calibrated threshold triggers a
"Preempted" NBA card with full math rationale.Visit /admin/signals to: refresh the feed, tune source / asset / magnitude weights, view the live preempt-vector, and inspect each classified headline with its sectors and asset impacts.
Goals & Life Events
Each customer auto-generates a deterministic goal set from their profile (age, segment, tier, mortgage status). Default goals include Emergency Fund, Retirement Corpus, Home Down Payment, Wedding, Travel, Legacy Planning, and more.
Visit /sandbox/goals to pick a customer, review progress bars + projected future value vs target, then trigger any of 9 life events:
- marriage
- Adds Honeymoon Fund + LIFE insurance gap
- child_born
- Adds Child Education Fund + LIFE & HEALTH gaps
- home_purchase
- Removes the home-down-payment goal + adds HOME & CREDIT_LIFE gaps
- job_loss
- Pauses all goal contributions + adds DISABILITY gap
- promotion
- Boosts retirement contribution by 1.5×
- retirement_5y
- Shortens retirement horizon + adds HEALTH gap
- divorce
- Halves balances and targets + adds LIFE gap
- inheritance
- Distributes a windfall 60 / 40 between retirement and legacy
- education_start
- Adds an Education Loan Repayment goal + DISABILITY gap
Historical Backtest Engine
Five deterministic strategies, side-by-side, 6–60 months. Visit /sandbox/backtest.
- Static Policy
- Buy-and-hold synthetic balanced portfolio, no rebalancing — the baseline.
- Liquid Cushion Decay
- Defensive: exit equity when decay-days < 90.
- Preempted NBA
- Rotates based on the signal preempt-vector — this is the “hero” strategy.
- Policy Coefficient
- Tactical tilt toward lower-coefficient (less risk-weighted) sectors.
- Goal-aligned Glidepath
- Time-horizon weighted equity allocation — longer goal ⇒ higher equity.
Each result returns equity curve, drawdown trajectory, CAGR, Sharpe, max-drawdown, hit-rate, and annualised volatility. Output is deterministic per (entity × strategy) seed.
Schema Aligner
Visit /admin/aligner. Upload (or paste) a sample of your legacy CSV / JSON / fixed-width data. The aligner builds a column-to-canonical-field mapping manifest, you commit the manifest, and the commit is anchored on the Audit Ledger with a SHA-256 hash.
Manifests are versioned, addressable by schema_alignment_id, and re-usable across batches.
Audit Ledger
Every action that mutates state — policy compiles, schema commits, signal calibration changes, goal creates/deletes, life events, backtest runs, seed events — produces a ledger entry with: timestamp, action, prev_hash, current_hash, and event-specific details.
The chain is tamper-evident: changing any historical entry breaks the SHA-256 cascade. In production the chain replicates to your WORM (write-once-read-many) storage tier so any tampering is provably detectable by your own auditors.
Admin · Policy Compiler
The Policy Compiler at /admin lets compliance officers re-weight sector risk coefficients live. Type or paste a directive like “increase real-estate sector risk weights by 10 %” and the engine produces a JSON delta, attaches it to the active policy, and anchors the change to the ledger.
Affected sectors are surfaced in the top bar as N sector(s) under active policy — click through to review the live coefficients.
Localization · 80+ jurisdictions
The country picker in the top bar drives:
- Currency formatting throughout the UI (via Intl.NumberFormat)
- Product catalog filtering (target_country / target_currency)
- Signal Console priority sorting (the user's country's signals surface first)
- Regulator-pack citation style (RBI vs MAS vs OCC)
Security & Privacy
- Zero data egress
- On-Premise Pilot / Enterprise+ deploys inside your VPC. Only the LLM gateway makes outbound calls — and even that can be routed through your own air-gapped inference endpoint.
- Tokenized PII
- No raw customer identifiers leave the perimeter. All references use opaque tokens (customer_token, entity_token).
- SHA-256 ledger
- Every state change is hash-chain anchored. Replicated to WORM in production.
- RBAC
- Two demo roles — executive_demo (retail + corporate) and compliance_admin (all access including /admin*).
- Right-to-erasure
- GDPR / DPDP Act / CCPA — write to info@preemptify.com.
Keyboard Shortcuts
| ? | Open the Help Agent chat |
| / | Focus the customer search bar |
| esc | Close any open modal or sheet |
| g g | Go to dashboard (/sandbox) |
| g a | Go to admin (/admin) |
| g s | Go to Signal Console (/admin/signals) |
| g m | Go to user manual (/manual) |
Note: shortcut handlers are progressively rolled out — verify availability in your build.
Glossary
FAQ
What does Preemptify do that a traditional CRM doesn't?
What data do you collect? Does it leave my perimeter?
Where do the news signals come from?
How does the AI know about my specific product catalog?
What's the difference between an NBA card and a Preempted NBA card?
Can compliance officers tune sensitivity?
How is the demo data generated?
What if a backtest disagrees with my preferred strategy?
Need help with something not covered here? Open the Ask Preemptify chat (bottom-right of every page) or write to info@preemptify.com.