Consultancies
Deliver a strategy deck and leave. Cannot write production code.
Writes the code. Stays until it runs.
Forward deployed engineeringInsurance
We embed engineers inside your carrier until it runs on AI.
Compress underwriting cycle times from days to minutes, slash loss adjustment expenses by 28%, and unlock underwriting capacity without net-new hires -- turning your back office into a competitive weapon.
300%
Increase in underwriting capacity with zero new hires
28%
Reduction in Loss Adjustment Expenses (LAE)
9 days to 9 min
Quote turnaround on commercial specialty lines
$5M+
Annual claims leakage eliminated
One engagement. From the first day on the floor to production.
Underwriting01
What the engineer found
Mid-market commercial specialty MGA writing $350M in GWP across GL, professional liability, and excess casualty lines, with 45 underwriters and 20 support staff
Brokers were defecting to faster-quoting competitors. The underwriting team spent 60% of their day manually keying data from unstructured broker submissions -- 100-page loss run PDFs, messy ACORD forms, and financial statements -- before they could even evaluate the risk. Quote turnaround averaged 9 business days on specialty lines. Good brokers stopped sending them business.
02
What the engineer built
Underwriting
Deployed an AI ingestion layer that intercepts broker emails, reads unstructured loss runs, maps ACORD data, runs initial rules-engine appetite checks, and pre-populates the underwriting workbench. HitL insurance specialists QA the extracted data before the underwriter sees it.
Built in the client’s repository. Runs on their infrastructure.
03
What the company got
Underwriting team's capacity to write new premiums increased by 300% with zero new underwriting hires. Bind ratios increased 40% because they were suddenly the first to reply to the broker. Quote turnaround compressed from 9 days to under 15 minutes on standard commercial lines.
300% increase in premium writing capacity; 9-day to 9-minute quote turnaround
12 weeks from kickoff to production on first two lines of business
Every number is from a confirmed engagement. Names withheld under NDA.
01The model
Not a consultant. Not a dev shop. An engineer on your floor.
A forward deployed engineer is a senior engineer who works inside your company rather than for it. They sit with the claims processor, the dispatcher, the underwriter. They find the workflow where AI pays off first, build the system on top of the software you already run, ship it to production, and train your people to run and extend it. Then they move to the next workflow.
In 2019 the hard part of enterprise AI was the model. Today the models are commodity infrastructure. The hard part is the twenty-year-old ERP, the undocumented process, the compliance rule nobody wrote down, and the operations manager who has watched three digital transformations fail. That is an engineering problem, and it is solved on site.
Dev shops
Build what you spec. Never see your operations.
Sits in your operations. Finds what to build.
Hiring
Six months to hire one AI engineer who starts from zero.
Arrives in a week with patterns from 47 deployments.
SaaS tools
Horizontal products configured for nobody in particular.
Systems built for your data, your edge cases, your compliance.
We don’t consult. We engineer intelligence.
02How it works
Contact. Audit. First deliveries. Monthly.
From the first call to a system in production in six weeks. Then a retainer that scales up or down every month.
01 / Contact30 minutes
A call with an engineer, not a salesperson.
We find out whether there is a fit and which workflow to start with. No deck, no discovery workshop, no proposal cycle.
- Who
- Your COO, your head of operations, and a Neume engineer.
- We ask
- Where the work piles up. Which systems it lives in. Who reviews it today.
- You get
- A straight answer on fit, and the workflow we would start with.
Nothing to prepare. Bring the person who owns the process.
02 / AuditWeek 1
The engineer embeds with the teams doing the work.
Every workflow mapped and ranked by AI leverage. The output is a 90-day build plan with the first systems specified and ROI attached to each.
- 01Build first
Submission Intake & Triage
45-90 min per submission intake; 5-9 day average quote turnaround; 30% of underwriter time spent on uninsurable risks
- 02Queued
Loss Run Analysis & Risk Scoring
2-4 hours manual loss run analysis per risk; inconsistent scoring across 30+ underwriters; 8% adverse selection rate on the book
- 03Queued
Quote & Proposal Generation
30-60 min per quote; 5-7% error rate on coverage terms; no real-time pipeline visibility
- 04Queued
Renewal Underwriting & Book Management
Average renewal processed at T-10 days; 100% of renewals require full underwriter review; 82% retention rate
90-day build plan signed. First system: Underwriting.
03 / First deliveriesWeeks 2 to 6
The first system ships to production with human review switched on.
Real data, real compliance constraints, real users. This is the proof, before any retainer.
Human review
- WK 02All
- WK 04All
- WK 06Flagged
Starts at 100% review. Falls as the system earns trust.
300% increase in premium writing capacity; 9-day to 9-minute quote turnaround
12 weeks from kickoff to production on first two lines of business
04 / MonthlyMonth 2 onward
The engineer stays. Department by department, workflow by workflow.
Review thresholds fall as the systems earn trust. Scale the engagement up or down each month. Our revenue depends on it working, so we stay until it does.
- M2First Notice of Loss (FNOL) Processingshipped
- M3Policy Issuanceshipped
- M4Loss Triangle Construction & Reserve Analysisin review
- M5Broker Production Monitoring & Analyticsscoped
3.2x
productivity, day 90
8x
at twelve months
0
data incidents
Embedded. Not engaged.
03Field reports
What production looks like when the engineer is in the building.
Two insurance engagements, written the way an engineer would report them. Names withheld under NDA. Every figure confirmed.
Before
Mid-market commercial specialty MGA writing $350M in GWP across GL, professional liability, and excess casualty lines, with 45 underwriters and 20 support staff
Brokers were defecting to faster-quoting competitors. The underwriting team spent 60% of their day manually keying data from unstructured broker submissions -- 100-page loss run PDFs, messy ACORD forms, and financial statements -- before they could even evaluate the risk. Quote turnaround averaged 9 business days on specialty lines. Good brokers stopped sending them business.
Built
300% increase in premium writing capacity; 9-day to 9-minute quote turnaround
Deployed an AI ingestion layer that intercepts broker emails, reads unstructured loss runs, maps ACORD data, runs initial rules-engine appetite checks, and pre-populates the underwriting workbench. HitL insurance specialists QA the extracted data before the underwriter sees it.
Result
Underwriting team's capacity to write new premiums increased by 300% with zero new underwriting hires. Bind ratios increased 40% because they were suddenly the first to reply to the broker. Quote turnaround compressed from 9 days to under 15 minutes on standard commercial lines.
Before
Regional P&C carrier writing $600M in premium across personal and commercial auto, property, and GL lines, with 120 adjusters and $380M in annual incurred losses
Adjusters were buried in third-party repair estimates, police reports, and medical bills, manually cross-referencing line items against policy limits. Slower claims processing was driving higher indemnity payouts and increased attorney involvement. Claims leakage from manual oversight errors exceeded $5M annually.
Built
28% reduction in LAE; $5M annual claims leakage eliminated
Deployed an AI BPO engine that takes over the unstructured document pipeline. The AI extracts line-item data from third-party estimates, cross-references it with the policyholder's deductibles and limits, flags anomalies (upcharging on parts, duplicate line items), and routes a clean, structured package to the human adjuster for final adjudication.
Result
FNOL-to-settlement time cut in half. Vendor spend on outsourced adjusting firms slashed. $5M in annual claims leakage from manual oversight errors eliminated. Loss adjustment expenses reduced by 28%.
47+
Production systems deployed
6 wks
Average kickoff to production
3.2x
Productivity gain at 90 days
8x
At twelve months
0
Data incidents
The full report
Every insurance department mapped before the engineer walks in.
The analysis our engineers work from: pain points, opportunities with ROI, workflows before and after, the regulatory constraints, and a phased roadmap.
Read the insurance report8
Departments mapped
26
AI opportunities
26
Workflows, before and after
04AI native, defined
Five levels. The engineer moves you up one workflow at a time.
AI native means AI is inside the workflow, not beside it. Every recurring process has been examined for what an agent does and what a human must do. Every knowledge worker has a copilot wired to the company’s own data. And the people who work there can extend the systems after the engineer leaves. The audit tells you where you are today.
Audit · Week 1
Where does the company sit today?
- 0level
Curious
ChatGPT on personal accounts. No policy, no systems, no data connected.
- 1level
Experimenting
A pilot or two in a sandbox with clean data. Nothing in production.
- 2level
Operational
One or two systems live in one department, with humans reviewing every output.
- 3level
Embedded
AI inside the core workflows of most departments. Humans handle the exceptions.
- 4level
Native
New work is designed AI-first by default. Your own team extends the systems.
Most companies arrive at level 0 or 1. The first system takes them to level 2 in six weeks.
05What we build
Fourteen systems an engineer ships. The first 13 are where insurance starts.
The audit decides which one comes first. Each brief covers what the system is, how it is built, and what the first weeks look like.
Used in insurance
Autonomous Agents
Claims intake, underwriting data collection, and policy servicing are high-volume, judgment-intensive processes where agents deliver immediate cycle time compression and cost reduction.
Read the brief
Used in insurance
Team Copilots
Underwriting copilots, claims adjuster copilots, and policy servicing copilots that compress cycle times and improve decision accuracy in both commercial and personal lines.
Read the brief
Used in insurance
Document Intelligence
Claims documents, ACORD forms, loss runs, policy endorsements, and underwriting submissions are the lifeblood of insurance operations -- all unstructured, high-volume, and perfectly suited to AI extraction.
Read the brief
Used in insurance
Decision Engines
Underwriting risk selection, pricing, and claims triage are high-volume decision workflows where Decision Engines deliver immediate ROI through faster turnaround and improved consistency.
Read the brief
Used in insurance
Workflow Automation
Claims processing, underwriting triage, and policy issuance workflows involve high-volume multi-step orchestration across internal departments and external parties.
Read the brief
Used in insurance
Predictive Analytics
Claims severity prediction, loss ratio forecasting, and policyholder churn propensity scoring are core actuarial challenges that benefit from ensemble ML models trained on carrier-specific claims and underwriting data.
Read the brief
Used in insurance
Data Integration & ETL
Policy administration system consolidation, loss data aggregation, and regulatory reporting across multiple legacy platforms are core data integration challenges in insurance.
Read the brief
Used in insurance
Knowledge Management
Underwriting guidelines, claims precedents, and policy interpretation history are high-value knowledge assets that directly impact loss ratios when properly surfaced and applied.
Read the brief
Used in insurance
Compliance & Audit AI
State-by-state regulatory variation, NAIC model law adoption tracking, market conduct examinations, and emerging AI governance requirements create multi-jurisdictional compliance complexity that is ideally suited for AI-driven regulatory monitoring.
Read the brief
Used in insurance
Computer Vision
Photo-based damage assessment, fraud detection through image forensics, and automated underwriting property surveys are transforming claims and underwriting efficiency.
Read the brief
Used in insurance
Process Mining
Claims adjudication, policy servicing, and underwriting workflows exhibit high variant counts and rework rates that process mining can quantify and trace to root causes.
Read the brief
Used in insurance
AI Training & Enablement
Claims, underwriting, and actuarial teams sit at the intersection of data-heavy workflows and nuanced human judgment -- exactly where AI enablement drives the highest ROI by teaching teams to calibrate trust in AI outputs.
Read the brief
Used in insurance
Custom LLM Fine-Tuning
Models fine-tuned on carrier-specific policy forms and adjudication history enable automated coverage determination, claims triage, and subrogation identification with carrier-specific precision.
Read the brief
07
Conversational AI
Deploy AI agents that understand context, handle complexity, and operate across chat, voice, and messaging channels — built around your business logic, not a generic template.
Read the brief
Not sure which one?
If the work runs on documents, decisions, and handoffs, one of these applies. The audit tells you which to build first.
06Engagement shapes
Monthly. Scale up or down each month.
No minimum term. The shape is agreed on the call and can change as the roadmap does.
Two days a week
Fractional
One engineer, part time, one workflow at a time. The right shape for a 50 to 200 person company taking its first system into production.
Talk about this shapeOne full-time engineer
Embedded
One engineer inside one department, shipping a system every four to six weeks and training the team that runs it.
Talk about this shapeTwo to three engineers and a lead
Pod
Several departments at once, with a lead who owns the roadmap across them. For companies that want to move up the ladder fast.
Talk about this shape07Built for companies that cannot get this wrong
Your data. Your infrastructure. Your repository.
Four guarantees, in every contract, before any system goes live.
Human in the loop
Every system ships with a review layer your team controls. It starts at 100% human review and falls as the system earns trust. By month six most clients run 85 to 90% autonomous, with humans on the edge cases.
100% audited outputs
Data sovereignty
Your data stays on your infrastructure and never trains public models. SOC 2 Type II, with every access, inference, and review logged and attributable.
0 data incidents
You own everything
Everything the engineer builds lives in your repository and runs on your infrastructure. Your people are trained to run it and extend it. Nothing walks out the door when the engagement ends.
Yours code and repository
Contractual KPIs
Processing time, error rate, cost per transaction, hours freed. Agreed before we build, reported monthly. If the return is not there, we tell you first.
Agreed before deployment
The window
Every week you wait, the gap compounds.
Productivity is 3.2x at month three and closer to 8x at month twelve, because the systems learn from every transaction and the people learn alongside them. A competitor starting from zero a year from now faces the same six-week build. They are twelve months of institutional learning behind, and that gap does not close.
Book a call with an engineer
30 minutes. An engineer, not a salesperson.
Or forward this page to your CEO.