AI you can’t see into isn’t AI you can trust. Flexivity records every decision, draft, reply, and change the AI makes — and makes the aggregate picture clear through analytics that show where the AI is holding up, where it isn’t, and where the knowledge or rules need attention. The infrastructure beneath it all is built so your customer data stays yours: tenant isolation, PII scrubbed before data is stored, and controls that stop the AI instantly when you need to.
Features
Analytics
See where the AI is holding up, where it isn’t, and where to tune.
Flexivity logs every action the AI takes on every ticket — the rules that fired, the attributes it set, the knowledge articles it cited, the replies it drafted or sent, the handovers it made and the reasons for them, the changes it prepared or executed in your systems. Analytics turns that log into the aggregate picture: how many tickets the AI handled at each Progressive Autonomy level, how often agents edited or discarded drafts, which attributes get corrected most often (and might need a sharper classification rule), which classes of tickets are moving the needle on handle time.
The point isn’t dashboard theater. It’s giving you the evidence you need to make configuration decisions. Promote a class of tickets up the ladder when the data shows the AI is answering it well. Demote a class when the correction rate is high. Fix a rule when the attributes it depends on are being manually overridden. Analytics is where you see what your Flexivity deployment is actually doing, and what to change about it.
The point isn’t dashboard theater. It’s giving you the evidence you need to make configuration decisions. Promote a class of tickets up the ladder when the data shows the AI is answering it well. Demote a class when the correction rate is high. Fix a rule when the attributes it depends on are being manually overridden. Analytics is where you see what your Flexivity deployment is actually doing, and what to change about it.
- Every AI action logged — rules, attributes, drafts, replies, handovers, changes — on every ticket
- Aggregate reporting — Progressive Autonomy level distribution, agent acceptance/edit/discard rates, attribute corrections, handover reasons
- Decision support — the data points to the specific rules, classes of tickets, or knowledge gaps worth adjusting
Features
Trust
Built so your customer data stays yours.
Trust is a critical product property. Flexivity’s architecture is built around a specific set of assurances about what happens to your data, who sees it, and what you can stop.
Personal information is scrubbed before any customer data is stored — email addresses, phone numbers, names — using the same rules whether the content comes from a ticket, a knowledge base article, or an uploaded document.
Everything runs on AWS with tenant isolation — your data is never shared across customers, never used to train models. A single kill switch stops all AI replies to customers instantly, no matter which Progressive Autonomy level a ticket is at. The audit log records every AI action on every ticket, so when you need to explain why a ticket went a particular way, the record is there.
AI responses are grounded in your knowledge base, with sources cited, plus optional verification of responses against sources. AI guardrails are in place to prevent misuse and abuse.
Our full security posture is documented, with specific controls and data handling practices available on request.
Personal information is scrubbed before any customer data is stored — email addresses, phone numbers, names — using the same rules whether the content comes from a ticket, a knowledge base article, or an uploaded document.
Everything runs on AWS with tenant isolation — your data is never shared across customers, never used to train models. A single kill switch stops all AI replies to customers instantly, no matter which Progressive Autonomy level a ticket is at. The audit log records every AI action on every ticket, so when you need to explain why a ticket went a particular way, the record is there.
AI responses are grounded in your knowledge base, with sources cited, plus optional verification of responses against sources. AI guardrails are in place to prevent misuse and abuse.
Our full security posture is documented, with specific controls and data handling practices available on request.
- PII scrubbed before storage — email, phone, names removed by rule
- AWS-hosted, tenant isolated, never used to train models — your data remains yours
- Kill switch and audit log — stop AI replies to customers instantly; record every AI action for accountability
Built around the principles support teams and their customers need
Flexivity’s design assumes that AI in customer service should be transparent about what it is, accountable for what it does, and controllable by the team deploying it. Those principles aren’t a response to any one requirement — they’re what responsible AI in support looks like, and they’re how the product is built.
- The AI identifies itself as AI. Customer-facing messages generated by Flexivity are produced by an AI assistant, and the product supports configurations that make that transparent to the customer.
- A human path is always available. Every conversation has an unambiguous route to an agent, whether a customer requests one, the AI reaches a limit, or your rules specify a handover.
- Every AI action is recorded. The audit log captures what the AI did on each ticket — the rules that fired, the attributes it set, the knowledge it cited, the replies it drafted or sent, the changes it prepared or executed, and the reasons behind handovers.
- Grounded answers are explainable. Every reply to a customer is tied to a source in your knowledge; unverified claims are withheld; agents and admins can trace any answer back to the article it came from.
- Data handling is tenant-isolated and controlled. Your data is never used to train models, is scrubbed of personal information before the AI processes it, and is stored in the regions your deployment requires.These capabilities give support teams the tools they need to run AI responsibly, meet the obligations their businesses and customers expect, and adapt as expectations evolve.