Consultancies
Deliver a strategy deck and leave. Cannot write production code.
Writes the code. Stays until it runs.
Forward deployed engineeringLogistics & Supply Chain
We embed engineers inside your 3PL until it runs on AI.
The average 3PL operates on 3-8% net margins while processing thousands of documents daily by hand. Every manual touchpoint is margin leakage. We quantify it, automate it, and return it to your bottom line.
$22M
Net-new revenue captured via algorithmic spot quoting for a $120M 3PL
400bps
EBITDA recaptured from carrier overbilling through autonomous freight audit
45 min to 45 sec
Quote turnaround compression on spot freight bids
0 net-new hires
Headcount added while scaling operations 40%
One engagement. From the first day on the floor to production.
$120M third-party logistics · 6 weeks01
What the engineer found
- WK 01Twelve clerks keying bills of lading by hand.
- WK 01Six percent error rate. About $340K a year in rework.
- WK 01Headcount denied by the board three quarters running.
Audit complete. Bill-of-lading processing ranked first by AI leverage.
02
What the engineer built
Autonomous document agent
- Reads bills of lading across seventeen carrier formats
- Checks every rate against the master contract repository
- Processes invoices end to end
- Routes the 6% it flags to a human reviewer
Built in the client’s repository. Runs on their infrastructure.
03
What the company got
- WK 06In production. 94% of invoices straight-through. Error rate 6% to 0.3%.
- DAY 90Nine clerks redeployed to carrier negotiations. Second system scoped.
- YR 01$30M in new revenue absorbed. Zero back-office hires.
94%
straight-through
0.3%
error rate
9
clerks redeployed
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.
- 01
Bill-of-lading processing
12 clerks · 6% error rate
96 - 02
Carrier invoice audit
Rate checks by hand
82 - 03
Spot quote turnaround
45 min per quote
74 - 04
Carrier onboarding
3 to 5 days
48
90-day build plan signed. First system: bill-of-lading agent.
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 02100%
- WK 04100%
- WK 066%
Starts at 100% review. Falls as the system earns trust.
94%
invoices straight-through
0.3%
error rate, from 6%
Seventeen carrier formats. Every rate checked against contract.
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.
- M2Carrier invoice auditshipped
- M3Spot quoting engineshipped
- M4Carrier onboardingin review
- M5Track-and-trace updatesscoped
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.
Three logistics engagements, written the way an engineer would report them. Names withheld under NDA. Every figure confirmed.
Before
A $120M third-party logistics firm operating 1,800 loads per week across domestic truckload, LTL, and intermodal. 28 dispatchers, 12 track-and-trace coordinators, growing 15-20% annually through new shipper acquisition.
Every new shipper contract required hiring 2-3 additional operations staff. The CEO was winning business but watching margins compress from 6.2% to 4.8% over 18 months as payroll grew faster than revenue. The dispatch floor was running at capacity -- experienced dispatchers were burning out, and new hires took 4-6 months to become productive.
Built
$30M in net-new annualized revenue absorbed without hiring a single back-office data entry clerk
Deployed an AI-augmented operations layer across dispatch and track-and-trace. Automated carrier matching, check-call processing, and routine customer updates. Human dispatchers shifted from data entry and phone tag to exception handling and carrier relationship management. The entire Tier 1 processing layer was absorbed by the algorithmic BPO team.
Result
Scaled operations by 40% (from $120M to $168M in managed freight) with zero net-new operational hires. Per-load processing time dropped 55%. Margin stabilized at 6.0% despite the volume growth. Freed 15 senior staff to focus on strategic carrier negotiations, which improved contracted rates by 3.2%.
Before
A $140M 3PL processing 10,000+ carrier invoices per month across truckload, LTL, and intermodal modes. 14-person AP team struggling with volume growth.
The CFO identified that carrier overbilling was a known problem but could not quantify the leakage. AP clerks, overwhelmed by invoice volume, were auto-approving accessorial charges (detention, layovers, lumper fees) because cross-referencing the original BOL and carrier contract took too long. An internal audit of 500 random invoices revealed a 4.1% overbilling rate -- implying over $4M in annual margin leakage.
Built
400 basis points of EBITDA recaptured from carrier overbilling
Ingested the firm's entire historical contract repository. Deployed an algorithmic pre-audit engine that matches every invoice line item against contracted rates, BOL terms, and historical billing patterns with 99.9% accuracy. Only the 4% of heavily disputed invoices are routed to a specialized human dispute-resolution team.
Result
$4.2M in annual margin leakage recaptured. AP headcount costs frozen despite a 40% increase in total freight volume. The CFO described the recovered margin as 'pure EBITDA -- it dropped straight to the bottom line without a dollar of incremental revenue required.'
Before
A $95M freight brokerage with a 6-person pricing team handling 200+ spot quote requests daily. Spot freight represented 28% of total revenue but was growing as a strategic priority.
The CEO and CCO were watching spot bid win rates decline from 18% to 12% over two years. The root cause was speed: shippers award spot freight to the first viable quote, and the pricing team's 45-minute manual cycle was losing to competitors who responded in under 10 minutes. Hiring more pricing analysts was not economically viable -- each analyst cost $85K fully loaded and could only handle 35-40 quotes per day.
Built
312% increase in spot bid win rate; $22M net-new annualized revenue
Deployed an AI engine that bypasses the manual process entirely. The system instantly ingests unstructured spot emails, extracts shipment parameters, cross-references historical carrier rate data and real-time market API feeds, and drafts a margin-optimized quote. The human pricing manager reviews and approves with one click.
Result
Quote time compressed from 45 minutes to 45 seconds. Bid win rates increased from 12% to 38%, driving $22M in net-new annualized top-line revenue without adding a single pricing analyst. The CCO noted that they were 'winning freight we didn't even know we were losing.'
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 logistics 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 logistics report8
Departments mapped
24
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 7 are where logistics 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 logistics
Autonomous Agents
High document volume, thin margins, and time-sensitive operations make logistics one of the highest-ROI environments for autonomous agents. Freight audit, carrier onboarding, and claims processing are immediate deployment targets.
Read the brief
Used in logistics
Document Intelligence
Bills of lading, customs declarations, commercial invoices, and proof-of-delivery documents flow in massive volumes where extraction speed directly impacts shipment visibility and customs clearance.
Read the brief
Used in logistics
Decision Engines
Carrier selection, route optimization, and demand forecasting are high-frequency decisions where small improvements in consistency compound into significant cost savings.
Read the brief
Used in logistics
Predictive Analytics
Carrier rate forecasting, transit time prediction, and demand-driven inventory positioning enable logistics operators to reduce margin erosion from spot-market volatility and improve on-time delivery performance.
Read the brief
Used in logistics
Data Integration & ETL
Multi-carrier data reconciliation, EDI parsing and normalization, and shipment-to-invoice matching across dozens of carrier formats drive immediate ROI.
Read the brief
Used in logistics
Computer Vision
Cargo verification, damage detection, label reading, and warehouse inventory audits use vision to automate dock-to-stock verification and reduce freight claim disputes.
Read the brief
Used in logistics
Process Mining
Multi-party, multi-system freight and fulfillment processes create complex event trails where bottlenecks and conformance gaps directly impact service levels and cost.
Read the brief
02
Team Copilots
Not another chatbot. Team Copilots are role-specific AI systems that live inside your workflows — drafting, researching, analysing, and recommending in real time. They learn your processes, respect your compliance boundaries, and get sharper with every interaction. Production-grade in 4–6 weeks.
Read the brief
05
Workflow Automation
Replace brittle rule chains and manual handoffs with AI-orchestrated workflows that route, decide, and escalate based on context -- not just pre-programmed if/then trees.
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
09
Knowledge Management
Stop losing critical expertise when employees leave. Neume indexes every document, email, ticket, and system of record into a unified knowledge layer -- so your entire organization can find answers in seconds, not hours.
Read the brief
10
Compliance & Audit AI
Regulated enterprises spend 15,000-40,000 person-hours per year on manual compliance activities -- evidence gathering, control testing, policy mapping, and audit preparation -- that are fundamentally pattern-matching and document-processing tasks. Neume's Compliance & Audit AI compresses these cycles from quarterly marathons into always-on, machine-verified assurance with human oversight at every decision boundary.
Read the brief
13
AI Training & Enablement
Generic courses teach theory. Neume embeds AI literacy directly into your workflows, creating internal AI champions who drive adoption long after the engagement ends. Your people stop fearing AI and start leveraging it -- within weeks, not quarters.
Read the brief
14
Custom LLM Fine-Tuning
Neume Labs builds fine-tuned LLMs that speak your industry's language -- from legal clause interpretation to medical coding taxonomy -- delivering measurably higher accuracy, lower inference cost, and full data sovereignty throughout the training lifecycle.
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.