GenAI Document Ingestion
Extracts data from submissions, ACORD forms, loss runs, and policy documents.
Insurance runs on documents, decisions, and data.
AI earns its place when it takes work off your people without taking judgment away from them.
Our AI + Intelligent Automation solutions help insurers:
Stop paying people to re-key documents
Give underwriters and adjusters better information, faster
Automate the decisions that genuinely don't need a person
Build the data foundation everything else depends on
Products + Services
AI + Intelligent Automation Solutions
What we build, implement, and support in this category.
Products
AI Copilots
Assistants built into the workflow an underwriter or adjuster already uses.
Submission Intake and Clearance
Ingestion, clearance, and routing, built for specialty and E&S volume.
Automated and Straight-Through Underwriting
Rules-based auto-decisioning for risks that don’t need a human.
Claims Workflow and Straight-Through Processing
Automation of the routine steps in a claim.
Risk Scoring and Predictive Pricing Models
Machine learning models that score risk and inform pricing.
Predictive Analytics
Forecasting across pricing, reserving, retention, and catastrophe exposure.
Insurance Data Warehouse and Data Lake
The unified data foundation everything else depends on.
BI Dashboards and Reporting
Management and operational reporting.
Services
Data, Analytics and AI Strategy
Data strategy, AI use case selection, and governance design. The usual front door for a board mandate.
AI Governance
Model documentation, bias and drift monitoring, human oversight, and the policies that keep a model in production.
Data Governance and Privacy Compliance
Data lineage, retention policy, and privacy compliance.
Data Cleansing and Quality
Standardizing, de-duplicating, and enriching datasets. Usually the prerequisite nobody budgeted for.
Data Annotation and Labeling for AI
Preparing and labeling training data for insurance models.
Analytics and Reporting as a Service
Ongoing managed analytics and dashboards, scoped to a defined outcome.
Use Cases
Where Insurers Put Us To Work
These are places AI does real work in an insurance operation. Each one has been built for an insurer already, and most engagements start in one and expand from there.
Document and data ingestion
Reading what arrives unstructured and turning it into fields your systems can use: submissions, ACORD forms, loss runs, policy documents, claim correspondence. The highest-volume and most measurable AI work in insurance right now, and the least controversial internally, because nobody enjoys re-keying.
Decision support inside the workflow
Copilots and summarization built into the screen your underwriter or adjuster already works in: risk summaries, exposure research, claim narratives, next-best-action prompts. Not a separate tool to log into. The measure is whether the person uses it without being reminded to.
Automated decisioning
Straight-through underwriting for the risks that don’t need a human, automated triage and routing, straight-through processing for routine claims. Rules first, models where rules can’t reach, and always with a defined referral path back to a person.
The data foundation
Data warehouse and lake builds, cleansing and standardization, pipeline engineering, annotation and labeling for model training, and the reporting layer that lets you see whether any of it worked. Frequently the first project, occasionally the whole project.
Governance and control
AI governance frameworks, model documentation, bias and drift monitoring, human oversight design, data governance and privacy compliance. The work that keeps a model in production once someone starts asking how it decided.
who we serve
Solutions often look different depending on where you sit
Carriers
The data exists and the mandate is real, but the core system makes it hard to reach. AI work usually starts at the edges: intake, documents, and the reporting layer, rather than inside the core.
Paktolus for Carriers →MGAs
Speed to market is the whole business. AI that shortens submission to quote matters more than AI that produces insight.
Paktolus for MGAs →Mutuals
Policyholder trust is the asset. Automation that stays visibly accountable to members, not automation that hides the decision.
Paktolus for Mutuals →Agencies and Brokerages
Volume across many carriers, in many formats. The gain is in ingestion, matching, and getting producers out of spreadsheets.
Paktolus for Agencies + Brokerages →Wholesalers
Appetite matching and submission triage at E&S volume, where the manual version simply doesn’t scale.
Paktolus for Wholesalers →Reinsurers
Cession data arriving in inconsistent shapes from dozens of sources. Extraction and normalization before anything predictive.
Paktolus for Reinsurers →Results
How would results like these impact your business?
Automating quote submission ingestion with OCR and AI
A mid-sized E&S insurer in commercial auto took submissions the way brokers preferred to send them: email attachments, mixed formats, handwritten in places. We built an end-to-end intake pipeline that parsed the attachments, extracted and validated the data, and pushed it structured into their underwriting system for scoring and prioritization.
90%
less manual data entry
85%
straight-through processing within 90 days, from zero
5x
faster initial
underwriting review
AI-powered claims processing for a home insurer
Property damage assessment was manual, subjective, and exposed to fraud during high-volume storm and flood events. We implemented computer vision analysis of pre and post FNOL property images, enriched with geolocation, policy data, claimant history, and weather feeds, producing a confidence score and an annotated report for the adjuster rather than a decision that replaced them.
50%
faster claim processing
25%
improvement in adjuster productivity
20%
decrease in fraudulent or exaggerated payouts
Modernizing personal lines underwriting with external data
A homeowners insurer was underwriting on self-reported property data and manual validation, producing inaccurate risk assessments, inflated replacement costs, and a steady stream of post-bind premium adjustments. We built a property intelligence layer using computer vision, geospatial analytics, and third-party data to validate roof type, square footage, pool presence, and hazard proximity at the point of quote.
18%
reduction in premium leakage
35%
faster quote issuance
2x
underwriter capacity through automation of routine review
Our approach
The question we always start with is:
“What does done actually look like for you?”
Some clients come to us with a defined project. Others come with a mandate and no idea what the shape of the solution is. Either way, the answer to that question is what we build against. We plug in wherever you need us, from a single initiative to a full-scale program, and we stay as long as it’s useful.
CEO and Co-Founder
From consulting to execution.
Advise
Discovery, use-case scoping, build-versus-buy analysis, and data readiness assessment. Some engagements stop here, and that's a legitimate outcome. You leave with a decision you can defend and a plan someone can actually execute.
Deliver
Custom development or implementation and configuration of what you already run. Phased, specified, and measured against the outcome you chose. When it goes live, your team knows how to run it.
Support
Ongoing support, enhancement, and managed services, for what we built or for what you were running before we met. Models drift. Somebody has to watch them.
We’re technology and vendor agnostic. Sometimes the right answer is custom-built around exactly how your team works. Sometimes it’s optimizing the platform you already run on. We don’t lead with a preferred stack, we lead with your goals.
Common Questions
Questions Insurers Ask Us About AI
Start with a process that runs on a high volume of documents or repetitive decisions, where the data already exists in usable form and the outcome can be counted. Submission intake, document extraction, and claim triage meet that test more often than anything else. Use the boring process with measurable payback to fund the more ambitious work. What we’d avoid is beginning with the use case that sounds most impressive in a board update, because those tend to have the least available data and the longest path to production.
Sometimes, and it’s worth finding out before you commit a budget. A well-scoped document extraction project can often work against data as it stands today, because it creates clean structured data rather than depending on it. Predictive models, risk scoring, and anything reading across systems generally can’t. Part of what discovery establishes is which of the two you’re proposing, and whether a data foundation project needs to come first.
Not in the work we do. The engagements that succeed give an experienced person better inputs, faster, with the reasoning visible so they can check it. Automated decisioning has a real place for routine, low-complexity risks and claims, and we build that too, always with a defined referral path back to a human. The judgment work stays where it is, which is also what makes the tool get adopted rather than worked around.
We design for it at the start rather than retrofitting it, because retrofitting is expensive and sometimes forces a model out of production. In practice that means model documentation, recorded reasoning and source data behind each output, bias and drift monitoring, defined human oversight, and an audit trail an examiner can follow. We also build AI governance frameworks as standalone engagements for insurers who have models in production and no policy governing them.
We work across the tooling our clients already run rather than a preferred stack. On AI engagements that commonly includes OpenAI and Anthropic models, Azure and Google Cloud, Snowflake and Databricks for data, and insurance-specific document and underwriting platforms. That is a sample rather than a list of preferences. We are not a reseller, so the right tooling is whatever fits the operation you already have.
Both, and the decision is part of discovery rather than something we bring in with us. A great deal of what insurers need is available in existing platforms and models and only needs configuring and integrating properly. Some of it, particularly anything shaped by your appetite, your rules, or your data, has to be built.
Then we say so. A meaningful share of what gets brought to us as an AI problem is better solved by a rules engine, a fixed integration, or a process change, all of which are faster to build, cheaper to run, and easier to defend to a regulator. Telling you that costs us a bigger project and saves you a failed one. It is also, in our experience, why clients come back.
Tell us your biggest pain point.
We'll tell you how we've solved for it.
Twenty minutes, a candid conversation. If we’re not the right fit, we’ll say so.