Forward deployed engineeringManufacturing & Industrials

We embed engineers inside your plants until it runs on AI.

Eliminate production halts, recapture margin leakage, and decouple regulatory overhead from production scale -- without ripping out your ERP.

Book a call with an engineer How it works

250bps

Gross Margin Recaptured

Line-Down Events Prevented (6 mo.)

40%

Procurement Volume Absorbed, Zero New Hires

99.7%

First-Pass Compliance Audit Rate

One engagement. From the first day on the floor to production.

Supply Chain / Procurement

What the engineer found

A $180M discrete manufacturer (aerospace and defense tier-2 supplier) with 2,400 active SKUs sourced from 220 global suppliers, running SAP ECC as its ERP platform.

Suppliers routinely sent pricing updates, lead-time changes, and part obsolescence notices as unstructured PDF attachments. The 8-person procurement team could not keep pace. A supplier changed a critical fastener part number on a PDF acknowledgment; the team missed it, and the assembly line went down for 11 hours -- costing $340K in lost production and air-freight expediting.

What the engineer built

Supply Chain / Procurement

Neume deployed an algorithmic BPO layer that ingests all supplier communications, cross-references them against the SAP master BOM, and flags margin-compressing variances and part-change risks in real time. HitL procurement analysts review only flagged exceptions and draft PO amendments.

Built in the client’s repository. Runs on their infrastructure.

What the company got

Prevented 14 potential line-down events in the first 6 months. Recaptured 250bps of gross margin from previously undetected supplier price creep. Procurement headcount frozen while raw material purchasing volume grew by 40%.

$1.85M annualized savings (margin recapture + avoided downtime + headcount avoidance)

8 weeks from kickoff to production deployment

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.

Consultancies

Deliver a strategy deck and leave. Cannot write production code.

Writes the code. Stays until it runs.

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.

Call with an engineer30 min

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.

Workflows ranked by AI leverageWeek 1 · 8 departments

01
Supplier Communication Ingestion & BOM Reconciliation
3-6 week avg detection latency; 12% of obsolescence notices missed; 15 hrs/week buyer time on data entry
Build first
02
Purchase Order Creation & Approval
12% maverick spend; 2.5-day avg approval cycle; 8% of POs issued at non-contract pricing
Queued
03
Goods Receipt & Receiving Inspection
22 min avg receiving processing per line item; 1.5-day inspection queue; manual cert filing
Queued

90-day build plan signed. First system: Supply Chain / Procurement.

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.

Supplier Communication Ingestion & BOM Reconciliation Production

Human review

WK 02All
WK 04All
WK 06Flagged

Starts at 100% review. Falls as the system earns trust.

$1.85M annualized savings (margin recapture + avoided downtime + headcount avoidance)

8 weeks from kickoff to production deployment

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.

Retainer roadmapOne engineer · embedded

M2Incoming Material Certification Reviewshipped
M3Work Order Release & Validationshipped
M4Preventive Maintenance Scheduling & Executionin review
M5Supplier Invoice Processing & Paymentscoped

3.2x

productivity, day 90

at twelve months

data incidents

Embedded. Not engaged.

03Field reports

What production looks like when the engineer is in the building.

Two manufacturing engagements, written the way an engineer would report them. Names withheld under NDA. Every figure confirmed.

Report 01Supply Chain / Procurement8 weeks from kickoff to production deployment

Before

A $180M discrete manufacturer (aerospace and defense tier-2 supplier) with 2,400 active SKUs sourced from 220 global suppliers, running SAP ECC as its ERP platform.

Built

$1.85M annualized savings (margin recapture + avoided downtime + headcount avoidance)

Result

Report 02Quality Assurance / Compliance10 weeks from kickoff to full production deployment

Before

A $220M aerospace tier-2 manufacturer (machined structural components) with AS9100D certification, 180 active part numbers under NADCAP special process requirements, running Epicor ERP.

20 senior quality engineers spent an average of 22 hours per week reviewing material test reports, certificates of conformance, and metallurgical test data against PO requirements and AMS/ASTM specifications. This paperwork burden left no capacity for continuous improvement initiatives. During a customer audit, a missed out-of-spec hardness value on an MTR resulted in a containment action costing $280K.

Built

$1.1M annualized value (quality engineer reallocation + scrap reduction + avoided containment costs)

Neume deployed an AI engine that reads complex 50-page material certificates, extracts chemical compositions and mechanical properties, and validates them against PO requirements and applicable specifications. HitL QA specialists sign off on the compliance packet. A structured traceability ledger is built autonomously and linked to lot records in the ERP.

Result

Zero compliance audit findings in the subsequent AS9100 surveillance audit. Freed 20 senior quality engineers from paperwork, allowing them to drive a continuous improvement program that reduced internal scrap by 22% in the first year.

47+

Production systems deployed

6 wks

Average kickoff to production

3.2x

Productivity gain at 90 days

At twelve months

Data incidents

The full report

Every manufacturing 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 manufacturing report

Departments mapped

AI opportunities

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?

Five levels · one ladder

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

Technical documentation models trained on proprietary parts catalogs, engineering specifications, and service procedures generate accurate maintenance content with correct part numbers and torque specs.

Read the brief

Document Intelligence

Combine state-of-the-art OCR with large language models to extract, classify, and validate data from invoices, contracts, claims, compliance filings, and any unstructured document -- at enterprise scale with human-grade accuracy.

Read the brief

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.

Book a call with an engineer

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 shape

One 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 shape

Two 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 shape

07Built 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.

rohan@neumelabs.ai