Hesper AI pricing and packaging: how investigation gets priced and how to get a quote

Hesper AI does not publish a fixed public price list, and neither does any other enterprise insurance fraud-tech vendor - FRISS, Shift, and Verisk are all quote-based. That is not opacity; it is what happens when price is a function of your claims volume, your lines of business, and how the platform integrates with your claims system rather than a per-seat sticker. So the useful question is not "what does Hesper cost" but "what determines my number."

This post answers that question for the people who own the budget line. It walks through the five pricing models live in the category, the six cost drivers that set a carrier's quote, the per-case unit economics that make the whole model work, and the five numbers to bring to a quote conversation. The spine of all of it is one comparison: roughly $150 per AI investigation against roughly $2,500 for a manual SIU case. That two-order-of-magnitude drop is the reason investigating 100% of flagged claims becomes affordable for the first time.

For the finance reviewer, the pricing page is the ROI page. This is the companion to the CFO ROI memo for AI claims investigation, which carries the full payback math; here the focus is what you actually pay for and what moves the number.

Enterprise insurance fraud-tech is quote-based across the entire category, and the absence of a public price list is the norm rather than a Hesper quirk. The reason is structural: a platform that investigates flagged claims is priced against the volume of flagged claims a carrier generates, and that volume swings by two orders of magnitude between a small regional carrier and a Top-25 national. A single published number would be wrong for almost everyone who read it.

The category proves the point. FRISS, one of the most established detection vendors, has no public pricing page at all - friss.com/pricing returns a 404. Shift Technology and Verisk are the same: enterprise quotes negotiated against a carrier's book, not a self-serve checkout. When every serious vendor in a category prices by quote, the right move for a buyer is to stop hunting for a sticker and start assembling the inputs that produce an accurate one.

There is a second structural reason specific to Hesper. Detection and investigation are different layers of the stack, and Hesper sits at the investigation layer that no detection vendor occupies. From fraud detection to fraud resolution is two jobs, not one: detection flags the suspicious claim, investigation resolves it end-to-end. That means Hesper is a separate budget line from your FRISS, Shift, or Verisk contract - additive spend, not a swap. The pricing conversation is not "replace what I have" but "fund the layer my current stack does not cover." The size of the problem that layer addresses is large: the Coalition Against Insurance Fraud estimates insurance fraud steals at least $308.6 billion every year.

There are five live pricing models in the category, and each aligns the vendor's incentive on a different thing. Knowing which model you are being quoted under tells you what the vendor is optimizing for - and whether that matches your coverage goal.

Per-case or per-investigation

You pay for each flagged claim investigated. This aligns cost directly with usage and is the cleanest map to the value of an investigation platform, because the value is investigations completed, not seats occupied. It is also the model that makes the unit economics legible: at roughly $150 per case, your annual investigation budget is simply your flagged-claim volume times the per-case figure. The risk to watch is unbounded spend on a high-flag book, which tiered pricing exists to cap.

Per-seat

You license each investigator using the platform. This is the legacy software model, and it has a structural mismatch with autonomous investigation: the whole point is that one investigator now reviews far more cases, so a per-seat price penalizes exactly the leverage you are buying. Per-seat makes sense for tools where a human does the work; it makes less sense where the platform does the work and the human reviews and signs.

Platform subscription, tiered, and outcome-based

The remaining three round out the category. Platform subscription is a flat annual fee for access, simple to budget but loosely tied to value. Tiered pricing brackets cost by claims-volume bands, which gives a carrier a predictable number and caps per-case exposure. Outcome or value-based pricing ties a portion of the fee to loss avoided or recovered, aligning the vendor's incentive most tightly with the carrier's but requiring agreed measurement. For autonomous investigation specifically, per-case and tiered-volume map most naturally to the value delivered, because both track investigations completed rather than seats or flat access.

The right model is the one that aligns the vendor's incentive with your coverage goal - getting 100% of flagged claims investigated rather than the roughly 25% manual teams reach. Per-case and tiered-volume both do that. Per-seat works against it.

Six drivers set the number on a quote. None of them is seat count. They all trace back to one thing: how many flagged claims you generate, and how deep the investigation on each one has to go.

1. Annual claims volume. More claims means more flagged claims to investigate. This is the single largest driver, which is why a quote is organized around claims volume rather than carrier name.
2. Fraud-flag rate. About 10% of property-casualty claims involve fraud, but your actual flagged volume is set by your detection vendor's threshold. A conservative threshold flags fewer claims at higher confidence; an aggressive one flags more, raising both coverage need and per-period investigation count.
3. Lines of business. Workers compensation, auto bodily injury, and property carry different investigation depth. A staged-accident ring or a workers comp claim with medical and financial layers needs more of the 15+ investigation phases than a clean first-party property loss.
4. Claims-system integration. Whether you run Guidewire ClaimCenter, Duck Creek, or another system of record shapes the integration work. An investigation layer consumes a flagged claim and returns a report as a case attachment, so the surface is small, but the integration shape still factors into deployment scope.
5. Data-source breadth. The number and type of external sources an investigation draws on - public records, OSINT, document forensics, financial-pattern analysis - affects depth per case.
6. Deployment scope. A scoped pilot on one line costs far less than full production coverage across the book. This is the lever a carrier controls most directly: start narrow, prove the economics, then expand.

One driver deserves a CFO-specific note. The cost of fraud is not abstract overhead - it shows up in premium. The Insurance Information Institute, citing the Coalition Against Insurance Fraud, attributes as much as 14% of personal auto premium to premium leakage. When a line carries leakage that large, the investigation depth on that line is doing real loss-cost work, and the cost driver is also the value driver.

The sticker is also not the whole cost. Integration, data plumbing, and change management carry their own line items, which is the total-cost-of-ownership story we lay out in the hidden integration costs of legacy claims AI. An investigation layer with a small integration surface keeps that tail short, which is part of why deployment scope, not headcount, is the variable a buyer should be tuning.

The entire pricing logic collapses to one number: cost per investigated case. The category benchmark for autonomous investigation is roughly $150 per AI case against roughly $2,500 for a manual SIU case. Everything else - models, drivers, scope - is downstream of that single ratio, because it is what determines how much of your flagged volume you can afford to investigate.

Here is why the ratio matters more than any sticker. A manual SIU investigation runs 14+ days per case, and a single investigator completes roughly 10 investigations a month. That throughput ceiling is why most US P&C carriers fully investigate only about 25% of flagged claims - the other 75% are paid, denied without full work, or queued. The constraint has never been detection recall; it is the human hours available downstream of the flag. AI investigation runs each case in hours, lifting throughput toward 800+ cases per investigator per month and coverage toward 100%. The per-case cost is what converts that throughput into an affordable budget.

Run the math on a representative mid-market carrier to see why per-case cost, not seat count, is the budget driver. Take 50,000 annual claims. At the roughly 10% P&C fraud rate, that is on the order of 5,000 fraud-suspect claims a year (an illustrative derivation, not a guaranteed flag count). Investigating all 5,000 at the roughly $150 AI benchmark is about $750,000. Investigating the same 5,000 at the roughly $2,500 manual cost is about $12.5 million - which is precisely why no manual team investigates all 5,000, and why coverage sits at 25%. The per-case cost is the gate on coverage.

The 5,000-claim line is an illustrative derivation from the roughly 10% fraud rate applied to a mid-market claim volume; your number depends on your detection threshold. But the shape holds at any volume: the AI per-case cost is roughly one-sixteenth of manual, and that ratio is what turns 100% coverage from a budget impossibility into a line item.

There are two distinct ROI surfaces, and conflating them is the most common mistake in a pricing conversation. The first is cost per investigated case - an order-of-magnitude reduction on work you already do. The second, and larger, is the loss recovered from the flagged claims you previously could not afford to investigate. Detection-only ROI never reaches the second surface.

Start with the detection benchmark, accepted at face value. Shift Technology publicly cites 4x ROI in the first year on a detection deployment. That is a real return, and it is a detection return: it improves the quality and recall of the flags. What it does not touch is the roughly 75% of flags that never get fully investigated, because investigation is a different layer. Detection ROI makes the flags better; it does not get the flagged claims worked.

Hesper's ROI lever is that second surface - the coverage gap. Lifting investigation from roughly 25% to 100% of flagged claims means loss recovered from claims that were previously paid or denied without full work. The pool is large: US P&C fraud runs about $45 billion a year and workers compensation about $34 billion, per the Insurance Information Institute citing the Coalition Against Insurance Fraud. Most flagged claims at a given carrier are resolved without full investigation, so closing that gap is the dollar lever detection-only spend cannot reach. The two ROIs stack: detection improves flag quality upstream, investigation works the flags downstream, and a carrier budgets for both.

For the finance reviewer who needs the full payback model, two companion pieces carry it. The CFO ROI memo above lays out year-one net spend, payback, and IRR; three modeled carrier ROI scenarios run the math by carrier tier.

A faster, more accurate quote comes from arriving with five numbers. Because price is a function of your book, the conversation moves quickly once these are on the table, and you avoid the back-and-forth that comes from quoting against assumptions.

1. Annual claims volume across the lines you want covered. This is the single largest input to the number.
2. Your current flag rate and the detection vendor in place, if any. This sets your actual flagged volume and tells us whether Hesper runs downstream of detection or standalone with its built-in detection.
3. Your top two or three fraud-prone lines. Workers comp, auto bodily injury, and property drive different investigation depth.
4. Your claims system of record - Guidewire ClaimCenter, Duck Creek, or other. This shapes the integration scope.
5. Your target coverage. Whether you are aiming to lift one line to 100% or expand across the book changes deployment scope, which is the variable you control most directly.

With those five numbers, the quote is grounded in your economics rather than a sticker that would be wrong for your book. The framing to hold onto is that Hesper is an additive line to your detection spend, not a replacement for it - complementary to FRISS, Shift Technology, and Verisk - so the budget question is how to fund the investigation layer your current stack does not cover, and the per-case economics are what make funding it work.

Phasing the spend is its own decision, and it usually starts narrow. A scoped pilot on a single high-fraud line proves the per-case economics before the budget commits to production scope. For the Claims VP funding and sequencing this, the Claims VP deployment playbook lays out the budget and 12-month timeline for moving from pilot to full coverage.

There is no rate card to compare against, and that is deliberate: the only number worth having is the one scoped to your book. Talk to us with the five inputs above and you will leave the first conversation with a scoped figure, the agents that fit your lines, and a pilot shape to prove the economics on.