Agentic Customer Success intelligence you can explain and control.
Send us the measures that matter each week. AgentIQ validates them, calculates health and routes each account through governed, deterministic logic your team configures — and an agent explains what came out. Every recommendation arrives with its evidence and its decision trail.
Preparing for controlled demonstrations and selected B2B SaaS design-partner pilots.
From weekly data to a decision
A week of measures in. One brief you can act on.
You send the measures each week — by export or direct entry, no integration required. The engine validates them and calculates health, attention and routing. The agent reads that result and states what it found, with the evidence behind it and how confident it is.
Product usage
Support
Commercial
Engagement
Account notes
EngineAgent
Evidence brief
Medium confidence
Account
Northstar Labs
Investigation summary
Adoption of the reporting workflow has fallen sharply in one team while the rest of the account is stable. Renewal is 62 days out and the original sponsor has disengaged.
Key evidence
Weekly active users down 34% across the last six weeks
Three unresolved P2 tickets on the reporting module, oldest open 19 days
Executive sponsor absent from the last two QBRs
Risk assessment
Renewal at risk — the workflow that justified the original purchase has degraded.
Contributing factors
Support friction blocking a core workflow
Possible internal ownership change
No expansion discussion logged before renewal
Illustrative brief. The account and its evidence are fictional; the shape
— evidence, assessment, contributing factors, stated confidence — is what
the platform produces.
See the full investigation record →
One operating path. Clear handoffs. Full control.
The same five stages run behind every account, in the same order, every time. Where AI contributes and where configured logic decides is fixed by the architecture — not by a prompt.
Weekly data in
The measures you already track, sent as an export or entered directly. Validated before anything is calculated from them.
AI investigation
The agent gathers evidence, derives signals and forms a hypothesis it can defend.
Governed decision
Data quality gate, health scoring, attention and playbook routing — deterministic, versioned.
AI recommendation
A recommended motion, grounded in the evidence and the routed playbook.
Controlled action
Nothing reaches a customer without a person approving it.
Built as a domain model, not a wrapper
The moat is the Customer Success model underneath: a terms catalogue, KPI formulas, health components, thresholds, playbook rules and an outcome loop. The deterministic core ships first and is explainable end to end. Agents add language and richer recommendations on top of it.
Deterministic core
KPIs, health scoring and playbook routing are calculated, not generated. The same inputs produce the same decision — reproducible and audit-friendly.
Evidence you can check
Every conclusion carries the source records behind it, with system and date, plus the evidence that argues against it.
European by default
Your data and the deterministic engine run in the EU, under GDPR-aligned processing, with no model training on customer data. Where the AI layer is used, that inference step is disclosed and region-configurable.
AI investigates. Governed logic decides. Your team stays in control.
Bring us a real Customer Success decision.
A renewal call, an adoption intervention, an escalation you are unsure about. We will show you how AgentIQ investigates it, what evidence it surfaces, and what it recommends.