AI-Powered Automation

How it works

1. 01Client completes your digital intake form, or you upload existing documents (emails, dec pages, applications)
2. 02AI extracts all relevant data: business info, locations, coverage needs, loss history, and additional insureds
3. 03System maps extracted data to the correct fields across ACORD 25, 126, 140, or custom carrier applications
4. 04Your agent reviews the pre-filled forms on screen - editing, correcting, or approving each one before submission
5. 05Approved forms are exported as PDFs or pushed directly into your AMS client record

Before and after

A 12-person independent agency in Ohio writing 35 new commercial accounts per month

Before

Agent Sarah spent 20-30 minutes per new business ACORD submission, manually copying data from client emails, old policies, and scribbled intake notes. Her team averaged 3-4 carrier rejections per week due to incomplete or inconsistent data. One rejection delayed a $22K premium account by 9 days - the client nearly walked.

After

AI pre-fills all forms in under 60 seconds. Sarah reviews each for accuracy (typically a 2-minute check) and submits. Rejection rate dropped from 12% to under 2%. She now quotes 5 additional accounts per week with the reclaimed time, adding roughly $8,500/month in new premium revenue.

Common questions

Does this work with specialty lines like cyber or E&O?

Yes. The AI can be configured for any ACORD form or custom carrier application. During implementation, we train it on your specific lines of business - including specialty and surplus lines. If a carrier has a unique supplemental app, we add that to the system as well.

What if the client intake is incomplete?

The system flags every missing required field and generates a follow-up checklist for your team. It can even draft a client-facing email requesting the specific missing details, so you gather everything in one pass instead of multiple back-and-forth exchanges.

Can it integrate with Applied Epic or AMS360?

Yes — we build integrations with the major AMS platforms, including Applied Epic, AMS360, HawkSoft, EZLynx, and QQCatalyst, where their APIs or export formats allow. The goal is bi-directional flow: data pulled from your AMS into forms, and completed forms written back to client records. The depth of integration depends on what each AMS exposes; until a native connection is in place, the system works from your AMS exports so you get value on day one.

How it works

1. 01FNOL arrives via phone transcript, email, web form, or direct carrier feed - AI processes it immediately
2. 02System extracts key claim details: date of loss, type of claim, estimated severity, involved parties, and initial description
3. 03AI cross-references the insured's active policies to verify coverage and flag potential coverage gaps or exclusions
4. 04For routine claims, the system recommends an adjuster assignment based on expertise, workload, and geography - your team confirms with one click
5. 05Complex or high-value claims are escalated to senior staff with a complete briefing package: extracted details, coverage analysis, and recommended next steps

Before and after

A regional P&C agency managing 1,200 active policies with a 3-person claims support team

Before

Claims coordinator Maria spent her mornings reading through overnight FNOL emails and voicemails, manually checking each policy for coverage, and assigning adjusters. Average triage time was 45 minutes per claim. During storm season, the backlog grew to 3-4 days. Two claims were misrouted to the wrong adjuster type, causing 2-week delays and a formal client complaint.

After

AI triages every FNOL within seconds of arrival. Maria reviews the AI's coverage check and adjuster recommendation - approving 85% with a single click. Average triage time dropped to 4 minutes. Storm season backlog eliminated. No misrouted claims in 6 months. Client satisfaction scores improved 23%.

Common questions

What happens when the AI isn't sure about coverage?

The system flags uncertain coverage determinations and escalates them to your senior staff with a detailed explanation of why it's uncertain - for example, a policy exclusion that might apply but requires interpretation. It never makes a coverage determination on ambiguous cases without human review.

Can it handle both personal and commercial lines claims?

Yes. The triage engine is configured for your specific book of business during implementation. Whether it's a homeowner's water damage claim or a complex commercial liability incident, the system adapts its routing logic to match your workflows and adjuster specializations.

Does it integrate with carrier claims systems?

We can integrate with carrier claims portals and feeds where APIs are available. For carriers without direct integration, the system still processes FNOL from your email or web forms and provides triage recommendations - your team then submits to the carrier through their normal portal.

How it works

1. 01Upload loss run PDF directly to the system, forward it via email, or batch-upload multiple files simultaneously
2. 02AI parses the entire document using OCR and data extraction - handling any carrier format, including scanned documents
3. 03System calculates loss ratios by year, identifies claim frequency patterns, flags severity trends, and detects anomalies
4. 04A one-page intelligence summary is generated with key metrics, trend charts, and recommended underwriting talking points
5. 05Your agent reviews the summary, adds strategic notes, and includes it with the submission package to strengthen positioning

Before and after

A commercial lines agency quoting workers comp for a 50-employee manufacturing client

Before

Agent Mike received a 42-page loss run from the incumbent carrier and spent 90 minutes manually creating a spreadsheet of claims, calculating loss ratios, and writing notes for the underwriter. He missed a pattern of 7 back injuries in 18 months that led to a declined quote - the underwriter wanted a safety program but Mike hadn't identified the trend.

After

AI processed the loss run in 45 seconds. Mike received a summary showing a 2.1 loss ratio, the back injury cluster flagged as a "critical frequency pattern," and a recommended risk mitigation narrative about ergonomic improvements. He included the AI-generated talking points in his submission. Quote was approved with a safety program requirement - and the client was impressed by the agency's thoroughness.

Common questions

What if the loss run is poorly formatted or a low-quality scan?

Our AI uses advanced OCR specifically tuned for insurance documents. It handles carrier-specific formats, scanned PDFs, and even slightly skewed images. If a specific data point is unclear, the system flags it for your review rather than guessing - so you always know what's verified versus what needs a second look.

Can it compare multiple years of loss runs from different carriers?

Yes. Upload loss runs across multiple carriers and years, and the AI normalizes the data into a single timeline. You'll see trends across the full history, not just one carrier's view. This is especially valuable for accounts that have moved between carriers.

Does it generate content I can include in submissions?

Absolutely. The summary includes pre-written underwriting talking points that position the account favorably. You can copy these directly into your submission narrative, edit them to match your voice, or use them as a starting point for your own analysis.

How it works

1. 01Upload 2-5 competing quotes, proposals, or dec pages in PDF or image format
2. 02AI extracts all coverage limits, deductibles, exclusions, sublimits, endorsements, and pricing from each document
3. 03System creates a normalized side-by-side comparison, highlighting meaningful differences in coverage
4. 04Coverage gaps and potential E&O risks are flagged prominently - e.g., "Quote B excludes products liability, critical for this manufacturer"
5. 05Your agent reviews the comparison, adds strategic recommendations, and generates a client-facing summary document

Before and after

An agency presenting renewal options to a $2M-revenue manufacturing client with general liability, property, and umbrella coverage

Before

Agent Lisa spent 3 hours building an Excel comparison of three GL quotes, manually typing coverage details from each PDF. She missed that Quote B had a laser exclusion for products liability - a critical gap for a manufacturer. The client selected Quote B based on lower premium. Six months later, a product defect claim was denied. The client filed an E&O complaint against the agency.

After

AI compared all three quotes in 2 minutes, immediately flagging Quote B's products liability exclusion as a "Critical Gap - High E&O Risk." Lisa confidently recommended Quote A, explained the exclusion clearly to the client using the AI-generated summary, and documented the coverage discussion. Client renewed with full protection. No surprises, no E&O exposure.

Common questions

What types of policies can it compare?

General liability, property, workers comp, commercial auto, professional liability, cyber, umbrella, and EPLI. Any commercial policy with structured coverage details. The system normalizes different carrier formats so you get true apples-to-apples comparisons, even when carriers structure their quotes differently.

Can it create client-facing comparison documents?

Yes. It generates a clean, branded comparison document you can send directly to clients or present during renewal meetings. The client-facing version uses plain language explanations rather than insurance jargon, helping clients understand exactly what they're buying.

How does it help with E&O documentation?

Every comparison includes a timestamped record of what was analyzed, what gaps were identified, and what recommendations were made. This creates a defensible audit trail showing your agency performed thorough due diligence - invaluable documentation if coverage questions arise later.

How it works

1. 01AI connects to your AMS and reviews each client's policy portfolio, claims history, and communication patterns 90 days before renewal
2. 02System identifies coverage gaps by comparing the client's portfolio against industry norms for their business type and size
3. 03Churn risk is assessed using interaction frequency, claims experience, and payment history - at-risk accounts are flagged for priority outreach
4. 04Personalized renewal emails are drafted for each client, mentioning their specific business, recent changes, and targeted coverage recommendations
5. 05Your agent reviews each draft, personalizes further if needed, and sends - or schedules the outreach sequence to send at optimal times

Before and after

A 400-client commercial agency with 82% retention and an 8% cross-sell rate

Before

The agency sent generic "your policy is up for renewal" emails 30 days before expiration. No personalization, no coverage recommendations. Clients felt like a policy number. Retention sat at 82% for three years. Cross-selling happened only when agents remembered during a phone call. The agency was leaving an estimated $180K in annual premium on the table from unidentified coverage gaps.

After

AI analyzed every client portfolio and identified 47 clients without cyber coverage, 23 without EPLI, and 31 with umbrella limits below recommended levels. Personalized renewal emails went out 90 days early with specific recommendations. Retention climbed to 91% in the first year. Cross-sell rate jumped from 8% to 22%. The agency wrote an additional $145K in new premium from existing clients alone.

Common questions

Is it truly personalized or just mail merge?

Truly personalized. The AI considers each client's industry, coverage history, claims experience, business changes, and even recent news about their company. A restaurant client gets different messaging than a tech firm. It's not "[First Name], your policy expires soon" - it's a substantive review of their coverage with specific recommendations that demonstrate your agency's value.

Can we review everything before it's sent?

Always. The system operates in draft mode by default - every email, every recommendation goes through your team first. You can approve, edit, or reject any message. Most agencies start fully manual and gradually automate routine renewals while keeping high-touch accounts under direct agent control.

How does it identify cross-sell opportunities?

The AI compares each client's policy portfolio against industry benchmarks and your agency's own recommendation patterns. A $5M-revenue manufacturer with GL, property, and auto but no cyber or EPLI? That gets flagged immediately. The system also tracks what your best producers typically recommend for similar accounts.

How it works

1. 01Upload your carrier appetite guides, rate sheets, and submission guidelines - PDFs, spreadsheets, or any format
2. 02When a new risk arrives, enter key details: industry class, revenue, location, loss history, and special exposures
3. 03AI instantly matches the risk against all carrier appetites, ranking best-fit options with specific reasoning for each recommendation
4. 04Your producer reviews the matches, selects target carriers, and submits - the system tracks actual outcomes to refine future recommendations
5. 05Over time, the AI learns which carriers actually accept your submissions (not just what their appetite guide says) and adjusts rankings accordingly

Before and after

A mid-size agency with 25 carrier appointments and 3 producers (one senior, two newer)

Before

New producer Ashley relied on her senior colleague for carrier guidance, but he was often in meetings or out on appointments. She submitted a restaurant risk to a carrier that explicitly excluded restaurants - declined in one day. She submitted a habitational risk to a carrier that had quietly stopped writing that class - another wasted submission. Her hit rate was 40%, and she was getting frustrated. Senior producer hoarded market knowledge as job security.

After

All 25 carrier appetites were loaded into the system. Ashley enters a restaurant risk, and the system instantly suggests three carriers with specific reasoning: "Carrier X actively writing restaurants under $3M revenue in OH with clean loss history." Her hit rate jumped from 40% to 78% in the first quarter. Market knowledge is now shared, searchable, and improving with every submission outcome.

Common questions

How current does the carrier appetite data stay?

You control updates - upload new appetite guides whenever carriers change their programs. Most agencies update quarterly or when they receive carrier bulletins. The system also flags when actual submission outcomes diverge from expected appetites, suggesting a guide may need updating.

Can it learn from our actual submission results?

Yes - this is one of the most powerful features. Over time, the AI tracks your actual hit rates by carrier, class code, and risk characteristics. It adjusts recommendations based on real results, not just published appetite guides. If a carrier says they write restaurants but declines 90% of yours, the system learns that.

Does it work for specialty and surplus lines?

Especially well for specialty and surplus lines, where narrow appetites make carrier selection critical. Load your wholesale broker guidelines and E&S carrier appetites, and the system helps you avoid out-of-appetite submissions that waste time and damage relationships.

How it works

1. 01AI monitors your client communications (email, call transcripts), submissions, and policy changes in real-time
2. 02System cross-references client statements and needs against their actual policy coverages, exclusions, and endorsements
3. 03When a potential E&O risk is detected - like a coverage gap, undocumented request, or conflicting exclusion - an alert is generated immediately
4. 04Your agent reviews the alert, takes corrective action (add endorsement, document declination, update coverage), and the resolution is logged
5. 05A complete audit trail is maintained: what was flagged, when, who reviewed it, and what action was taken - invaluable for E&O defense

Before and after

A regional agency that had settled two E&O claims in the past 3 years, totaling $68K

Before

Agent Karen quoted a BOP for a contractor. During a phone call, the client casually mentioned "we sometimes do jobs in New York." Karen forgot to note it. The policy bound with NY excluded. Six months later, a claim in New York was denied. The client sued the agency for E&O. Settlement: $45K plus increased E&O premiums for the agency.

After

AI scanned the call transcript and flagged: "Client mentioned New York operations - current policy excludes NY. Action required before binding." Karen received the alert, called the client to discuss, and added the NY endorsement. When a claim later occurred in New York, it was covered. No lawsuit, no settlement, no increased E&O premiums. The system paid for itself with a single prevented claim.

Common questions

What types of E&O risks does it detect?

Coverage gaps between client needs and policy terms, undocumented verbal requests or commitments, policy exclusions that conflict with known client operations, endorsements that were removed at renewal, and situations where a client's business has changed but coverage hasn't been updated. The system learns your agency's specific risk patterns over time.

How does the audit trail work for E&O defense?

Every alert, review, and action is timestamped and stored in a tamper-proof log. If an E&O claim arises, you can produce documentation showing: "On [date], the system flagged [risk]. Agent [name] reviewed it on [date] and took [action]." This demonstrates your agency's systematic due diligence - a powerful defense in any E&O proceeding.

Does it catch issues in phone calls?

If you use call recording with transcription (or we can set this up for you), the AI scans call transcripts for mentions of exposures, coverage requests, or commitments that might create E&O risk. This catches the most dangerous gaps - verbal requests that never get documented in writing.

How it works

1. 01Upload or compile your submission package - ACORDs, loss runs, supplemental apps, narratives, photos, and supporting documents
2. 02AI scans all documents against your target carrier's specific submission requirements (which you've configured during setup)
3. 03System generates a completeness checklist: green checks for complete items, yellow warnings for potential issues, red flags for missing requirements
4. 04Your team addresses any gaps before submitting - gathering missing documents, updating outdated loss runs, completing empty fields
5. 05Submit a clean, complete package to the carrier, dramatically reducing back-and-forth and quote turnaround time

Before and after

An agency submitting a complex restaurant package to a specialty carrier that requires detailed supplemental information

Before

CSR Jessica submitted what she thought was a complete package. The carrier came back 3 days later requesting the liquor liability supplemental, updated loss runs (hers were 18 months old, carrier requires 12 months), and a kitchen hood suppression certificate. Jessica scrambled to get the documents, resubmitted 5 days later. By then, the client had accepted a competitor's quote that came in faster.

After

Before submitting, the AI flagged all three gaps: missing liquor app, outdated loss runs, and missing suppression certificate. Jessica gathered everything upfront and submitted a complete package. Carrier quoted in 48 hours. Client was impressed by the speed and professionalism. Won the $18K premium account.

Common questions

Does it know specific carrier requirements?

Yes. During setup, we configure the system with your key carriers' submission guidelines. We build templates for each carrier that reflect their specific requirements - including quirks like "this carrier always wants 5 years of loss runs for habitational risks over $1M." The system gets smarter over time as you add feedback from carrier responses.

Can it check for data consistency across documents?

Absolutely. The AI cross-references data across all documents in the submission: matching business names on ACORD forms vs. applications, verifying consistent effective dates, checking that named insureds align, and flagging discrepancies like different revenue figures on different forms.

How long does it take to set up carrier templates?

Initial setup covers your top 5-10 carriers in the first two weeks. Each carrier template takes about 30 minutes to configure based on their submission guidelines. After that, adding new carriers is quick - and the system suggests templates based on similar carriers you've already set up.

How it works

1. 01System connects to your AI-assisted workflows (quoting, underwriting support, communications) and logs decision patterns
2. 02Statistical analysis runs continuously, comparing outcomes across protected classes, geographic regions, and business categories
3. 03When a statistically significant disparity is detected, the system generates a detailed finding report with data, analysis, and context
4. 04Your compliance officer or agency principal reviews the finding, investigates root causes, and decides on corrective action
5. 05All audits, findings, and resolutions are documented in a compliance log that's ready for regulatory review

Before and after

A growing agency that had recently deployed AI-assisted quoting tools and was preparing for a state regulatory review

Before

The agency had no systematic way to audit their AI-assisted decisions for fairness. During a routine state department of insurance inquiry, they couldn't demonstrate that their AI tools weren't introducing bias into quoting or coverage recommendations. The inquiry expanded into a formal investigation, costing $35K in legal fees and 4 months of management distraction - even though no actual bias was found.

After

With the Fairness Monitor in place, the agency produced a comprehensive audit report within 24 hours of the regulatory inquiry: decision patterns across demographics, geographic analysis, and documented review processes. The inquiry was resolved in 2 weeks with no findings. The regulator commended the agency's proactive approach. When the agency later detected a minor geographic pricing disparity in their quoting tool, they corrected it before it became a regulatory issue.

Common questions

What types of bias does the system detect?

The system monitors for disparate impact across race, gender, age, geography, and other protected classes. It analyzes patterns in quoting outcomes, coverage recommendations, communication frequency and tone, and claims handling. It uses statistical methods (like the four-fifths rule and regression analysis) that are aligned with NAIC and state regulatory frameworks for AI fairness in insurance.

Is this relevant for small and mid-size agencies, or just large carriers?

Increasingly relevant for agencies of all sizes. States like Colorado, Connecticut, and New York are implementing AI governance requirements that apply to any entity using AI in insurance decisions - including agencies. Having a documented fairness monitoring process demonstrates due diligence and positions your agency ahead of regulatory requirements that are expanding rapidly.

Does this slow down our AI workflows?

No. The monitoring runs asynchronously - it analyzes decision patterns in the background without adding latency to your AI-assisted workflows. You won't notice any performance impact. Reports are generated on a scheduled basis (weekly or monthly), with immediate alerts only for statistically significant findings that warrant prompt review.

How it works

1. 01Connect your email inbox (Gmail, Outlook, or IMAP) to the AI assistant
2. 02When a quote request arrives, AI automatically identifies it and extracts key information
3. 03System creates a structured summary: lines of business, effective dates, locations, exposures, and special requirements
4. 04Summary appears in your dashboard for agent review before it pushes into your AMS or CRM

Before and after

Commercial agency receiving 40-60 quote requests per week via email

Before

CSR team spent the first hour of each day reading through lengthy email threads, highlighting key details, and manually entering them into spreadsheets. Critical deadlines were sometimes missed buried in long emails, and two $15K+ premium opportunities were lost last quarter because urgent requests were not triaged in time.

After

Every morning the team sees a clean dashboard of structured quote summaries. They immediately know which quotes are urgent, what coverage is needed, and all relevant details. They review the AI extraction in under a minute per request and are quoting within minutes of arrival instead of hours.

Common questions

What if the email is vague or missing information?

The AI extracts what it can and clearly flags missing fields. It can draft a follow-up email to the prospect asking for the specific missing details, which your agent reviews and sends.

Does it work with forwarded emails from brokers?

Yes. It parses complex email threads with multiple forwards and replies to extract the core risk information from the underlying conversation.

How accurate is the extraction?

Typically 95%+ accuracy on structured fields. Every extraction is reviewed by your agent before it moves into the AMS or gets actioned.

How it works

1. 01Install a lightweight browser extension in Gmail, Outlook 365, or your email platform
2. 02When composing a reply, select from preset insurance templates (quote follow-up, policy delivery, claims update, renewal reminder)
3. 03AI reads the thread context and generates a professional, situation-aware draft in seconds
4. 04Your team reviews, edits as needed, and sends - every email goes out under human control

Before and after

Agency team of 5 sending 40+ client and carrier emails per day

Before

The team spent a collective 3+ hours daily writing similar emails: quote follow-ups, policy delivery messages, claims status updates, renewal reminders, and certificate requests. Everyone had their own style, quality was inconsistent, and new CSRs needed weeks to match the agency's voice.

After

With the AI drafting assistant, the team uses consistent professional templates. Average drafting time dropped from 4-5 minutes to under 1 minute per email. The team saved 2+ hours per day collectively. Clients commented on improved clarity and response times, and new hires ramped up in days instead of weeks.

Common questions

Can we customize the templates and tone?

Yes. During setup we build templates around your most common email types and train the AI to match your agency's voice, whether that's formal, friendly, or somewhere in between.

Does it access our email data in the background?

No. It only sees the email thread you are actively composing when you click to use it. Nothing is stored, read, or trained on in the background.

What types of insurance emails does it handle?

Quote follow-ups, policy delivery, claims updates, renewal reminders, certificate requests, coverage questions, and carrier inquiries - essentially any routine insurance email your team writes repeatedly.

How it works

1. 01AI gathers data from your intake forms, quotes, and client files
2. 02System generates a professional proposal with coverage summaries, pricing tables, and recommendations
3. 03Applies your agency branding (logo, colors, disclaimer language) automatically
4. 04Your producer reviews the draft, makes any final edits, and approves the branded PDF before sending

Before and after

Agency pitching a commercial package to a growing restaurant group

Before

Producer Tom spent 2+ hours copying data into a Word template: client info, coverage descriptions, pricing tables, and disclaimers. Formatting broke constantly. He found a typo after sending the proposal to the prospect.

After

AI generated a complete 8-page proposal in 3 minutes with all client details, coverage explanations, and pricing tables perfectly formatted. Tom reviewed it, made one tweak, and sent it within 15 minutes of receiving quotes. Won the account over a competitor who was still assembling their proposal.

Common questions

Can we customize the proposal sections and language?

Yes. During setup we define your exact proposal structure, coverage descriptions, and disclaimers. You can maintain multiple templates for different situations (new business, renewal, specialty lines).

Does it include coverage explanations or just pricing?

Both. The AI generates plain-language coverage explanations alongside pricing so clients understand what they are buying, which improves close rates.

How long does setup take?

3 weeks, including gathering your branding, building custom templates, and training the AI on your writing style. After that, proposals are generated in minutes.