SpendShark has upgraded from a rule-based detection tool into an autonomous, agentic investigation system. We took our proven enterprise data pipeline and injected a coordinated team of AI agents directly into the decision engine.
The same robust enterprise controls you rely on, now powered by agentic intelligence.
Before we talk about AI Agents, let's talk about the foundation. SpendShark already processes billions in transactions through a highly robust data pipeline connecting your ERPs to our core Inspectors. This foundation isn't changing. It's getting an upgrade.
Continuous data ingestion from ERPs, Connectors, or Manual uploads.
Qualifying, enriching, and standardizing raw transaction data.
The analytical engine of SpendShark. This is where Agentic Intelligence is injected. Algorithms, rules, and AI Agents collaborate to generate Confidence Scores and KPIs based on deep investigation.
Every decision, pattern, and confidence score flows into the central SpendShark DB. It becomes a living asset utilized by AP Specialists, Data Scientists, and Chatbots.
Ground truth from Recovering Specialists continuously trains the models.
Directly connected to Workday, Oracle, Infor, and robust 3rd party APIs.
The foundation above is proven. But instead of relying on static rules that throw false positives, we've wrapped our battle-tested Inspectors with autonomous AI agents. Here is how the shift from rules to agents changes the game.
SpendShark's foundation remains — its intelligence has evolved.
Traditional payment controls rely on static logic and siloed alerts. SpendShark — Agentic Edition wraps every core function in an intelligent agent that actively investigates, collaborates, and resolves.
"SpendShark is a digital fraud investigation team — not a fraud score."
In enterprise software, an "Agent" is an AI system given a specific goal, a set of tools, and the autonomy to figure out how to achieve that goal.
You ask a question. It predicts the next word to generate an answer. It has no tools and acts in a single step.
You give it a complex goal (e.g., "Verify if this invoice is legitimate"). It:
Reasoning Engine
Every payment passes through a coordinated team of AI agents — each applying a different investigative lens. They don't vote. They argue. Evidence is weighed. Conflicts are surfaced. A decision is reached.
Listens to live payment events from ERP, AP systems, or event streams. Normalizes data instantly.
Parses intent, urgency, and key fields. Assigns an initial risk baseline before deeper analysis begins.
Fuzzy-matches invoice numbers, amounts, and date windows. Catches duplicates even when IDs are altered.
Resolves vendor aliases, shared tax IDs, and subsidiary relationships. Eliminates vendor-identity noise.
Identifies legitimate recurring payments (monthly rent, subscriptions). Applies variance tolerances.
Spots anomalies within recurring patterns: amount spikes, timing deviations, frequency changes.
Synthesizes all agent evidence using weighted reasoning. Business-policy aware. Conflicts are surfaced, not hidden.
The final arbiter. Makes the actual decision based on risk score, recurrence confidence, business rules, and historical outcomes. Always explains its reasoning.
Only triggers when confidence falls below threshold. Pauses the payment, and requests human approval.
Confirms what actually happened: Was payment reversed? Was vendor contacted? Was dispute raised?
Updates confidence models, vendor aliases, recurrence patterns. Every decision makes the system smarter.
Full pipeline decision:
≤ 1 Second
Before payment is released
The Inspectors are one of SpendShark's strongest proven features. In the Agentic Edition, they don't get replaced — they become the detection engine each agent calls, with their results interpreted in context rather than fired as isolated alerts.
Previous SpendShark
Every inspector fires independently. Every alert requires manual review.
Agentic Edition
Inspectors run inside agents. Humans review only when confidence is low.
Exact Match
Company, vendor, invoice, date & amount all identical
Fuzzy Match
Levenshtein distance — catches altered invoice IDs
Clustering & LLM
Semantic similarity — catches meaning-level duplicates
Velocity Check
Rapid-fire invoices from the same vendor in a time window
Benford's Law
Leading-digit statistics — catches manipulated numbers
Round Amount
Perfectly round numbers — often estimates or fraud signals
Weekend Posting
Saturday/Sunday invoice timing — unusual for B2B
ML Anomaly Detection
Trained on historical data — finds statistical outliers
AGENT 6
Risk Aggregation
AGENT 7 — ProtoVision Core
Decision Agent
In the previous version, an inspector firing on a recurring monthly payment would generate an alert — and a human would spend time reviewing it, only to clear it. In the Agentic Edition, the Recurrence Pattern Agent sees that same inspector signal, cross-references 14 months of payment history, and suppresses the false positive automatically — with confidence 94% and a full explanation. No human time wasted.
These agents aren't triggered by a single payment. They continuously study the data — like a senior fraud analyst who never sleeps.
Detects changes in vendor or payer behavior over time — subtle shifts that only emerge across hundreds of transactions.
Studies timing anomalies across the full payment history — payments too soon, too frequent, or on unusual schedules.
Spots subtle overpayments hidden within normal ranges — 2%, 5%, or 8% above historical means that rules would never catch.
Continuously discovers new recurring payment patterns, emerging fraud tactics, and vendor behavior shifts — then propagates findings to the live pipeline.
New hypotheses
Updated pattern libraries
Improved agent heuristics
Reduced false positives
"ProtoVision discovered a new quarterly billing pattern for this vendor."
Each agent is independent, opinionated, and specialized. They don't average results — they present evidence and let the Decision Agent reason through conflicts.
Connects to your payment systems and streams live transactions into the analysis pipeline. Event-driven, zero batch delay.
Classifies, normalizes, and scores each payment event before it reaches the specialist agents. Sets the analytical baseline.
Exact and fuzzy matching across invoice IDs, amounts, and date windows. Catches duplicates even when identifiers are deliberately altered.
Detects duplicate vendors, legal vs trade name aliases, shared bank accounts, and subsidiary relationships. Massive reduction in false positives.
Distinguishes legitimate recurring payments (subscriptions, rent, payroll) from risky repetitions. Applies variance tolerances and cadence models.
Spots anomalies within recurring patterns: amount spikes, timing shifts, frequency changes. Because recurring does not mean safe.
Synthesizes evidence from all agents using weighted, context-aware reasoning. Not a simple average — conflicts are surfaced explicitly.
The ProtoVision decision engine. Auto-approves, blocks, or escalates based on aggregated evidence, business rules, and learned history. Always explains its reasoning.
Automatically pauses payments when confidence is low. Presents reasoning clearly. Human approval is logged and used to train future decisions.
Closes the loop by confirming the real-world result of every decision — reversal, vendor contact, dispute, or clean payment.
Updates vendor aliases, recurrence models, and confidence weights after every outcome. The system improves without constant reconfiguration.
Always-on background agents that study historical data to discover new fraud tactics, emerging patterns, and behavioral changes — before they become a problem.
The agents aren't theoretical. They're already wired into the SpendShark dashboard. Explore the platform to see how each capability works in practice.
The command center. Real-time KPIs, trend charts, and live agent activity — in one view.
Total Spend Analyzed
$2.4B
↑ 12% vs last month
Potential Savings
$3.2M
45 new anomalies
Prevented + Recovered
$1.28M
Year to date
AI Auto-Correction
98.5%
⚡ High confidence
> Scanning Invoice #9921...
> Vendor 'Acme Corp' verified.
> Duplicate detected: INV-2023-001
> Anomaly Score: 0.02 (Low Risk)
The agents made configurable. Each detection algorithm can be enabled, tuned, or customized per your organization's rules.
Active Detection Algorithms
The human-in-the-loop made visible. A Kanban board for every case the agents escalate for review.
To Do
High
INV-9921
$24,500
High
INV-8834
$8,200
Med
INV-7712
$3,900
Active
Med
INV-6601
$12,100
Med
INV-5543
$6,750
Review
High
INV-4421
$18,300
Done
✓ Closed
INV-3310
✓ Closed
INV-2209
✓ Closed
INV-1187
Every escalated payment becomes a trackable case.
The platform includes Analytics, Predictions, ROI & Value, Vendor Risk, AP Performance, Credits, and Integration Setup.
SpendShark connects directly to your payment systems and analyzes transactions in real time — using agentic AI to stop fraud before money moves.
No batch delays. Every payment is analyzed the moment it enters the system.
Agents 2 through 5 run simultaneously, delivering depth without sacrificing speed.
Payment is temporarily paused while agents analyze. Released automatically if approved — blocked if not. Recommended default. Best balance of safety and speed.
Decision must complete before the payment is authorized. Highest protection — ideal for high-value or first-time vendors. May add latency for edge cases.
Payment is released immediately; agents flag anomalies for audit and recovery. Best for organizations prioritizing payment continuity, with recovery workflows in place.
Safe under latency pressure. No decision is worse than a cautious fallback.
Waiting for payment event...
Every decision SpendShark makes includes the full reasoning trail — so your finance team, auditors, and executives can always understand why.
Every decision includes which agents contributed, what evidence was considered, and why a specific action was recommended.
Every recommendation includes a confidence level. Low confidence automatically triggers human review — autonomy is earned, not assumed.
Automation is gated by confidence. Humans remain in control. Every approval or override is captured and improves future decisions.
Built on years of real-world experience in payment systems and enterprise data platforms. This reflects lessons from production — not experiments.
Finance teams see dramatically fewer alerts without losing detection power.
Sub-second to seconds-level payment decisions — before money leaves the account.
AP teams trust the automation. Human review reserved for genuinely uncertain cases.
The system gets smarter over time without constant reconfiguration or retraining cycles.
Finance Teams
"Fewer alerts"
Auditors
"Explainable decisions"
Engineers
"Composable, extensible"
Executives
"Gets smarter over time"
SpendShark goes beyond just stopping bad payments. Every decision feeds an enterprise Insights Database, empowering our Recover Specialists to act.
Every transaction analyzed, every hidden relationship discovered, and every confidence score generated isn't just a point-in-time decision—it becomes a permanent, queryable asset.
Detecting the error is only half the battle. Our dedicated team of Recover Specialists uses the Insights Database to take action, returning lost capital directly to your bottom line.
Common questions from Enterprise IT and Finance leaders.
SpendShark — Agentic Edition is available to select organizations through our Early Access Program. Early Access partners work directly with the iTK team on implementation, validation, and configuration — shaping the platform as it scales.
Dedicated iTK Team Access
Custom Configuration
Influence the Roadmap
Enterprise-Grade Support
Built on years of real-world experience in payment systems and enterprise data platforms. iTK Technologies has analyzed over $2.2 trillion in transactions across healthcare, retail, and financial services.
Accepting a limited number of Early Access partners. Contact us to discuss fit and timeline.