Purpose - built software for intelligent customer interventions.
Actioneer gives agents the context to act: it surfaces the signal from your data, shows you every action you can take on it, and, with your approval, runs the intervention on the systems you already run.
Your lifecycle is
leaking value.
Your lifecycle is
yours to bend.
At every stage, customers drop off silently: a KYC never finished, a first purchase never made, an account going quiet. Each one is a visible signal, and a missed intervention.
The context you need is scattered across nine systems.
Every org's context is unique. It is spread across acquisition, product, and support systems, with no standard way for agents to search and retrieve across them. Agents can't absorb what's scattered.
Hallucinations
Agents guess when they can't retrieve the facts.
Siloed at scale
Each tool sees a slice: none sees the whole customer.
No loop back
What happened on the call never reaches the CRM.
Missed triggers
The KYC stall, the dormancy dip - seen too late, or not at all.
One stalled KYC, start to finish.
Sovereign context.
It never leaves your stack.
Actioneer connects to your warehouses, CRM, docs and chat read-only, and builds the context graph inside your own environment. Nothing is extracted or centralised externally.
An ontology of your entities and relationships across sources, with cross-source joins and lineage.
An ontology of your entities and relationships across sources, with cross-source joins and lineage.
An ontology of your entities and relationships across sources, with cross-source joins and lineage.
An ontology of your entities and relationships across sources, with cross-source joins and lineage.
An ontology of your entities and relationships across sources, with cross-source joins and lineage.
Three products on one sovereign context:
ask,segment,speak.
AI Analyst
Ask simple or complex questions in natural language, across your org's data, documents, and playbooks. Every answer is verifiable to source and logic, with no black boxes.
Segmentation
Seamless cohorting and opportunity spotting: diagnose drop-offs, find high-value and at-risk users, and orchestrate omni-channel journeys from your existing CRM. No CDP bolt-ons.
Voice
Voice campaigns with personality and policy guardrails. Agents make tool calls in-call on approved data, and every interaction is evaluated and enriched back into the CRM.
Built for data-rich, highly regulated customer operations.
NBFCs
Recover stalled KYC journeys, trigger cross-sell like top-up loans, coordinate renewals, and run compliant collections outreach.
SIP RESTART RATE BY TIME TO CONTACT
SIP book: 62,000 mandates, 4,100 failed debits monthly.
Lending & banking
Act on eligibility, repayment and service signals: pre-approved offer campaigns, repayment reminders before due dates, dormancy reactivation, and RM enrichment for better targeting.
WHAT HAPPENS AFTER A MISSED EMI
Retail EMI book · 4,900 missed EMIs a month
Insurance
Follow up leads while intent is live, complete policy onboarding, service routine requests in-language, trigger cross-sell on life events, and coordinate renewals ahead of expiry.
WHY A POLICY LAPSED — WHAT YOU ACTUALLY KNOW
Motor & health book · 76,400 policies up for renewal
Healthcare & pharma
Run patient support programs with medication reminders, adherence nudges, refill coordination, feedback collection and CRM enrichment — without providing clinical advice.
SIP RESTART RATE BY TIME TO CONTACT
SIP book: 62,000 mandates, 4,100 failed debits monthly.
Ranked #1 globally on data agent benchmarks.
The agents run on a multi-agent harness we built and benchmark openly — not a prompt on someone else's model.
DABStep
Know that your data & models are secure
Connects to what you run. Acts in what you use.
Teachable flexes to fit your workflow, integrating with the apps you love while powering them into a system for growth.
Thinking on data, agents, and customer lifecycle.
Practical lessons from building and deploying agents inside real customer operations.
The signal is usually present. The challenge is finding it early enough, with enough context, to act.
How to make scattered organizationalknowledge useful without movingsensitive data out of your stack.
Where human judgment belongs, what agents can safely handle, and how to keep every decision auditable.
