Agivant builds agents that reason across how customers, accounts, suppliers, and assets connect, using TigerGraph’s native graph engine and GraphRAG. First results land in days and weeks.
| What Agivant brings to TigerGraph
TigerGraph stores and traverses the connections. Agivant engineers everything around them: the data that feeds the graph, the schemas that shape it, the agents that act on what it shows, and the controls that keep people in charge.
Graph schema design mapped to the questions the business needs answered
Real-time ingestion pipelines that keep the graph current as transactions and events land
Multi-hop traversal logic written in GSQL and tuned for production load
GraphRAG grounding, so agent answers carry the relationships and context behind them
Human checkpoints in front of high-stakes decisions, with evaluation and rollback from the first sprint
Reusable schemas, traversal logic, ingestion pipelines, and agent patterns that carry into the next use case
Where Agivant applies TigerGraph
Each of these starts with a question a row-level query struggles to answer.
Fraud ring detection
Supply chain risk intelligence
Entity resolution and customer 360
Anti-money laundering network analysis
Organized fraud runs as a network of shared devices, addresses, accounts, and intermediaries. Agivant builds agents on TigerGraph that trace those links as transactions land and bring the ring to investigators while it is still active.
Key capabilities
• Multi-hop traversal across accounts, devices, locations, and transaction chains • Ring and anomaly detection as patterns form • Risk-scored cases, prioritized for investigators • Investigator review before any account action
Reported outcomes
up to 60% reduction in fraud ring detection time and up to 35% of cases auto-prioritized by risk score
Organized fraud runs as a network of shared devices, addresses, accounts, and intermediaries. Agivant builds agents on TigerGraph that trace those links as transactions land and bring the ring to investigators while it is still active.
Key capabilities
• Multi-hop traversal across accounts, devices, locations, and transaction chains • Ring and anomaly detection as patterns form • Risk-scored cases, prioritized for investigators • Investigator review before any account action
Reported outcomes
up to 60% reduction in fraud ring detection time and up to 35% of cases auto-prioritized by risk score
More connected-data use cases on TigerGraph
The same graph and agent layer extends to other decisions that depend on relationships.
Know Your Customer (KYC)
Entity risk scored across every relationship.
Network security
Threat paths traced through the full topology.
Recommendation engines
Next-best actions grounded in real connections.
Master data management
One resolved entity, every system.
Clinical pathways
Patient journeys traced across providers.
Telecom network optimization
Capacity and faults traced across the network.
Engineering depth behind the TigerGraph practice
Agivant’s wider engineering practices connect TigerGraph work to the data, agents, and platforms around it.
Agentic AI and AgentOps
Agent orchestration and guardrails, with human checkpoints in front of high-stakes calls.
Data engineering
Pipelines that feed the graph from transactional systems and event streams in near real time.
AI and ML engineering
Graph-derived features feeding risk and churn predictions.
Cloud and platform engineering
Secure TigerGraph deployments on the cloud the enterprise already runs, observable from day one.
Get Amp’d on TigerGraph
Amp’d is how Agivant builds on TigerGraph. Forward Deployed Engineers sit with business and technology teams to turn one high-value workflow into a production agent on connected enterprise data. Every engagement leaves behind graph schemas, traversal logic, ingestion pipelines, and agent patterns the next use case builds on. Every build amplifies the next.
Proof from production
Gemini Enterprise engagements already running, with outcomes measured in the environment they were built for.
