Of 107 insurance technology companies analyzed, 75-85% now claim "AI" somewhere in their positioning. Fewer than 5% were built with AI from the first line of code. The rest bolted it on.
That distinction is not a marketing nuance. It is the difference between infrastructure that compounds intelligence with every transaction and infrastructure that runs the same blind processes with a better interface.
This is a framework for telling them apart.
Open any insurance technology company's website. Count the seconds until you see the letters A and I. On average, it takes fewer than three.
"AI-powered underwriting." "AI-driven claims." "AI-native platform." The language has achieved total saturation. It no longer describes a capability. It describes a marketing consensus.
Here is what the consensus obscures: there are two fundamentally different architectures being sold under the same two letters.
"AI-enabled" means a legacy system learned a new trick.
"AI-native" means the system was born thinking.
The difference is not incremental. It is structural. And in an industry where the product being sold is certainty, the architecture producing that certainty determines whether the promise is real.
We believe the insurance industry needs a clear framework for evaluating which claims are architecture and which are decoration. Not because we are neutral observers. We are not. We built Slingshot on one side of this line, and we believe the distinction matters enough to name it publicly.
Two architectures. One vocabulary. Entirely different outcomes.
Definition: A legacy system — built on traditional databases, manual workflows, and batch processing — with AI capabilities added as a layer on top.
Architecture pattern
Legacy core system (often 10-30 years old)
API wrapper or middleware layer
AI module (bolted on top, reads from legacy data)
New UI (modern skin, legacy bones)
What this produces
The honest description
A faster horse. The underlying architecture — how data is created, stored, traced, and trusted — remains unchanged. The AI layer can only be as good as the data it reads, and the data has no provenance.
Definition: A system designed from its first line of code with AI as the foundational operating logic — not added to an existing architecture but inseparable from it.
Architecture pattern
Data objects with provenance from creation
Intelligence layer woven into every operation
Institutional memory that compounds cross-partner
Single architecture serving all parties simultaneously
What this produces
The honest description
A different species. The architecture does not add intelligence to existing processes. It replaces the processes with intelligent infrastructure. The data model, the trust layer, and the learning system are one thing — not three things wired together.
This is not a quality judgment about any specific company. It is a structural observation about two architectures that produce fundamentally different outcomes — and the market's failure to distinguish between them.
Architecture is not a back-office concern. It determines what an insurance platform can do, what it can prove, and what it can become.
Three consequences separate AI-native from AI-enabled at the operational level.
In an AI-enabled system, data arrives from legacy pipelines without provenance. The AI operates on information it cannot verify. Reconciliation is faster but still necessary — because the system does not know where its own data came from.
In an AI-native system, every data object is born with a chain of custody. Reconciliation is not accelerated. It is eliminated by architecture.
Industry stat: Only 4% of insurance companies fully automate reconciliation. 60-70% of finance team time is spent on manual data gathering.
An AI-enabled system learns from the data it is shown. Each partner's operations are siloed. When an employee leaves, their institutional knowledge leaves with them. The system starts from scratch every time.
An AI-native system compounds intelligence across every transaction, every partner, every decision. Day 1 performance equals Year 5 knowledge. The memory is in the architecture, not in anyone's head.
Competitive data: Fewer than 5% of 107 companies analyzed have any form of cross-partner institutional memory.
An AI-enabled carrier still carries the OpEx of its legacy infrastructure — the 500-person team, the vendor stack, the manual workflows that AI accelerates but does not replace. The cost floor stays.
An AI-native carrier operates from a fundamentally different cost structure. Processes that required teams now require architecture. The economics of serving a $5M MGA program become viable — not because the carrier tries harder, but because the infrastructure costs less.
Market reality: Legacy carrier OpEx structures make sub-$20M MGA programs structurally uneconomic. The average State National program is approximately $133M.
AI / ML Module
Reads from legacy data. No provenance. No memory.
API Wrapper / Middleware
Translation layer. Format conversion. Batch sync.
Core Admin System
Policy, billing, claims. Batch processing. Nightly runs.
Legacy Database
No provenance. No lineage. The source of truth nobody can audit.
Each layer speaks a different language. Data loses context at every boundary. The AI at the top can only work with what survives the climb.
Intelligent Operating System
AI is the operating logic, not a module. Every operation is intelligent by default.
Institutional Memory Engine
Cross-partner intelligence. Compounds with every transaction. Never forgets.
Artifact-Based Data Trust
Every data object: birth certificate, chain of custody, version history. Audit-ready by default.
Single Data Model
One truth. One language. MGA, carrier, reinsurer see the same data at the same time.
Every layer speaks the same language. Data carries context from birth to audit. The intelligence is in the foundation, not the ceiling.
This is not a theoretical framework. The data is visible to anyone willing to look.
107
Insurance technology companies analyzed
75-85%
Claim "AI" in their positioning
<5%
Built AI-native from the first line of code
0%
Offer artifact-based data trust with provenance
When every company claims AI, the claims escalate. Our analysis of the competitive landscape found a pattern we call the AI Superlative Olympics — a race to own the biggest adjective.
Socotra
"The most mature AI in insurance"
Sure
"The first AI-native insurance platform"
Hyperexponential
"The most proven AI-native platform"
Corgi
"The first full-stack AI insurance company"
Every one of these companies builds valuable products. The claims are not fraudulent. But when "most mature," "first," "most proven," and "first full-stack" all coexist, the language has ceased to describe reality. It has become a category tax — the price of entry into a conversation where every participant sounds identical.
The question is not who said "AI" first. The question is whose architecture makes the claim structural.
Swiss Re — $35 billion in annual revenue, unlimited capital, the deepest reinsurance relationships on earth — built iptiQ as a carrier-as-a-service platform. They invested over $100 million. They reached $750 million in annual revenue. They operated across multiple European markets.
They are now selling it off.
The lesson is not that carrier-as-a-service cannot work. The lesson is that the problem is not capital. It is architecture. Swiss Re attempted to retrofit a digital carrier experience onto infrastructure that was not designed for it. They had more capital, more relationships, and more market access than any startup will ever have. It was not enough.
You cannot bolt a digital-native experience onto an analog-native foundation. The architecture has to be right from the first line of code — or no amount of money fixes it.
We scored 88 features across 107 competitors and measured market saturation — how many companies claim each capability. The results reveal where the industry has converged and where genuine whitespace remains.
Nearly universal. "Weeks not months" is the most common phrase in insurtech.
Three-quarters of competitors claim AI leadership in some form.
Table stakes. No one builds on-premise anymore.
"Seamless partnership" — the phrase that describes everything and nothing.
Perfect universal gap. Zero competitors distinguish between snapshot and restated data.
The pattern is clear. Where the industry has invested, saturation is near-total. Where genuine architectural innovation lives, the market is nearly empty. The gap between what the industry claims and what the industry has built is the architecture gap.
These are not subjective judgments. They are architectural questions with verifiable answers. Ask them of any platform — including ours.
The question: Pick any data object in the system. Can the platform show you where it was born, who created it, every transformation it has undergone, and the current version — in under 30 seconds?
What AI-native looks like: Every data object — every policy record, every claim, every premium calculation, every bordereau entry — carries provenance from the moment of creation. Chain of custody is architectural. The system knows the complete history of every byte.
What AI-enabled looks like: The AI operates on data migrated from legacy systems. Provenance begins at the point of migration, not at the point of creation. The system has a gap in its memory — and that gap is where reconciliation errors live.
Why it matters: A reinsurer evaluating collateral requirements needs to trust the data. When the bordereau number is provably the real number — not the version after quiet adjustments — collateral requirements drop. When the data has no lineage, collateral stays trapped.
The question: If the three most experienced people at the carrier quit tomorrow, what does the platform remember?
What AI-native looks like: The platform retains every decision pattern, every underwriting judgment, every claim trend, every cross-partner correlation. Institutional knowledge is in the architecture. People enhance the system. They do not contain it.
What AI-enabled looks like: The AI module still runs, but the tribal knowledge — the context behind why certain decisions were made, the relationships between programs, the lessons from past losses — leaves with the people who held it.
Why it matters: The insurance industry's average employee tenure is declining. Every departure is a partial amnesia event. A platform with institutional memory compounds with time. A platform without it resets with every departure.
The question: Does your platform eliminate reconciliation or accelerate it?
What AI-native looks like: Reconciliation does not exist as a process. When MGA, carrier, and reinsurer operate on the same data model with the same provenance chain, there is nothing to reconcile. The numbers match because they were never separate.
What AI-enabled looks like: AI-assisted reconciliation. The same process — comparing disparate data sets, finding discrepancies, resolving conflicts — but faster. The fundamental problem (multiple versions of truth) remains. The AI just finds the errors more quickly.
Why it matters: The industry spends 60-70% of finance team time on manual data gathering and reconciliation. AI-enabled reconciliation cuts that to 30-40%. AI-native architecture cuts it to zero. The difference is not incremental efficiency. It is the elimination of an entire category of work.
The question: When a new MGA joins the platform, does their Day 1 underwriting intelligence reflect only their own data — or the collective intelligence of every partner who came before them?
What AI-native looks like: Day 1 performance equals Year 5 knowledge. The new partner inherits aggregate intelligence from every transaction across the entire collective — without compromising any individual partner's competitive data. The platform gets smarter with every member.
What AI-enabled looks like: Day 1 performance equals Day 1 data. The new partner starts from their own claims history, their own premium data, their own loss ratios. The AI can only learn from what it has been given. It does not know what it has not seen.
Why it matters: This is the network effect test. Platforms with cross-partner intelligence create compounding value — each new member makes the platform smarter for all members. Platforms without it create isolated data islands that do not benefit from the collective.
The question: How many vendor contracts does it take to run the full carrier operation? Is the AI a vendor — or is it the operating system?
What AI-native looks like: The carrier is vertically integrated. AI is not a vendor product plugged into the stack. It is the stack. Underwriting, compliance, data, reporting, and intelligence are one system. The platform has zero multi-year vendor contracts creating technical debt.
What AI-enabled looks like: The carrier operates 8-16+ vendor point solutions — core admin, claims management, policy admin, billing, analytics, AI/ML module, document management, compliance. Each vendor contract creates technical debt. Each integration boundary creates a potential failure point.
Why it matters: Vendor-assembled carriers are structurally prevented from achieving true AI-native intelligence. The AI module can only be as smart as the weakest integration in the stack. And every multi-year vendor contract locks the carrier into architecture decisions that were made by someone else, for someone else's use case.
These five tests are not proprietary. They are open. Use them on every platform you evaluate — including ours. Architecture that is truly native has nothing to hide.
The strategic implication for existing carriers is uncomfortable but straightforward: the architecture gap cannot be closed by adding AI to existing infrastructure. It requires choosing between two paths.
Add AI modules to existing infrastructure. Wrap APIs around legacy systems. Hire a data science team. Launch an innovation lab. Announce a partnership with an AI vendor.
This path produces measurable improvements. Faster claims processing. Better fraud detection. More efficient underwriting triage. These are real gains.
But the foundation does not change. The data model does not change. The provenance gap does not close. The institutional memory does not compound. The cost structure does not fundamentally shift.
This is the path Swiss Re took with iptiQ. They had $35 billion in revenue and unlimited capital. It was not enough.
Start from a blank file. Design the data model with provenance from the first byte. Build intelligence into the operating logic, not onto it. Accept that the existing architecture cannot be incrementally evolved into what the market now requires.
This path is expensive. It requires writing off decades of technical investment. It demands a founding team that bridges insurance domain expertise with systems architecture. It takes time.
But it produces a fundamentally different carrier — one whose economics can serve the embedded market, whose data can be trusted by default, and whose intelligence compounds rather than resets.
This is the path that creates new carriers. Not better versions of old ones.
You cannot retrofit a birth certificate onto data that was born without one. The provenance has to be there from the first byte — or it is not there at all.
For Managing General Agents — and for SaaS founders entering embedded insurance for the first time — the architecture gap creates both a risk and an opportunity.
If your carrier partner is AI-enabled rather than AI-native, you inherit their architecture gap. Their data limitations become your data limitations. Their reconciliation timelines become your reconciliation timelines. Their inability to serve sub-$20M programs becomes your inability to find a carrier that works at your scale.
The MGA does not operate in a vacuum. The carrier's architecture determines the ceiling of what the MGA can achieve.
When the reinsurer asks for data lineage and the carrier cannot produce it, the MGA's collateral gets frozen. When the carrier's institutional memory resets with every staff departure, the MGA has to re-explain their book from scratch. When the carrier's OpEx structure makes the MGA's program uneconomic, the relationship ends — usually after the MGA has invested months in integration.
The embedded insurance market is growing at 30%+ annually. It reached $144 billion in 2025 and is projected to exceed $1.4 trillion by 2034. The growth is in high-frequency, low-volume policies — the $25 checkout add-on, the rental protection, the embedded warranty.
Legacy carriers spent 200 years optimizing for the rare and catastrophic. Their economics cannot serve this market. That is not a strategy failure. It is a math problem.
For MGAs and SaaS founders who can find carrier infrastructure built for this inverted market — AI-native, with data trust, institutional memory, and economics that work at scale — the opportunity is structural. You are not competing for a share of an existing market. You are building the market that legacy economics made impossible.
Every small business in every town. Every embedded checkout. Every $25 policy at 30% year-over-year growth.
The carrier your MGA runs on is not just a partner. It is the foundation. If the foundation is AI-enabled, your ceiling is a better version of the old world. If the foundation is AI-native, there is no ceiling.
We are not neutral observers of this framework. We built Slingshot because we believe the distinction is real and the market consequence is structural.
Slingshot is an AI-native insurance carrier — a fully licensed carrier with 50-state capacity — built from the first line of code for the high-frequency, low-volume, embedded insurance market.
We did not add AI to an existing carrier. We did not build a technology platform that depends on someone else's carrier. We built the carrier and the intelligence as one system, because the architecture problem cannot be solved any other way.
Every data object carries a birth certificate, chain of custody, and version history. The bordereau number is provably the real number. Auditable by default, not by scramble.
The platform learns from every transaction across every partner. Day 1 performance equals Year 5 knowledge. The intelligence is in the architecture, not in anyone's calendar.
We profitably serve MGA programs from $1M to $20M that legacy carrier OpEx structures are structurally locked out of touching. The AI-native cost base makes the math work.
Slingshot was built by a Chief Actuary who managed $2 billion in carrier operations at a $10 billion public company and left because the institutional resistance to data infrastructure was intractable — and who also writes production code.
Our co-founder is our first customer. Brock built and runs Vertical Insure, an embedded insurance MGA with 400,000+ policies. He experienced every frustration — six-month onboarding, opaque data, poor economics, carrier friction. The voice of the MGA is permanently at the founding table. Not in an advisory role. As an owner.
Our investors built the embedded payments playbook — the same structural logic that turned Stripe from a startup into infrastructure. Rally Ventures has been backing embedded finance from its earliest days.
We are the only carrier whose economics were built from the first line of code for the high-frequency, low-volume, embedded insurance market that legacy carriers are structurally locked out of serving.
The question is not whether. It is when, and who builds it.
The $3 trillion insurance industry has operated on the same fundamental architecture for decades — spreadsheets emailed between people who have never met, bordereaux that could be 60 days out of date, reconciliation cycles that consume weeks and entire teams.
AI-enabled systems make this faster. AI-native systems make it unnecessary.
The embedded insurance market is growing at 30% annually. It needs carrier infrastructure that can serve a $25 policy as efficiently as a legacy carrier serves a $25 million program. That requires a different architecture. Not a better version of the old one.
The market has inverted. The infrastructure has not caught up.
We did not build Slingshot to be a better version of what already exists. We built it to be what should have existed all along.
Slingshot
The AI-native carrier for embedded insurance.
April 2026
Framework published for open use.