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Insurance

Every quote followed up. Every renewal reminded in time.

One bounded loop carries the repetitive conversation around a policy — the quote, the missing document, the claim status, the renewal. Underwriting and claims decisions stay with your specialists.

Book a demoSee the six agents

Incoming event

  • Quote request
  • Proposal doc missing
  • Claim query

Orkivanta agent

AI Voice Dialer

  • Follow up
  • Relay
  • Remind

Connected system

CRM · policy · claims

Contact & intake logged

Human owner

Advisor desk / claims team

Decides underwriting, claims & settlement

  • Incoming event
  • Orkivanta agent
  • Connected system
  • Completed update
  • Human owner

One bounded loop in Insurance, drawn as an example to tune to your own process — not a published client result.

A day in the operation

Four or five moments, one bounded loop.

Quotes go unworkedProposals stall on a missing documentServicing is endlessly repetitiveClaims feel like a black boxRenewals lapse quietly

  1. 01 · Quote

    Follow up & qualify

    Call a quote request back promptly, confirm cover type and rough requirements, and book high-intent prospects with an advisor.

  2. 02 · Onboard

    Chase proposal docs

    Detect the missing document on a proposal, explain what's needed, and update the record when it arrives.

  3. 03 · Serve

    Answer policy questions

    Relay an accurate coverage or endorsement detail from your system and escalate anything beyond a lookup.

  4. 04 · Claim support

    Register & status

    Capture initial claim details against your checklist, relay the recorded status, and route the case to the claims team.

  5. 05 · Renew

    Remind & re-engage

    Place a timely renewal reminder, answer routine questions, and hand anyone wanting advice to an advisor.

Six agents for Insurance

What each one would be configured to do here.

The same six agents we ship, read against this industry. Each panel is an example fit to tune to your process — including the ones where the honest answer is that this is not the workflow to automate first.

  • AI Voice Dialer

    Responds, qualifies and books

    Example fitCall a quote request back, confirm cover type and rough requirements, book the advisor

    Works on CRM · policy system · calendar

    Advisor desk takes every qualified conversation

    No premium quoted, no policy recommended

    Detail

    It confirms what kind of cover is being asked about and books the conversation. Product suitability is an advisor's judgement and is never the agent's.

    Wrong for If your product needs a human to build trust over several long conversations — a big enterprise sale, a clinical consultation — the agent should qualify and hand off, not close. Point it at volume, not at the relationship.

    See AI Voice Dialer

  • AI Marketing Agent

    Drafts, schedules and reports

    Example fitAn approved renewal-season content calendar, drafted and scheduled to your positioning

    Works on Content calendar · traffic and ranking data

    Your marketing owner holds the review gate

    Nothing publishes without an approval you control

    Detail

    Drafts and schedules against a position you have already set, and reports what moved. Nothing describing cover reaches a customer without a person approving the wording.

    Wrong for This is a marketing engine, not a brand strategist. If you have a capable CMO already, it makes them faster — it does not replace their judgement. And it will not invent a positioning you have not decided.

    See AI Marketing Agent

  • AI Meeting Notes Agent

    Captures decisions, closes the loop

    Example fitAdvisor meeting notes, action items with an owner, and the CRM record after

    Works on CRM · email draft

    The advisor approves the follow-up before it sends

    Not in a conversation that forbids recording

    Detail

    Routine advisor and broker calls generate follow-up nobody has time for; that is the work it takes. Any conversation your policy says may not be recorded stays out of scope.

    Wrong for If your meetings are confidential in a way that forbids recording — some legal, medical, or HR conversations — do not put an agent in the room. It earns its keep on the routine calls that create follow-up nobody has time to do.

    See AI Meeting Notes Agent

  • AI Data Entry Agent

    Does the work between the work

    Example fitChase a missing proposal document, then relay the claim status already recorded in your system

    Works on CRM · policy · claims systems · email · internal APIs

    Operations officer and the claims team own the case

    Assesses, values and settles nothing

    Detail

    It captures intake against your checklist and reads back a status that already exists. Every claims judgement — and every decision about what a policy pays — stays a person's.

    Wrong for If the task changes shape every single time and has no repeatable pattern, an agent has nothing to learn — a person is cheaper. This pays off on the work that is dull precisely because it repeats.

    See AI Data Entry Agent

  • AI Data Analyst

    Answers questions of your data

    Example fitAsk which servicing and renewal questions recur, and read the query behind the answer

    Works on Your warehouse, read-only

    Operations or CX lead audits the working

    Sets no pricing, product, or policy change

    Detail

    It surfaces the pattern and shows the exact conversations and query behind it. What you change in response is your call, on evidence you can see.

    Wrong for Trust here depends on clean data. If your source tables contradict each other and nobody owns the definitions, the agent will answer faithfully from a mess. Fix the warehouse first, or scope the work to the tables you trust.

    See AI Data Analyst

  • AI Resume Screener

    Ranks resumes with reasons

    Example fitShortlist claims-handler and advisor-role applications, with a written reason for each ranking

    Works on Your applicant records

    A recruiter reviews the shortlist and every reason

    Never auto-hires and never silently rejects

    Detail

    Every inbound resume is read against the spec you set, so a strong candidate at the back of the pile still surfaces. A person makes the decision.

    Wrong for It shortlists; it does not hire. Screening is where bias and legal exposure live, so the final call — and the accountability for it — stays with a person. If you want a tool that auto-rejects to zero human involvement, we will not build it.

    See AI Resume Screener

Where the boundary sits

Human decides here.

Orkivanta agent

The agent prepares the work

  • Call a quote request back
  • Confirm cover type and rough requirements
  • Chase a missing proposal document
  • Relay a status recorded in your system
  • Capture claim intake against your checklist
  • Place a renewal reminder and route the rest

Human owner

Decisions that stay with your advisor and claims teams

  • Underwriting
  • Claims assessment and valuation
  • Settlement
  • Quoting a premium
  • Recommending a policy or interpreting cover

The loop stops and routes the full context to advisor desk / claims team the moment it reaches underwriting, claims assessment, valuation & settlement.

What changes operationally

What stops depending on somebody remembering.

  • Visibility

    Every action is logged with the reason behind it, so a supervisor can read back what happened and why rather than take an outcome on faith.

  • Less repetitive follow-up

    The callback, the missing document, the reminder — the contact that only works if it happens every time stops depending on somebody remembering.

  • Clear human ownership

    The sensitive decision has a named owner before anything runs, and the agent stops and hands over the full context instead of pressing on.

The detail, in full

Everything above, spelled out.

Open any of these for the long form — the worked examples, the comparison, an estimate of what the work costs today, and the exact wording of what a person keeps.

Every example configuration, stage by stage

Quote

Follow up & qualify

Quote follow-up

AI Voice Dialer
  1. Trigger

    A customer requests a quote through a form

  2. Agent action
    • The Calling Agent calls back promptly
    • Confirms cover type
    • Who's covered
  3. Systems & record

    CRM · calendar

    Action logged with its reason
  4. Human owner

    Advisor desk

    Owns what happens next

Stops here — It does not quote a premium, recommend a policy, or give insurance advice

The full configuration, in words
Trigger
A customer requests a quote through a form, aggregator, or missed call.
Agent action
The Calling Agent calls back promptly, confirms cover type, who's covered, and rough requirements, scores intent, and books high-intent prospects with an advisor.
Human owner
Advisor desk — takes every qualified conversation.
Honest limit
It qualifies and books; it does not quote a premium, recommend a policy, or give insurance advice. Product suitability stays with an advisor.
See the AI Voice Dialer

Onboard

Chase proposal docs

Proposal & document chase

AI Data Entry Agent
  1. Trigger

    A proposal is submitted with missing information

  2. Agent action
    • The Employee Productivity Agent detects the gap
    • Contacts the customer through the channel you approved
    • Explains what's needed
  3. Systems & record

    CRM · spreadsheets · email · internal APIs

    Action logged with its reason
  4. Human owner

    Policy issuance / operations officer

    Owns what happens next

Stops here — It does not verify a document, underwrite, or issue a policy

The full configuration, in words
Trigger
A proposal is submitted with missing information — a KYC document, a medical report, a signature.
Agent action
The Employee Productivity Agent detects the gap, contacts the customer through the channel you approved, explains what's needed, and updates the record when it arrives.
Human owner
Policy issuance / operations officer.
Honest limit
It chases and files; it does not verify a document, underwrite, or issue a policy. Those stay inside your process.
See the AI Data Entry Agent

Serve

Answer policy questions

Policy servicing

AI Data Entry Agent
  1. Trigger

    A policyholder asks about coverage details

  2. Agent action
    • The Employee Productivity Agent looks the answer up in the system you connected
    • Replies with the accurate detail
    • Escalates anything beyond a straightforward lookup
  3. Systems & record

    CRM · spreadsheets · email · internal APIs

    Action logged with its reason
  4. Human owner

    Service desk

    Owns what happens next

Stops here — Read-and-relay of information already recorded only

The full configuration, in words
Trigger
A policyholder asks about coverage details, an endorsement, or a document.
Agent action
The Employee Productivity Agent looks the answer up in the system you connected, replies with the accurate detail, and escalates anything beyond a straightforward lookup.
Human owner
Service desk.
Honest limit
Read-and-relay of information already recorded only. It does not interpret coverage, confirm what a policy will pay, or change a policy.
See the AI Data Entry Agent

Claim support

Register & status

Claim registration & status

AI Data Entry Agent
  1. Trigger

    A customer wants to register a claim or asks for a status update

  2. Agent action
    • The Employee Productivity Agent captures the initial claim information against your checklist
    • Explains what documents are required
    • Relays the status recorded in your system
  3. Systems & record

    CRM · spreadsheets · email · internal APIs

    Action logged with its reason
  4. Human owner

    Claims team

    Owns what happens next

Stops here — It does not assess, approve, value, or settle a claim

The full configuration, in words
Trigger
A customer wants to register a claim or asks for a status update.
Agent action
The Employee Productivity Agent captures the initial claim information against your checklist, explains what documents are required, relays the status recorded in your system, and routes the case to the claims team.
Human owner
Claims team — owns every assessment and decision.
Honest limit
It intakes and informs; it does not assess, approve, value, or settle a claim. Every claims decision is a person's.
See the AI Data Entry Agent

Renew

Remind & re-engage

Renewal reminders

AI Voice Dialer
  1. Trigger

    A policy is approaching its renewal date

  2. Agent action
    • The Calling Agent places a timely reminder
    • Answers routine renewal questions
    • Sends the payment link you configured
  3. Systems & record

    CRM · calendar

    Action logged with its reason
  4. Human owner

    Retention / advisor desk

    Owns what happens next

Stops here — It does not re-underwrite, change cover, or advise on switching

The full configuration, in words
Trigger
A policy is approaching its renewal date.
Agent action
The Calling Agent places a timely reminder, answers routine renewal questions, sends the payment link you configured, and routes anyone wanting advice to an advisor.
Human owner
Retention / advisor desk.
Honest limit
It reminds and collects a renewal the customer chooses to make; it does not re-underwrite, change cover, or advise on switching. Any change of cover goes to an advisor.
See the AI Voice Dialer

Across the lifecycle

Measured, not decided

Renewal & claim-call analysis

AI Data Analyst
  1. Trigger

    A period of servicing and renewal calls closes

  2. Agent action
    • The Data Analysis Agent groups the recurring questions and objections and shows the exact conversations behind each theme
  3. Systems & record

    Your warehouse, read-only

    Action logged with its reason
  4. Human owner

    Operations / CX lead

    Owns what happens next

Stops here — It does not decide policy, pricing, or product changes

The full configuration, in words
Trigger
A period of servicing and renewal calls closes.
Agent action
The Data Analysis Agent groups the recurring questions and objections and shows the exact conversations behind each theme.
Human owner
Operations / CX lead.
Honest limit
It surfaces patterns; it does not decide policy, pricing, or product changes. That call is yours, on evidence you can see.
See the AI Data Analyst
  • Incoming event
  • Orkivanta agent
  • Connected system
  • Human owner
  • Where the agent stops
Before, and with Orkivanta

Before

Manual, fragmented — every step waits for a person to get to it.

Before: Quote request, then Follow-up when an advisor is free, then Generic document chase, then Repeat 'where's my claim?' calls, then Renewal notice missed. The gaps between those steps: Waits for a free advisor; One document still outstanding; Each call pulls someone off other work; The policy lapses quietly.

With Orkivanta

One connected route, ending on a named person.

With Orkivanta: Quote request, then Prompt qualifying callback, then Document gap chased on a schedule, then Claim intake + status relayed, decision human, then Timely renewal reminder, advice handed off, then your advisor and claims teams owns the decision.

Illustrative workflow, not a customer result. Actual outcomes depend on your process, the system access you grant, and execution.

Plan a workflow estimate

A repetitive-work cost estimate from your own numbers — the same transparent arithmetic as the full calculator, not an ROI and not a saving. The defaults are placeholders; replace them with yours.

Estimate this Insurance workflow
Monthly repeatable-work cost

₹14,548.80

Annual repeatable-work cost
₹174,585.60
Repeatable hours / month
41.568 hrs

Planning estimate from your inputs — not a quote or expected saving.

Run full calculatorCheck workflow fit
See the Calling Agent

What to baseline first

Candidates to scope against on your own systems — not promised uplift.

  • Quote-to-advisor conversion and callback time
    BaselineTarget you set
  • Proposal / document completion rate
    BaselineTarget you set
  • Policy issuance turnaround
    BaselineTarget you set
  • Claim-status contacts deflected from the team
    BaselineTarget you set
  • Renewal reminder reach and on-time renewals
    BaselineTarget you set
  • Cost per interaction
    BaselineTarget you set
You stay in control, and when not to automate this

You set what the agent may say and where it must stop. It follows up, chases, relays, and reminds; it does not quote a premium, recommend a policy, interpret coverage, or make a claims decision. Those sit outside its boundary by design, and it routes them to a specialist with the full context attached.

Every action is logged with its reason, and a hard kill-switch stops every line at once. That audit trail is the point in a high-stakes workflow: you can show what the agent did, why, and where a human took over. You set the rules, every agent action is logged, and sensitive cases route to a named person.

When not to automate this

Underwriting, claims assessment, valuation, and settlement stay with your specialist team. These agents handle the repetitive contact around a policy — following up a quote, chasing a document, relaying a status, reminding a renewal. The moment a decision turns on risk, suitability, or the worth of a claim, the agent's job is to hand it over with the full context, not to make the call.

Next step

See it on your workflow.

Tell us which workflow you would point an agent at and roughly how much of it there is. We will come back on whether it is a fit — including when the answer is that you should not build it.

  • A demo is a working walkthrough on a scenario like yours, not a deck.
  • A free POC is scoped to one workflow so you can watch it run first — request one and we will agree what it covers.
  • Prefer to check yourself first? The readiness test scores your workflow in six questions and asks for no email.
What would you like?

A working walkthrough against a scenario like yours — not a deck.

This sends a request; it does not book a slot or start a POC. We read every message and reply ourselves.