Atomic Build/Insurance

Stop chasing renewals. Automate them.

Atomic Build is a forward-deployed product and engineering team that automates your entire policy renewal lifecycle—from extracting renewal requirements out of legacy documents to routing escalations to underwriting. Ship it in weeks. Keep renewal rates up, lapsed policies down.

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Your renewal lifecycle is still being run by humans and spreadsheets.

Agents juggle renewal dates across fragmented systems. Compliance checks happen manually. Renewal correspondence gets stuck in email queues. Policies lapse. Retention rates stay flat. Your underwriting team spends half their time on renewals instead of new business.

Manual document extraction
Someone still reads each legacy policy document to find renewal dates, requirements, and rider details. Per policy. Every renewal cycle.
Compliance gaps slip through
Missing coverage verification, outdated underwriting requirements, regulatory changes—none of these get flagged until a renewal hits a wall or lapses.
Slow renewal correspondence
Renewal notices are templated and mailed. Responses trickle back weeks later. Follow-up sequences are manual. Lapsed policies become urgent escalations.
Distributed portfolios, no visibility
Agents hold renewals. Underwriting sees them last. Finance doesn't know which ones are at-risk. Nobody has a single source of truth for renewal status across the book.
Underwriting drowning in renewals
Your underwriting team spends 40–60% of time on renewal underwriting instead of new business. Renewal turnaround times stretch. Policies lapse before they can be reviewed.
No automation roadmap
You know the renewal cycle is a pain point. But nobody's scored where AI would actually move retention rates or reduce manual headcount.

Renewal automation isn't about sending emails faster. It's about eliminating the manual handoffs that cause policies to lapse.

We embed a small product and engineering team inside your renewals operation, score where AI cuts the most cycle time and touches the most policies, and ship production workflows in weeks. Your renewal team gets leverage. Your underwriting team gets their time back. Lapsed policies drop.

Automate the longest pole in the tent
We map your entire renewal lifecycle—document extraction, compliance flagging, correspondence, escalation, underwriting review—and only build against the steps that actually extend renewal cycles or cause lapses.
Extract from anything, instantly
Whether your policies live in PDFs, legacy policy management systems, or hybrid archives, AI pulls renewal requirements, dates, and compliance flags without manual data entry.
Flag compliance and risk before it becomes urgent
AI compares renewal requirements against current underwriting guidelines, coverage limits, and regulatory changes—so gaps surface weeks before the renewal hits underwriting, not days.
Generate, not just template
Renewal correspondence gets personalized and routed based on risk level and requirements. Follow-up sequences trigger automatically. Escalations land in underwriting queues with full context, not as cold files.
Humans stay in control of underwriting decisions
AI surfaces the renewal, flags risks and requirements, and routes to underwriters—but underwriters make the renewal decision. No rip-and-replace of underwriting judgment.
Measure in renewal rate and lapsed policies
Every engagement targets metrics that matter: lower lapse rates, faster underwriting turnaround, higher retention on renewals due for action.

From renewal bottleneck to production AI in six weeks

Most insurance partners start with an Opportunity Sprint to identify the highest-leverage renewal workflows, then move into a focused build. We stay forward-deployed the entire time.

Opportunity Sprint · Week 1
We embed with your renewals, underwriting, and tech teams to map the entire renewal lifecycle, quantify where policies lapse, and score the highest-leverage AI opportunities.
Scope & system design · Week 2
We translate the chosen opportunity into a concrete system design: document extraction approach, compliance logic, routing rules, integration points with your policy management system and underwriting queues.
Forward-deployed build · Weeks 3–6
Atomic Build engineers sit inside your renewals team and ship the system into production against real policies and real renewal cycles—not a staging sandbox.
Operate, measure, and scale · Week 7+
We monitor lapse rates, underwriting turnaround, and agent productivity against week 1 metrics. Queue the next renewal workflow—each build is faster because the extraction and routing substrate is already in place.

Relevant services

Most engagements combine three or four of these. Start with what hurts most.

OperationsCompliance & RiskAgent OperationsUnderwritingFinance & RetentionAutomate the longest pole in the tentExtract from anything, instantlyFlag compliance and risk before it becomes urgentGenerate, not just templateHumans stay in control of underwriting decisionsMeasure in renewal rate and lapsed policies

AI workflows we've shipped for insurance renewal teams

Each build starts from a scored opportunity inside your renewal operation. These are the patterns we see move retention rates and free up underwriting capacity the fastest.

  • Legacy policy document extraction

    AI reads PDFs, policy management system exports, and archived documents to pull renewal dates, coverage details, rider information, and underwriting history. Feeds into your renewal queue automatically.

    0 manual data entry per renewal

  • Compliance gap detection

    AI compares each renewal against current underwriting guidelines, regulatory requirements, and coverage limits to flag gaps—missing medical exams, outdated coverage riders, lapsed coverage certifications—before the renewal hits underwriting.

    −86% renewals with surprise compliance issues

  • Renewal correspondence generator

    AI drafts personalized renewal notices, follow-up sequences, and compliance request letters. Routes high-risk renewals to agents with priority flags. Triggers automated reminders to policyholders on lapse timelines.

    −42% days to first correspondence

  • Intelligent renewal routing and context

    AI prepares each renewal for underwriting review—extracts prior loss history, flags coverage changes, surfaces compliance gaps, and routes based on risk tier and underwriter specialty. Underwriters see full context, not a cold file.

    −54% underwriting review time per renewal

When to talk to us

Some patterns we hear on the first call. If two or more of these are true, the conversation is worth having.

  • Someone still reads each legacy policy document to find renewal dates, requirements, and rider details. Per policy. Every renewal cycle.
  • Missing coverage verification, outdated underwriting requirements, regulatory changes—none of these get flagged until a renewal hits a wall or lapses.
  • Renewal notices are templated and mailed. Responses trickle back weeks later. Follow-up sequences are manual. Lapsed policies become urgent escalations.
  • Agents hold renewals. Underwriting sees them last. Finance doesn't know which ones are at-risk. Nobody has a single source of truth for renewal status across the book.
  • Your underwriting team spends 40–60% of time on renewal underwriting instead of new business. Renewal turnaround times stretch. Policies lapse before they can be reviewed.
  • You know the renewal cycle is a pain point. But nobody's scored where AI would actually move retention rates or reduce manual headcount.

Let's Connect

Decide what is worth building first.

Most insurance partners start with an Opportunity Sprint to identify the highest-leverage renewal workflows, then move into a focused build. We stay forward-deployed the entire time.

What insurance leaders ask before engaging

How do we identify the highest-value AI automation opportunity in our renewal operation?
That's what the Opportunity Sprint does. We map your entire renewal cycle—document extraction, compliance checking, correspondence, underwriting review, lapse tracking—and quantify the cost of each bottleneck. Then we score where AI cuts the most time or touches the most policies. Most carriers find that intelligent document extraction and compliance flagging move the needle fastest.
How does AI extract renewal requirements from legacy policy documents?
Our AI models are trained on your policy document formats—PDFs, legacy system exports, archived files—to intelligently extract renewal dates, coverage details, underwriting history, and rider information. We don't require manual template setup for each document type. The system learns your formats and feeds extracted data directly into your renewal queue or policy management system.
Can we use this without hiring a full internal AI team?
Yes. Most of our insurance partners don't have dedicated AI teams when we start. We bring product and engineering and partner with your renewals, underwriting, and tech leads to ship the system. Many clients eventually hire internal engineers around what we deploy—but it's never a prerequisite.
How do we make sure underwriters don't lose control of renewal decisions?
AI handles extraction, compliance flagging, and routing. Underwriters make the renewal decision. We design the system so AI surfaces the renewal with full context—prior loss history, compliance gaps, coverage changes—and underwriters review and approve, not the other way around. Escalation paths are built in for edge cases and exceptions.
What happens if a policy document is damaged, archived, or in a non-standard format?
Our models handle a wide range of document quality and formats—scanned PDFs, low-resolution archives, legacy system outputs. Difficult cases get flagged for manual review, but the vast majority extract cleanly. During the Opportunity Sprint, we assess your document archive and refine the extraction approach based on what we actually see in your portfolio.
How long until we see results?
First production renewal AI is live by week 6, processing real policies from your renewal queue. You'll see lapse rate and underwriting turnaround improvements within the first month of operation. The longer-term benefit compounds as we add workflows—each subsequent build is faster because the extraction and routing substrate is already in place.