What a Rexto engagement actually looks like.

The situation

A regional insurer’s claims intake team manually read every first-notice-of-loss submission, whether email, PDF, or a web form, to decide which queue it belonged in and which documents were still missing before an adjuster could open the file. The team already knew the shape of the fix: a classifier and a documentation checklist, not a discovery process.

Because the requirements, source systems and compliance constraints were already mapped internally, the insurer wanted a fixed scope and fixed price, not a time-and-materials engagement that could drift.

What we build

Rexto delivered against a written scope and a fixed price agreed before the build started, with a two-week buffer built into the schedule rather than a change-order process for anything within the original brief.

• Intake classifier — A model that reads incoming claim submissions across email, PDF and web-form channels and assigns a severity tier and line of business, replacing a manual first read.
• Documentation checklist engine — Rules-plus-model logic that checks each claim against the documentation required for its type and flags what's missing before it reaches an adjuster.
• Claims-system integration — Output writes directly into the insurer's existing claims management system as a routed, annotated record, with no separate dashboard for adjusters to check.

Delivery timeline

What your team owns

The insurer's IT team owns the deployed classifier and checklist engine inside their own claims system from go-live. Rexto's fixed-scope contract included a defined warranty period for the agreed deliverable, after which the client team runs it independently.

What to expect

• Every incoming claim is tiered and routed before it reaches a human queue, instead of during a manual first read.
• Adjusters see a documentation checklist against each claim instead of assembling one from memory.
• The build shipped against the fixed price and scope agreed at the start, with no change-order surprises.
• Model decisions are logged with the input that produced them, so a disputed routing can be reviewed.

Related

The situation

A fast-growing payments fintech was losing money to card-testing and account-takeover fraud faster than its two-person risk team could write rules for. It needed a real-time scoring system and, longer-term, an in-house team that could own and evolve it. But it had no data science hires yet and no twelve months to wait for them.

Rather than staff the whole thing on contract indefinitely, the fintech wanted Rexto to build the capability, run it in production while the client hired, and then leave, with a transfer date agreed before the engagement started, not negotiated at the end.

What we build

Rexto built and operated the system as a de facto team embedded in the client's infrastructure, recruiting and onboarding the client's own hires into it as they joined, rather than working in parallel to them.

• Real-time scoring service — A transaction-risk model serving scores in the payment authorization path at sub-100ms latency, replacing static rule thresholds with a model retrained on labeled fraud outcomes.
• Feature pipeline — A streaming feature store computing device, velocity and network-graph signals per transaction, built on infrastructure the client's own engineers could inherit without a rewrite.
• Case-review tooling — An analyst console for the risk team to review flagged transactions and label outcomes, feeding the labels back into retraining. This is the loop the in-house team took over at transfer.

Delivery timeline

What your team owns

On the transfer date, the client's own risk-engineering team, hired and onboarded during the operate phase, owns the scoring service, the feature pipeline and the retraining loop outright. Rexto's access is revoked the same day, per the transfer plan agreed at contract signing.

What to expect

• The client has a staffed, in-house team running the fraud function, not a system nobody who remains can maintain.
• The transfer date was fixed at the start of the engagement, not extended as a rolling dependency.
• Scoring logic and feature pipelines were built on infrastructure the client's own engineers could take over without a rewrite.
• The case-review and retraining loop that keeps the model current transferred along with the system, past the model weights alone.

Related