For claims, underwriting, and policy operations.
We build the software and AI that runs inside a carrier, MGA, or broker — claims automation, underwriting support, policy administration, document intake, and fraud detection. It’s grounded in your own policy and claims data, wired to the core systems you already run.
Every automated decision is explainable and auditable, with a human in the loop where it counts. We build the operations and intelligence layer on top of your core; we do not set your rates or replace your policy-administration system.
Because the work is buried in documents and locked in core systems that were never built to talk to anything. A claim arrives as a PDF, an email, photos, and an adjuster’s notes; a submission lands as a spreadsheet and a broker’s narrative.
The data a carrier needs to triage, price, and decide is present — it’s just trapped in unstructured form, re-keyed by hand, and routed by tribal knowledge. So cycle times stretch, leakage creeps in, and the expensive people spend their day on data assembly instead of judgment.
That’s before the slower, quieter losses: the days a complex claim waits to be assigned, the submissions an underwriter can’t get to, the policy-service request that takes a week of emails. The gap isn’t a new core system or more headcount — it’s the software layer that reads what the documents already say, scores and routes the work, and puts a recommendation in front of the right person while the decision still belongs to them. That layer is what insurance software development, done properly, delivers.
This isn’t one product. It’s a set of high-leverage applications that sit on top of your existing policy and claims systems. For each: what it does, the benefit it produces, and a one-line illustration of how that plays out. Every one is built so a person owns the decision the software recommends.
Ingests the first notice of loss, classifies and routes it, extracts the facts from documents and photos, flags the straightforward claims for fast settlement and the complex ones for an adjuster. Benefit — shorter cycle times and lower leakage, with adjusters freed for the cases that need judgment. Simple claims stop waiting behind complex ones, and the routine data assembly comes off the adjuster’s desk.
For example, a low-value auto glass claim with clean documentation is triaged and moved to settlement in minutes instead of sitting in a queue for days — while the disputed total-loss claim goes straight to a senior adjuster with the file already assembled.
Reads submissions, pulls the relevant data from third-party and internal sources, surfaces the risk signals, and gives the underwriter a prioritized, pre-assembled view — without setting the price. Benefit — faster quote turnaround and more submissions worked per underwriter. The underwriter spends time on the risk decision, not on hunting down and re-keying data.
For example, a commercial submission that used to take days to assemble arrives pre-summarized with the missing-data gaps already flagged, so the underwriter can decide to quote or decline the same day — the rating and the final call stay with them.
Builds the workflows, portals, and integrations around policy issuance, endorsements, renewals, and servicing — so changes that took a chain of emails happen in a guided flow wired to your core. Benefit — lower service cost and faster turnaround on routine policy work. Endorsements, certificates, and renewals stop bottlenecking on manual handoffs.
For example, a policyholder requests a coverage change and gets it processed through a self-service flow that updates the core system directly, instead of waiting on a back-office queue.
Turns the PDFs, ACORD forms, emails, and images that flood every insurance process into structured, validated data the rest of your systems can use. Benefit — the re-keying disappears and the downstream automation finally has clean inputs.
For example, a loss-run report or an ACORD certificate is read and validated on arrival — fields extracted, exceptions flagged for a human — instead of a clerk typing it into the system field by field.
Scores claims and applications for fraud indicators, surfaces the suspicious patterns and network links, and routes the right cases to the Special Investigations Unit with the evidence assembled. Benefit — more of the genuinely suspicious claims caught earlier, with fewer false accusations of honest policyholders.
For example, a claim that fits a known staged-accident pattern is flagged for SIU review before payment, while clean claims flow through untouched — so investigators spend their time where the recovery actually is.
Answers policy, claim-status, and coverage questions for policyholders and brokers from your own approved sources, escalating anything it isn’t sure of. Benefit — instant answers on routine questions and lower call volume, without unapproved or invented answers.
For example, a broker checking the status of a submission or a policyholder asking what their deductible is gets a grounded, sourced answer at any hour — built on our conversational AI work, with escalation to a person the moment confidence drops.
The scope below is the operations and intelligence layer — what reads, scores, routes, and recommends on top of your core insurance systems. We integrate with your policy-administration, claims, and rating systems; we do not replace your core, and we do not set actuarial rates — the pricing and the final decision stay with your people.
We build the intake, triage, routing, and straight-through-settlement workflows around your claims system — including the machine-learning models that classify and prioritize claims — validated against your historical claims so the routing is trustworthy, not a black box your adjusters learn to override.
Submission ingestion, third-party data enrichment, and risk-signal surfacing that give underwriters a pre-assembled view — built on your own data through our data engineering work. We support the underwriting decision; we don’t set the price.
Portals, guided workflows, and integrations for issuance, endorsements, renewals, certificates, and self-service — wired into your core PAS rather than replacing it, plus modernization of the legacy policy or claims systems that are too brittle to build on, without ripping out what works.
The intake pipelines that turn ACORD forms, loss runs, PDFs, emails, and images into structured, validated data — with exceptions routed to a person — so every downstream automation has clean inputs instead of garbage.
Models and case-management tooling that score claims and applications for fraud signals, surface network and pattern links, and route assembled cases to investigators — tuned against your own outcomes to balance catch rate against false positives on honest policyholders.
Grounded, auditable conversational assistants for claim status, coverage questions, and broker servicing — answering only from your approved sources, with human-in-the-loop escalation designed in.
What you get when you hire us — all assigned to you under full work-for-hire IP transfer
The same delivery model behind all our work, tuned for a regulated carrier’s operations — one accountable lead, fixed scope, no handoffs to account managers.
Scope the use case and the loss it targets (claim cycle time, leakage, quote turnaround, service cost), and confirm the data and core-system access exist to support it. Run as our AI readiness assessment, with the honest “this one isn’t ready yet” call included.
Output: a ranked plan & the metric we’ll be judged on
Connect to your policy-administration, claims, and rating systems through governed, permissioned interfaces, and stand up the document-intake pipeline. We read from and write back to your core; we don’t replace it.
Output: a trusted data foundation & live integrations
Develop the application and, where it applies, train and validate the model against your historical record, in your own cloud or on-prem environment, with explainability and an audit trail on every automated decision and a human in the loop where a wrong call is costly.
Output: a working system tested on your real data
Shadow mode, then a contained pilot on one line or process, prove the metric moves, then scale — your team trained to operate, retrain, and extend it.
Output: a production system & a team that owns it
Most engagements reach production in 4–8 weeks, payment is tied to the ROI we agreed at kickoff, and full work-for-hire IP assignment is signed before we start.
A system that touches claims, premiums, or a policyholder’s coverage has to be right, explainable, and auditable — which is exactly the production discipline Silicon Prime is built on. To be straight about it: our named case studies are not in insurance. What transfers directly is the engineering rigor a regulated financial-operations system demands, and we’ll show it through honest adjacent examples rather than an insurance case we don’t have.
The clearest proof of that rigor is BJ’s Restaurants — a 200+ location operation whose software is critical to daily operations. Over four years we moved their release cadence from every two weeks to twice a week while sustaining zero critical defects, through evals before release, staged rollout, and continuous production monitoring. That’s a different industry, but it’s precisely the “ship fast, never break the system the business depends on” standard a claims or policy-administration integration has to meet.
Closer to the financial-systems side, we built YardClub end to end — a marketplace whose payments and transaction infrastructure processed $120M+ before it was acquired by Caterpillar in 2017 — so the money-moving, transaction-integrity engineering that insurance operations share with fintech is familiar ground, even though that engagement was a marketplace, not a carrier.
Silicon Prime is a Stanford-rooted Responsible AI lab, founded in 2011, run by founder Kelvin Tran — 20+ years of production engineering, including multimillion-dollar systems for one of the world’s largest enterprises, and personally accountable for every engagement. If your problem is a genuine stretch for what we’ve shipped, we’ll tell you, scope a contained pilot to prove it before you commit, and put the accountability in writing.
Auditable by design, with a human on the decision. In insurance, an automated call you can’t explain is a liability. Every model and workflow we build carries an audit trail and explainability, with human-in-the-loop review where a wrong decision costs a claim or a customer — because Responsible AI is our founding charter, not a bolt-on.
The operations layer, honestly scoped. We build the intelligence and workflows on top of your core — claims, underwriting support, policy servicing, intake, fraud — not a replacement core system, and we don’t set actuarial rates. That boundary means a faster, lower-risk engagement and no overpromising on the parts that belong to your actuaries and your PAS vendor.
Production discipline first. A carrier system is judged on uptime, accuracy, and trust. The same evals-before-release, staged-rollout, monitor-after discipline that holds a 200+ location operation at zero critical defects is what we bring to a system that touches claims and premiums.
Founder-led, built to transfer. One accountable lead, not a relay of account managers; and the code, models, and integrations are assigned to you with your team trained to run them when we step back.
What carriers, MGAs, and brokers want to know before they build software on top of their core systems.
No. We build the software and intelligence layer on top of the core you already run — claims automation, underwriting support, policy-servicing workflows, document intake, and fraud detection — integrating through governed, permissioned interfaces rather than rip-and-replace. Where a legacy policy or claims system is genuinely too brittle to build on, we’ll modernize that piece without ripping out what works, and we’ll be honest about which approach your situation actually needs. Keeping that boundary clear is part of why our engagements are fast and lower-risk.
No — and that’s deliberate. We build software that assembles data, surfaces risk signals, scores and routes work, and recommends — but the rating and the final decision stay with your underwriters, adjusters, and actuaries. Every automated recommendation carries an explanation and an audit trail, and human-in-the-loop review is designed in wherever a wrong call costs a claim or a customer. We support the decision; we don’t quietly automate away the accountability for it.
Insurance operations are their own discipline. A bank’s core problems are payments, lending, accounts, and real-time transaction decisioning — covered on our fintech software development page. A carrier’s are claims, underwriting, policy administration, document-heavy intake, and fraud across long-lived policies and regulated lines. The transaction-integrity and auditability engineering overlaps, but the workflows, data, and regulatory shape are insurance-specific — which is why this is a separate practice, not a relabeled fintech page.
By tuning the model against your own historical outcomes and keeping a human on every consequential decision. A fraud model is only useful if investigators trust it, which means balancing catch rate against false positives and routing flagged cases to your SIU with the evidence assembled for a person to judge — not auto-denying claims. Insurance fraud is an estimated $308.6 billion-a-year problem in the U.S. (Coalition Against Insurance Fraud), so the recovery is real — but so is the cost of accusing the honest, and we design for both.
We’ll be straight: our named case studies are in restaurants (BJ’s, a 200+ location operation held at zero critical defects for four years) and a financial-transaction marketplace (YardClub, $120M+ processed, acquired by Caterpillar), not in insurance carriers. What carries over is the production-reliability and transaction-integrity engineering a regulated insurance system demands. For a first insurance engagement we scope a contained pilot on one process — claims triage, document intake, an SIU model — to prove the value before you commit. The accountability is the founder’s, in writing.
The software runs in your own cloud or on-prem environment under your access controls; integrations to your core are permissioned and scoped to what the use case needs; and every engagement starts with an NDA and a security review. We document every data path and put an audit trail on every automated decision so your compliance, risk, and IT teams can verify rather than trust. We build to be auditable; we don’t provide regulatory or legal sign-off — that stays with your compliance function, and we build the evidence trail that makes their job possible.
You do — completely. The applications, trained models, document pipelines, and integrations transfer under full work-for-hire IP assignment signed at kickoff, and your team is trained to operate, retrain, and extend them. Keep us on a reduced retainer or take the keys; the engagement is built around the handover, not around locking you in.
Most engagements reach production in 4–8 weeks under a fixed-scope, ROI-tied model with one accountable lead, and we typically prove the metric on a single process first before scaling. Build cost depends on scope — our AI development cost guide gives real ranges — and we set the target metric at kickoff so the value is measured against a baseline, not assumed.
Thirty minutes · No pitch deck
Bring the process you want to attack — a claims backlog, slow quote turnaround, a document-intake bottleneck, a fraud-leakage problem — and we’ll tell you honestly whether the data supports it, what it takes to build, and what it costs to run.