Industries Insurance & InsurTech
Nobody buys a policy.They buy the claim.
AI Engineering and software development for insurance companies.
Everything a policyholder was promised is settled at the claim, and everything an insurer earns is decided by pricing that has to be justified factor by factor. We build the document, risk, and fraud infrastructure underneath both, so the answer arrives with reasoning an adjuster, a regulator, and a customer can each follow.
VinreadyA Vehicle History Platform, VIN to Paid Report
Efuturemedia's consumer vehicle history product, in the market Carfax and carVertical sit in. We built the whole platform to a fixed scope with one engineer: VIN entry, funnel and checkout, the report and its PDF, and the dashboard.
- 48
- Currencies at checkout
- 17
- Report sections
- 6
- Data segments per report
Why insurance companiestrust us with delivery.
A pricing factor nobody can explain, or a fraud ring that scores clean one claim at a time, ends up in loss ratio and a market conduct review. This is why we pay special attention to the most sensitive parts of the system.
A rate has to be justified line by line
When a regulator asks what moved a premium, lift is no answer. Filed rates and proxy discrimination rules mean every factor has to be explained, and none of them can be a protected characteristic in disguise.
Claims arrive as documents and photographs
Cycle time drags because a claim arrives as a phone call, then estimates, invoices, police reports, and forty photos of a bumper. The number policyholders judge you on is how fast that becomes structured data.
Fraud is organised, not opportunistic
The expensive fraud is never one inflated claim. It is a ring, with repeated participants, shared addresses, and the same repair shop across claims, which scoring one at a time cannot see.
The policy admin system predates all of this
Integrations drag because the core holds decades of policies in force and nobody will replace it on your timeline. Everything new has to work around its batch windows and reconcile back to it. That is the constraint, not a phase.
The number belongs to the actuary
Pricing models stall in review when they route around ratemaking's own governance. The ones that reach production inform the actuary's number and are validated inside that framework, not against it.
What insurance demandsfrom engineering and AI.
01Straight-through processing for the simple majority
Most claims are small and unambiguous, settled by a person who did not need to be involved, and every one of them adds days of queue time. Automating that tail end to end, with clear rules for what gets pulled back to an adjuster, frees the expertise for the complex claims where it changes the outcome.
02Risk selection that shows its working
Underwriters are judged on a book they can defend to a regulator and a broker, so a score they cannot interrogate is a score they will not use. Submission triage, appetite matching, and exposure enrichment have to carry their drivers with them, adding context per hour without taking the pen out of anyone's hands.
03Evidence an investigator can follow
A fraud flag with no trail behind it wastes an investigator's day and risks accusing the wrong person. Entity resolution across claims, parties, vehicles, addresses, and providers, with graph analysis over the result, produces a chain of evidence rather than an unexplained score.
04A single view of the book
Reserving, reinsurance reporting, and every model above them depend on exposure, premium, and loss history that reconcile to the systems of record. Two views of the same book that disagree are a governance problem before they are a technical one, so lineage back to source is part of the build, not a cleanup.
Real-life stories of triumph.
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Insurance is something you just expect to work. People don't want to reflect back on deals they've signed years ago. This is why it's crucial that platforms in these domains must provide security, stability, and work like a clock.
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Vinready's vehicle history platform, Five Degrees' loan management core, and Amini's environmental data infrastructure: the systems risk gets priced off.
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Frequently asked questions
Everything insurers usually want to know before putting a model in the claims path. Not here? Ask us directly.
Versioned models, documented data lineage, validation results, and champion-challenger comparison produced as artefacts of the build rather than assembled afterwards for a review.
Your actuarial and model risk functions own approval. We make sure what reaches them is documented well enough to be approved rather than sent back.
Removing protected characteristics is the easy part, and it isn't sufficient on its own: postcode, occupation, and vehicle choice can all reintroduce them. We test outcomes across groups, examine which features drive the effect, and document the analysis.
Where a variable is predictive but its use is legally or ethically indefensible, that's a decision your compliance and actuarial teams make with the evidence in front of them, not one buried in a feature set.
Yes. Guidewire, Duck Creek, Sapiens, or a mainframe core with decades of policies on it: we integrate around batch windows and reconcile back to the system of record rather than establishing a competing one.
New capability ships alongside the core. Replacing it is a separate programme with its own business case.
The closest engagement on our own shelf is Five Degrees, where the loan management we helped build had to live with a core banking platform's batch windows and reconcile back to it.
Minimised, segregated, and retained only as long as the claim and regulation require, with production data reaching only the environments and people that genuinely need it. Development runs against masked or synthetic data wherever it can.
Where health information is involved, the access boundary is designed before the data model, because retrofitting it means rebuilding.
In the parts driven by document handling and routing, substantially, because that is where the waiting is. Intake, extraction, validation, and triage are mechanical work that currently sits in a queue.
What it won't compress is investigation, negotiation, or a third party who hasn't responded. We measure against your current cycle time by claim type, so you can see exactly where the improvement came from.
You do. Code, trained models, pipelines, and documentation are yours, along with the infrastructure access to run them without us.
We hand over a codebase your own team can pick up. Nothing we build is designed to keep you dependent on us.