AI for insurance agents that takes quote intake, renewals and service requests off your team

We build AI agents for agencies, brokers and MGAs that read submissions, prepare renewals and answer service requests inside the agency management system and inbox you already run. A licensed producer approves anything that touches coverage. For IES Limited, a travel insurance group, a system we built reads all 25 pages of the daily MI pack and alerts the right people by noon.

Jason, our COO, runs the call and scopes the first workflow with you.

Agent run: submission intakeSample
  1. Submission email readACORD application, loss runs and a schedule of locations
    Done
  2. Fields extractedNamed insured, class codes, revenue, prior carrier
    Done
  3. Gaps foundLoss runs cover three of five years, request drafted to the insured
    1 request
  4. Markets matchedCarriers checked against your appetite notes
    4 markets
  5. Producer reviewA licensed producer approves the markets and sends
    Approval
  6. Written to your AMSSubmission record, follow-up tasks and run log entry
    Done

What the agent can seeOnly the fields this task needs. Other client files are never retrieved.

Named insured
Visible
Class codes and revenue
Visible
Loss runs
Visible
FEIN
12-•••••••
Payment details
Not retrieved
Other client files
Not retrieved

Illustrative run. Fields, rules and markets are set per workflow during scoping.

Clients include

AudinatePinkcubeBizIQCourserAllClear Travel Insurance

Two systems running at an insurance group

IES Limited sells travel cover for people with pre-existing medical conditions, across four brands in the UK, Ireland and Australia. We built its daily MI alerting and its content engine, and each case page carries the full figures. See all case studies

IES LimitedInsurance, daily MI alerting
20 days30 min
Time to detection
  1. 11:30The MI pack lands in OneDrive
  2. 2.7 minMedian ingest into the database
  3. 12:00Alerts in Teams, by audience

14 metrics · 4 brands · 6 Teams audiences · every working day

A 25-page MI pack nobody finished, read in full every working day

Around 50 people received the pack each morning, and a drop in the quote funnel took 20 days to notice. Every figure is now calculated in the database, the model only writes the sentence around it, and a verification step names any figure it was not given.

1,751metric evaluations
4 of 4replayed incidents caught
34threshold changes, zero tickets

IES moves its own thresholds in a control panel we built, and every change is recorded with who made it.

Read the MI alerting case

Four regulated brands on one content engine

IES LimitedInsurance, compliance-first content engine
4 brands1
Content engine for every brand and region

Compliance checked before a person reads the draft

An 11-stage pipeline on n8n researches, briefs and drafts in each brand's voice, then audits every draft against that brand's rulebook, inside the Notion workspace the marketing team already used.

0external agencies in 2026
11stages automated
100+topics generated
Read the content engine case

Working with Owen and the Ovidius team has been a seamless experience; they are incredibly responsive and adapted quickly to our specific requirements. By leveraging their AI-driven content for our travel insurance sites, we've completely phased out our reliance on external agencies and freelancers in 2026 while significantly speeding up production.”

Bhavek RughaniBhavek RughaniHead of Marketing, IES Limited

Where AI agents for insurance pay off first

Start with work that arrives in volume, follows rules your team already writes down, and ends with a licensed person signing off. These six workflows meet all three.

Submissions

Quote intake and submissions

The agent reads ACORD applications, loss runs and broker emails, fills your agency management system, and drafts the request for anything missing. Your producer picks the markets and sends.

Prove it on your own submissions
Renewals

Renewal preparation

Ahead of each expiration date, the agent pulls the policy, the year's claims and any exposure changes into one renewal packet, and drafts the remarketing request for your account manager to approve.

How the agent finds the right policy facts
Servicing

Certificates and endorsements

The agent reads certificate requests from email, checks the holder and additional insured wording against the policy, and fills your certificate template. Anything non-standard goes to a CSR.

Built on self-hosted n8n
Claims

First notice of loss intake

The agent captures the loss from email, a web form or a call transcript, structures it for the carrier's claim submission and confirms receipt to your client. Coverage and claim decisions stay with the carrier and your licensed staff.

Same document pipeline as our invoice automation
Service

Customer service after hours

The agent answers questions from the policy documents, books callbacks and hands any coverage question to a licensed person with the conversation attached. A second agent checks each reply before your client reads it.

See the chat agent build
Reporting

MI and production reporting

The agent reads the daily pack or your management system export, checks each metric against its expected range and alerts the people who need to act. IES cut its time to detection from 20 days to 30 minutes.

Read the MI alerting case

Compliance-checked insurance content, from a system you own

Your marketing team runs the engine, and we build and maintain it. The engine researches, briefs and drafts in each brand's voice, and a compliance agent audits every draft against that brand's rulebook before a person reads it.

  • Banned terms flagged and replaced with the brand's approved phrasing
  • Statistics, such as a claims-paid rate, checked against approved sources
  • A forced answer, correct or improvement needed, with each issue named
  • A separate ruleset for each region
See the compliance checker in the IES case
Compliance check: Brand A, UKIllustrative

Cover for travellers with conditions such as Covid-19, with 98% of claims paid last year.

{ "verdict": "improvement_needed", "issues": [ { "type": "banned_term", "text": "Covid-19", "action": "replace with approved phrasing" }, { "type": "statistic", "text": "98% of claims paid", "action": "verify against approved source" } ] }

The two issues and the verdict format follow the IES project. Draft text and field names are illustrative; the brand is withheld.

A licensed person approves anything that touches coverage

Agents read, check and draft. They never bind coverage, send a quote or advise your client; a licensed producer or CSR does that, with the agent's work attached.

Your environment
  1. SourcesManagement system, inbox, carrier portalsRead through the access your team already has
  2. ScopeFields per taskEach agent reads only what its task needs
  3. AgentSelf-hosted n8nRuns the workflow and your rules in your cloud
  4. ApprovalLicensed producer or CSROn anything that touches coverage, before it leaves

run 4127 · submission intake · 14 fields read · 1 request drafted · markets approved by producer · written to AMS

Outside the boundary

A model provider receives scoped fields only, or is replaced by a model you host.

Never allowed
  • Binding coverage
  • Coverage advice to a client
  • Client data used to train models
Approval gates

A person signs off before it leaves

Quotes, renewal offers, certificates with non-standard wording and anything a client reads as advice wait for a licensed person. The agent cannot send them on its own.

See AI governance
Second-agent checks

A second agent checks the first

At IES a compliance agent audits every draft, and the MI system names any figure the model was not handed. On a service chat agent, a second agent reads each reply before your client does.

Read the content engine case
Logged

Every run on the record

Each run records what it read, what it changed and who approved it. IES keeps an append-only history of every metric evaluation and every threshold change, with the name of the person who made it.

See AI managed services

How insurance agency automation runs with us

The same team carries your workflow from the first call to the system running in production, and every build is tested in staging before it goes live. See how we work

  1. Jason
    Jason, COO

    AI Audit, if you need one

    Eight phases across strategy, operations, data and systems over 4 to 8 weeks, ending in a ranked 90-day roadmap. The fee is credited toward your first build.

  2. Jason
    Jason, COO

    Scope

    One workflow, the systems it touches and what it will take, agreed before you commit. Owen joins the first calls until you are onboarded.

    • The fields each agent may read
    • The steps that always wait for a licensed person
    • The timeline, before you sign
  3. Maciej
    Maciej, CTO

    Build

    Built against your real data, in your environment, as production software from the first week.

    • Staging first, on test or masked client records
    • Your producers and CSRs use it on real files before go-live
    • Self-hosted n8n in your cloud
  4. Jason
    Jason, COO

    Integrate

    Into production behind your auth, your governance and your monitoring, with your team trained on it.

    • Runs behind your own auth
    • Alerts and logs to your team
    • Your people trained on the system
  5. Maciej
    Maciej, CTO

    Run and improve

    Watched, fixed and extended in weekly sprints by the engineers who built it.

    • Error alerts to the engineers who built it
    • Weekly sprints that you direct
    • The next workflow, scoped with you

What your agency gets from a build with us

We build the agent around your rules and your carriers, and run it in your environment.

What you get
You receiveWorking agents inside your management system and inbox
Where client data livesIn your environment, on self-hosted infrastructure
Fit to your rulesBuilt around your appetite notes, approval steps and carrier requirements
Who approvesA licensed person, on anything that touches coverage
TimelineScoped per workflow and agreed before you sign
After launchThe engineers who built it monitor and extend it

What insurance agents ask about AI

Ask us something else

Will AI replace insurance agents?

No. The agents we build take the reading, re-keying and chasing around each policy, and a licensed person keeps the advice, the coverage decision and the client relationship. At IES the MI system tells finance which numbers moved, and the same people decide what to do about it.

How can insurance agents use AI?

On the work that arrives in volume and follows written rules: submission intake, renewal packets, certificate requests, first notice of loss, after-hours service questions and daily reporting. Each agent prepares the work, and a licensed person approves anything that touches coverage.

What are the best AI tools for insurance agents?

That depends on where your team's hours go. A feature inside one system handles a task inside that system. When a workflow crosses your management system, carrier portals and inbox, a custom build connects them and follows your rules. The AI Audit ranks your candidates by value, effort and risk before you spend on either.

How does AI help with insurance claims?

At the agency end, mostly at first notice of loss. The agent captures the loss from an email, a form or a call transcript, structures it for the carrier's claim submission and confirms receipt to your client, so your staff review a complete report instead of typing one. The carrier's adjuster and your licensed staff make every claim and coverage decision.

How do you use AI safely in insurance marketing?

Run the compliance check before a person reads the draft. In the IES engine, a compliance agent audits every draft against the brand's banned terms, approved phrasing and verified statistics, and must answer correct or improvement needed with each issue named. A person reads only drafts that passed. See the IES case.

Does our client data leave our environment?

No. We build on self-hosted n8n in your cloud, behind your own auth, and agents read your systems with the permissions your staff already have. An external model call carries only the fields that task needs, or goes to a model you host.

What does it cost, and how long does it take?

Every build is priced in a written scope before you sign. If you start with the AI Audit, it costs $10,000 to $100,000 depending on scope and takes 4 to 8 weeks. An AI pilot on one workflow costs $35,000 to $75,000 and takes 2 to 6 weeks. Both fees are credited toward your first build.

Industries on the other side of your claims, certificates and files

Hospitals, landlords, contractors and banks send your team claims, certificate requests and compliance files. These pages cover the work on their side.

Healthcare

AI solutions for healthcare

Claims, denials, payment posting and reporting for providers, with staff approving every action sent to a payer.

See AI solutions for healthcare
Property

AI for property management

Maintenance triage, leasing inquiries, after-hours tenant questions and rent reconciliation inside the system a property team already runs.

See AI for property management
Construction

AI for construction

Contract review, bid leveling, submittals and pay applications for general and specialty contractors, with the contractor's team approving every decision.

See AI for construction
Banking

AI in banking

KYC checks, AML alert cases and loan files prepared inside a bank's systems, with an analyst approving every decision.

See AI in banking

Not ready to book a call yet?

Decide

AI Audit

Eight phases across strategy, operations, data and systems, ending in a ranked 90-day roadmap.

See the AI Audit
Prove

AI pilot

A working proof of concept on your own submissions or renewals, before you commit to the full build.

See the AI pilot
Proof

Case studies

Every build written up with what the work cost or took before and after, including both IES systems.

See the case studies

Put your first workflow on the board

Answer a few questions about your team and what you want automated, then Jason scopes it with you on the call.

Owen, Maciej, Jason, Oskar and Ben