Consultancies
Deliver a strategy deck and leave. Cannot write production code.
Writes the code. Stays until it runs.
Forward deployed engineeringCommercial Real Estate
We embed engineers inside your portfolio until it runs on AI.
Mid-market CRE firms leak 3-7% of Net Operating Income through manual lease administration, under-billed CAM reconciliations, and fragmented AP workflows. Neume Labs deploys Human-in-the-Loop AI to recapture that margin without displacing your property management stack.
$1.8M+
Average Annual Tenant Recovery Leakage per 50-Property Portfolio
14-18 hrs
Average Time to Manually Abstract a Single Commercial Lease
23%
CAM Line Items Under-Billed Due to Manual Calculation Errors
4.2%
Average AP Error Rate Across Multi-Entity Property Portfolios
One engagement. From the first day on the floor to production.
Lease Administration01
What the engineer found
Mid-market CRE operator managing a 68-property mixed-use portfolio (retail, office, industrial) across three states with 420+ active tenant leases and $180M in gross asset value.
The firm was chronically under-billing CAM reconciliations due to the complexity of applying 400+ unique tenant formulas manually. Lease administrators defaulted to conservative calculations to avoid tenant disputes, systematically leaving recoverable revenue on the table. A forensic audit revealed $1.8M in annual under-billing. Simultaneously, lease abstraction backlogs during two recent acquisitions (35 leases each) delayed accurate billing by 8-10 weeks post-closing.
02
What the engineer built
Lease Administration
Neume Labs deployed its AI lease abstraction and CAM reconciliation engine. The AI ingested all 420+ lease documents, extracted financial covenants, CAM caps, exclusion language, and gross-up provisions, and structured the data directly into Yardi. HitL real estate accountants reviewed flagged discrepancies. The CAM reconciliation engine then calculated every tenant's accurate pro-rata share with full audit trail documentation.
Built in the client’s repository. Runs on their infrastructure.
03
What the company got
100% accurate, audit-defensible CAM billing across the portfolio. $1.8M in previously leaked tenant recoveries recaptured in the first year. Lease abstraction backlogs eliminated -- subsequent 42-lease acquisition fully abstracted within 72 hours of closing. Tenant audit challenges dropped by 85%.
$1.8M in annual recovered revenue with 100% CAM billing defensibility.
Phase 1 (lease abstraction): 10 weeks. Phase 2 (CAM reconciliation): 6 weeks. Full portfolio deployment: 16 weeks.
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
Lease Abstraction & Data Entry
14-18 hours per lease. 6-8 week backlog during acquisitions. 4.1% error rate on financial term extraction. Amendments abstracted 3-4 weeks post-execution.
- 02Queued
CAM / Tax / Insurance Reconciliation
6-10 week reconciliation cycle. 23% of CAM line items under-billed. $1.8M in annual recoverable revenue leaked across a 75-property portfolio. 200+ hours spent on tenant disputes.
- 03Queued
Critical Date & Lease Event Management
3-5 critical dates missed annually per 75-property portfolio, resulting in $400K-$900K in avoidable financial impact. No downstream dependency mapping.
- 04Queued
Estoppel Certificate & SNDA Preparation
4-8 hours per tenant estoppel. 3-4 week preparation cycle for a 30-tenant property. 6% error rate requiring post-closing corrections.
90-day build plan signed. First system: Lease Administration.
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.
$1.8M in annual recovered revenue with 100% CAM billing defensibility.
Phase 1 (lease abstraction): 10 weeks. Phase 2 (CAM reconciliation): 6 weeks. Full portfolio deployment: 16 weeks.
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.
- M2Multi-Entity Invoice Intake & GL Codingshipped
- M3Work Order Intake & Triageshipped
- M4NOI Optimization & Variance Analysisin review
- M5Acquisition Underwriting & Deal Screeningscoped
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 real estate engagements, written the way an engineer would report them. Names withheld under NDA. Every figure confirmed.
Before
Mid-market CRE operator managing a 68-property mixed-use portfolio (retail, office, industrial) across three states with 420+ active tenant leases and $180M in gross asset value.
The firm was chronically under-billing CAM reconciliations due to the complexity of applying 400+ unique tenant formulas manually. Lease administrators defaulted to conservative calculations to avoid tenant disputes, systematically leaving recoverable revenue on the table. A forensic audit revealed $1.8M in annual under-billing. Simultaneously, lease abstraction backlogs during two recent acquisitions (35 leases each) delayed accurate billing by 8-10 weeks post-closing.
Built
$1.8M in annual recovered revenue with 100% CAM billing defensibility.
Neume Labs deployed its AI lease abstraction and CAM reconciliation engine. The AI ingested all 420+ lease documents, extracted financial covenants, CAM caps, exclusion language, and gross-up provisions, and structured the data directly into Yardi. HitL real estate accountants reviewed flagged discrepancies. The CAM reconciliation engine then calculated every tenant's accurate pro-rata share with full audit trail documentation.
Result
100% accurate, audit-defensible CAM billing across the portfolio. $1.8M in previously leaked tenant recoveries recaptured in the first year. Lease abstraction backlogs eliminated -- subsequent 42-lease acquisition fully abstracted within 72 hours of closing. Tenant audit challenges dropped by 85%.
Before
Regional CRE operator managing 52 commercial properties (office, retail, flex industrial) across separate LLCs, processing approximately 12,000 vendor invoices per month with a 6-person AP team.
AP was the operational bottleneck preventing portfolio growth. Every new property acquisition added 200+ monthly invoices, requiring additional headcount. GL miscoding across 52 entities averaged 5.1%, distorting property-level NOI reporting. The firm was missing 58% of early-pay discount opportunities and incurring $145K in annual late fees. Month-end close averaged 16 business days.
Built
AP headcount frozen during 40% portfolio growth; $263K annual savings from discount capture and late fee elimination.
Neume Labs deployed its multi-entity AP automation engine. AI routes, reads, and codes invoices to the correct entity and GL accounts using vendor pattern recognition and historical coding data. The system matches invoices against vendor contracts, flags anomalous charges, and optimizes payment timing across all entities. HitL AP specialists handle exceptions and final approval routing.
Result
AP headcount frozen at 6 while the portfolio grew to 72 properties (40% growth). GL miscoding reduced to 0.4%. 100% early-pay discount capture. Late fees reduced to $11K annually. Month-end close compressed to 6 business days. The AP department transformed from a cost center into a measurable contributor to NOI.
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 real estate 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 real estate report8
Departments mapped
25
AI opportunities
25
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 5 are where real estate 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 real estate
Team Copilots
Investment analyst copilots, asset management copilots, and leasing copilots that accelerate deal screening, improve portfolio oversight, and streamline tenant communications.
Read the brief
Used in real estate
Workflow Automation
Lease administration, tenant onboarding, and capital expenditure approval workflows involve document-heavy processes across multiple stakeholders.
Read the brief
Used in real estate
Data Integration & ETL
Property management system consolidation, lease data aggregation, and tenant entity resolution across disparate platforms are natural fits for semantic data integration.
Read the brief
Used in real estate
Computer Vision
Property condition assessment, lease-space measurement, and construction progress monitoring leverage drone and mobile imagery analysis to replace manual, subjective inspection workflows.
Read the brief
Used in real estate
Custom LLM Fine-Tuning
Lease abstraction models trained on portfolio-specific lease libraries extract rent escalation formulas, co-tenancy provisions, and CAM structures with portfolio-calibrated accuracy.
Read the brief
01
Autonomous Agents
Most enterprise AI stops at suggestions. Autonomous agents perceive context, reason through ambiguity, and take action across your systems -- completing in seconds what previously required hours of human coordination across teams and tools.
Read the brief
03
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
04
Decision Engines
Replace gut-feel decisions and static spreadsheets with intelligent systems that ingest thousands of data points, weigh competing factors in real time, and deliver explainable recommendations -- so your best people spend their time on judgment, not data wrangling.
Read the brief
06
Predictive Analytics
Turn operational data into demand forecasts, risk scores, and opportunity signals that compound in accuracy over time. No data-science team required -- Neume deploys production-grade predictive models with human-in-the-loop validation, so your team acts on insights, not equations.
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
12
Process Mining
Neume's AI process mining reconstructs real execution paths from system event logs, surfaces bottlenecks invisible to management, and quantifies the gap between documented procedures and ground-truth behavior -- in weeks, not quarters.
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
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.