FORWARD-DEPLOYED AI ENGINEERING FOR SMALL & MID-SIZE COMPANIES · NASHVILLE · CHICAGO · NEW YORK
YourpeopleuseAI.Yourbusinessstilldoesn't.
Forward-deployed AI engineers, without the full-time hire. We build AI into the work your company already does, one workflow at a time, priced on the result.
How a FounderEdge engagement is built: your systems at the base, rules and an agent in the middle, human review on top, ROI metrics.
$5,000 fixed baseline sprint
4 weeks to a go / no-go
ROI metrics agreed before the pilot
6 workflows in production or pilot
01 ABOUT
Forward-deployed engineering, without the full-time hire.
Forward-deployed engineers sit with the people who do the work and build AI into it. It is how many of the largest AI and software companies get their products working inside customers. For a small or mid-size company without a dedicated engineering team, hiring that person full time is slow, expensive and hard to judge. FounderEdge makes forward-deployed engineering affordable for everyone else: insurers, importers, online sellers, manufacturers, distributors and professional-service firms.
Most of these companies already pay for AI tools. What they lack is an engineer who connects those tools to the systems, rules and sign-offs that real work runs on. You get that engineer for the length of a project, with no recruiting, no salary and no team to manage: we measure the workflow, build inside the tools you already own, and stay until the numbers move.
FORWARD-DEPLOYED
Engineers who sit with your operators and build inside your tools, not a slide deck handed to your IT team.
NO HIRE NEEDED
Engineering by the project: no recruiting, no salary, no team to manage. Every engagement starts with a baseline and ends with the same measurement.
PRICED ON RESULTS
A fixed first step, then fees tied to ROI metrics agreed up front.
Team across Nashville, Chicago and New York.
02 WORK
Six workflows, each with the measurement attached
Every figure below comes with its period, its source and what was left out. Where a number is still a target, we say so.
INSURANCE
TRADE + CUSTOMS
E-COMMERCE
HEALTHCARE OPERATIONS
RETAIL
SPECIALTY INSURANCE · IN PRODUCTION01 / 06
A multinational specialty insurer closes its quarter 30 days faster, with every manual adjustment checked before it reaches the books
30 days
faster quarter close · finance reporting cycle
1 week
from kickoff to the first workflow in production
4
workstreams · reporting and close · data governance · system integration · AI agents
Client
A multinational specialty insurance company
Financial reporting
manual journal adjustments collected, validated and loaded to the planning system each close; income statement rebuilt from actuals, plan and contract data with a snapshot after every refresh; segment reporting in the public-filing layout; premium projections refreshed and archived
Data governance
classification rules owned by the business in a governed rulebook; unclassified records routed through an approved exceptions process; sign and adjustment policy enforced at validation; test-to-production promotion logged with rollback
System integration
finance ERP, planning system, data warehouse and cloud data platform connected; data notebooks audited and promoted in waves; journal entries converted to subledger format automatically
AI agents
an adjustment-validation agent in the team chat that drafts results for a person to send; a documentation agent with a second agent that checks it against the source; a procedure-capture agent scored against an 11-point standard
Stack
cloud data platform and lakehouse, finance planning system, finance ERP reporting layer, enterprise data warehouse, BI semantic model, low-code agent builder
Engagement
embedded with the finance team, fixed monthly scope
The quarter close ran on spreadsheets that people downloaded, converted and re-pointed by hand, roughly a day of work before each refresh could even start. Rules lived in code that only engineers could change. Today the sources load themselves, business owners maintain their own classification rules, a run stops rather than report a wrong number, and every adjustment is checked before it lands.
What was hardChanging a live close process without stopping it: every change had to pass a test environment that can never write to production, with a logged rollback.
Close-cycle and first-workflow figures from the engagement, 2025–2026. Client name withheld.
IMPORT + EXPORT · IN PRODUCTION02 / 06
Import declarations that used to queue at peak now run through an agent first, with a person on every low-confidence field
318
customs runs in 7 days · one international consumer brand
76.4%
completed with no human touch · 1,842 runs platform-wide, same 7 days
24.8%
routed to a named reviewer · 3.6% exceptions · 10 items out of scope, reason stated
Client
An international consumer-goods brand, import and export operations
Documents
safety data sheets, UN numbers, certificates, loading plans, bilingual paperwork
Stack
Microsoft 365 (Outlook, OneDrive, Teams, SharePoint), ERP, OCR, human-review queue
Shipments do not arrive evenly. Every declaration needs certificate fields, dates and hazard codes checked across photos, scans, PDFs and spreadsheets, and the judgment behind those checks lived in a few senior people. The agent takes the standard steps first and leaves every low-confidence field to a named reviewer, with a full trace.
What was hardNot the OCR. Reading several documents together, making expert rules explicit, and keeping an audit trail a compliance team will sign.
Figures from a 7-day operations dashboard, 2026. Built with technology partner NoDesk AI.
E-COMMERCE COMPLIANCE · PILOT IN DESIGN03 / 06
The same factory paperwork, checked once, before a marketplace blocks the listing
target first-pass accuracy · a target, not yet a result
1–4 wks
baseline sprint, run in the US
For
sellers and importers in small appliances, kitchenware, pet consumables, personal care and baby products
Checks
labels, test reports, certificates and ingredient lists against regulator and marketplace rules
Output
a pass / fix / block verdict with the evidence attached
What was hardRules change by marketplace and by month; the system has to show which rule and which line of which document produced each verdict.
Pilot in design with technology partner NoDesk AI. No accuracy result is claimed yet.
HEALTHCARE OPERATIONS · DEPLOYED IN CHINA04 / 06
Radiology reports drafted in two minutes instead of fifteen, with the physician signing every one
15 → 2 min
per image, read to drafted report · single-hospital pilot
90%+
draft accuracy as scored by reviewing physicians
200,000
images a year at the pilot hospital
Client
A hospital imaging network in China
Workflow
image quality check → first read → report draft → physician review and sign-off → corrections fed back
Human role
the physician decides; the agent prepares
What was hardClinical work cannot be a black box: every prompt, score and correction had to be reviewable.
Deployed outside the United States; figures reported by the delivery team, baseline method not published. Not a medical-device claim. Built with technology partner NoDesk AI.
CONSUMER BRAND · IN PRODUCTION05 / 06
Promotion photos checked in under two seconds, competitor products rejected automatically
≈1.7 s
per image, end to end
98%+
recognition accuracy, as reported
100%
competitor products rejected in the reported sample
Client
A consumer-goods brand running purchase-proof promotions
Workflow
photo in → product and label match → verified or rejected with the reason → exceptions to a reviewer
What was hardPhone photos are tilted, cropped and badly lit; the rules for what counts as proof had to be written down before any model was tuned.
Figures reported by the delivery team; sample size and period not published. Built with technology partner NoDesk AI.
RETAIL · IN PRODUCTION06 / 06
A home-goods brand publishing 100–200 on-brand pieces a month with the same team
3.2×
content output per person, as reported
+28%
engagement on published pieces
100–200
pieces a month, reviewed before publishing
Client
A home-goods brand selling through online marketplaces
Workflow
product facts and brand rules in → drafts with images → brand review → scheduled publishing
What was hardKeeping the brand's voice and the product facts straight at volume; every piece still passes a human editor.
Figures reported by the delivery team; comparison period not published. Built with technology partner NoDesk AI.
03 HOW WE WORK
Three steps. Each one ends with a number.
An engagement runs in three steps: a four-week baseline sprint, a ten-week pilot of one workflow in production, then monthly operation with ownership transferred to your team.
01
Baseline sprint weeks 1–4
We sit with the people who run the workflow, time it, write the data contract and the acceptance test, and project the payback. You get a go / no-go in writing.
02
Pilot weeks 5–14
One workflow goes live inside the tools you already use. It is measured every week against the baseline, on a dashboard you can open.
03
Run monthly
We operate it, watch it and queue the next workflow. Runbooks and ownership move to your team.
EVERY LOW-CONFIDENCE DECISION GOES TO A NAMED PERSON. EVERY STEP LEAVES A TRACE.
04 HOW WE CHARGE
Priced on the result, not the hours
You start with a 30-minute fit check at no cost, then a fixed $5,000 baseline sprint; pilot and run fees are tied to ROI metrics agreed in writing.
01
Fit check
30 minutes
Free
Is there a workflow worth measuring?
02
Baseline sprint
Weeks 1–4
$5,000 fixed, credited against the pilot
Travel and logistics billed at cost.If the numbers won't pay back, we say so and stop here.
03
Pilot
Weeks 5–14
Base fee + a share held at risk, paid when the agreed ROI metrics are hit
Upside capped. ROI metrics and one signatory, set in writing.
04
Run
Monthly
Operating fee + optional capped gainshare on verified savings
Only where your finance team signs the measurement.
Pilot and run numbers are set in the baseline sprint, from your own baseline.
05 PARTNERS
Vetted international technology, contracted and delivered in the US
Some of the best operational AI is built outside the United States. We vet it, hold the US contract, own the data contract and the compliance rules layer, and run acceptance here.
production data stays in your environment; partners build on masked or synthetic data
ACCEPTANCE
nothing ships until it passes the test written in the baseline sprint
06 QUESTIONS
Questions buyers ask first
What is a forward-deployed engineer, and why not hire one?
A forward-deployed engineer sits with the team that does the work and builds AI into it, instead of handing over a tool. Hiring one full time is costly and hard to evaluate for a company without an engineering team, so we provide the role by the project. FounderEdge measures one workflow, builds the system inside the tools the company already uses, keeps a person on every low-confidence decision, and is paid against the result.
How much does it cost to start?
A 30-minute fit check is free. The first paid step is a four-week baseline sprint at a fixed $5,000, credited against the pilot if you continue. Travel and logistics are billed at cost.
What is outcome-based pricing for an AI project?
Part of the fee is held back until the ROI metrics agreed in writing are hit, measured against a baseline taken before the work began. It only works when both sides sign the measurement, which is why the baseline sprint comes first.
How long until we see a result?
Four weeks to a measured baseline and a go / no-go. Ten more weeks to one workflow running in production and measured weekly. We do not promise a payback date before the baseline exists.
Does it work with the tools we already have?
Yes. The systems above run inside Microsoft 365, ERP systems, shared drives and email. We do not ask you to move to a new platform.
Which industries do you work with?
Specialty insurance, import and export, e-commerce and consumer brands, healthcare operations and retail so far. The common thread is document-heavy work where experienced people make judgment calls.
Where is the engineering done, and who sees our data?
FounderEdge is a US company and holds the contract. Some systems are built with vetted international technology partners. Production data stays in your environment; partners build on masked or synthetic data, and acceptance runs in the US.
What happens if it doesn't work?
The baseline sprint ends with a written go / no-go. If the projected payback is not there, we say so and stop; you keep the baseline and the data contract.
FIT CHECK
Book a 30-minute fit check.
No deck, no pitch. Bring the workflow that costs you the most hours.