Consultancies
Deliver a strategy deck and leave. Cannot write production code.
Writes the code. Stays until it runs.
Forward deployed engineeringLegal Services
We embed engineers inside your firm until it runs on AI.
From M&A due diligence to case intake triage, Neume Labs deploys Human-in-the-Loop AI systems that compress timelines, eliminate margin leakage, and scale your firm's capacity without scaling headcount.
60%
Reduction in contract review OPEX during M&A due diligence
3x
Increase in active deal capacity without net-new associate hires
$12M
Additional settlement pipeline captured through AI-powered intake triage
48hrs
M&A data room review compressed from 30 days
One engagement. From the first day on the floor to production.
Corporate / M&A01
What the engineer found
Am Law 200 firm with a 35-attorney M&A practice generating $28M in annual revenue, handling 18-22 mid-market transactions per year ($50M-$500M deal value).
The firm was losing competitive mandates to Big Four advisory firms offering AI-powered due diligence at 40% lower cost. Associates were burning out on repetitive data room review, driving 30% annual attrition in the corporate department. The practice was capacity-constrained at 4-5 concurrent deals.
02
What the engineer built
Corporate / M&A
Deployed Neume Labs' algorithmic due diligence engine across the entire M&A practice. AI ingested data rooms, extracted critical clauses, flagged non-standard liabilities, and generated structured risk matrices. Senior attorneys performed QA review of AI findings rather than line-by-line document review.
Built in the client’s repository. Runs on their infrastructure.
03
What the company got
Contract review OPEX slashed by 60%. Data room review compressed from 30 days to 48 hours. The firm increased active deal capacity to 12+ concurrent transactions without a single net-new associate hire.
3x increase in M&A deal capacity; $4.2M annual OPEX reduction; associate attrition in corporate department dropped from 30% to 12%
90 days from engagement to first AI-assisted deal closing
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
M&A Due Diligence Document Review
800-1,200 associate hours per mid-market deal; 4-6 week review timeline; firm capacity limited to 3-4 concurrent transactions
- 02Queued
Contract Clause Extraction & Risk Scoring
85% abstraction accuracy; 3-5 day turnaround on clause reports; material provisions missed in ~8% of reviewed contracts
- 03Queued
Deal Closing & Condition Precedent Tracking
15-20 paralegal hours per week on closing checklist maintenance; 12% of closings delayed due to missed conditions; post-closing obligations missed in 20% of deals
- 04Queued
Corporate Governance & Entity Management
5-8 compliance failures per year across client portfolio; 10-15 hours per board meeting cycle for materials preparation; $200K+ annual outside counsel cost for 50-entity clients
90-day build plan signed. First system: Corporate / M&A.
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.
3x increase in M&A deal capacity; $4.2M annual OPEX reduction; associate attrition in corporate department dropped from 30% to 12%
90 days from engagement to first AI-assisted deal closing
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.
- M2Case Intake Triage & Viability Scoringshipped
- M3E-Discovery Document Reviewshipped
- M4Regulatory Change Monitoring & Impact Assessmentin review
- M5Legal Research & Memorandum Preparationscoped
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 legal engagements, written the way an engineer would report them. Names withheld under NDA. Every figure confirmed.
Before
Am Law 200 firm with a 35-attorney M&A practice generating $28M in annual revenue, handling 18-22 mid-market transactions per year ($50M-$500M deal value).
The firm was losing competitive mandates to Big Four advisory firms offering AI-powered due diligence at 40% lower cost. Associates were burning out on repetitive data room review, driving 30% annual attrition in the corporate department. The practice was capacity-constrained at 4-5 concurrent deals.
Built
3x increase in M&A deal capacity; $4.2M annual OPEX reduction; associate attrition in corporate department dropped from 30% to 12%
Deployed Neume Labs' algorithmic due diligence engine across the entire M&A practice. AI ingested data rooms, extracted critical clauses, flagged non-standard liabilities, and generated structured risk matrices. Senior attorneys performed QA review of AI findings rather than line-by-line document review.
Result
Contract review OPEX slashed by 60%. Data room review compressed from 30 days to 48 hours. The firm increased active deal capacity to 12+ concurrent transactions without a single net-new associate hire.
Before
High-volume litigation firm (mass torts, personal injury) with 85 attorneys across 4 offices, processing 400+ intake inquiries per month and generating $65M in annual revenue.
The intake department was hemorrhaging viable cases. Paralegals took 3 weeks to evaluate each inquiry, by which time 35% of prospective clients had retained competitors. Two SOL near-misses in a single year triggered a malpractice insurance audit. The firm estimated $12M in lost settlement pipeline annually.
Built
Case evaluation time reduced from 3 weeks to 2 hours; $12M additional settlement pipeline captured; zero SOL misses
Deployed Neume Labs' autonomous case triage engine. AI instantly extracted medical diagnostic codes, injury dates, and liability signals from intake documents. The system calculated SOL deadlines, scored case viability against 8 years of historical settlement data, and routed high-value cases to partners within 2 hours.
Result
Case evaluation time compressed from 3 weeks to 2 hours. Zero missed SOL deadlines since deployment. Prospect attrition dropped from 35% to under 5%. The firm captured $12M in additional settlement pipeline in the first year.
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 legal 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 legal report8
Departments mapped
25
AI opportunities
27
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 8 are where legal 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 legal
Autonomous Agents
Contract review, obligation tracking, and regulatory filing workflows are document-heavy, deadline-driven processes where agents eliminate manual review bottlenecks and reduce missed-obligation risk.
Read the brief
Used in legal
Team Copilots
Associate research copilots, contract review copilots, and litigation support copilots that reduce research time, improve drafting quality, and increase realisation rates.
Read the brief
Used in legal
Document Intelligence
Contract review, due diligence document sets, court filings, and regulatory submissions benefit from LLM-powered clause extraction and structured contract analytics at scale.
Read the brief
Used in legal
Decision Engines
Matter risk assessment, resource allocation, and budget forecasting benefit from systematic analysis of comparable outcomes and workload data.
Read the brief
Used in legal
Workflow Automation
Contract lifecycle management, matter intake, and compliance review workflows require parallel review, version control, and audit-grade logging.
Read the brief
Used in legal
Knowledge Management
Law firms generate enormous volumes of work product that is reusable across matters but difficult to locate. Knowledge management dramatically improves precedent research, brief drafting, and associate productivity.
Read the brief
Used in legal
Compliance & Audit AI
Law firms face expanding client-mandated security assessments, bar association ethics requirements, data protection obligations across jurisdictions, and regulatory compliance demands from institutional clients -- all areas where automated compliance evidence generation reduces administrative burden on practicing attorneys.
Read the brief
Used in legal
AI Training & Enablement
High-value knowledge workers whose productivity gains from AI are substantial but whose adoption barriers (billable hour concerns, hallucination risk, ethical obligations) require specialized change management.
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
08
Data Integration & ETL
Enterprises lose 30-40% of analyst capacity to manual data wrangling -- reconciling mismatched schemas, chasing down custodian feed failures, and hand-mapping fields between systems that were never designed to talk to each other. Neume replaces brittle, rule-based ETL with AI agents that understand your data semantically, adapt to schema drift automatically, and deliver clean, reconciled datasets to downstream systems in hours instead of weeks.
Read the brief
11
Computer Vision
Turn cameras, scanners, and satellite feeds into structured, actionable data -- replacing manual visual inspection with auditable, sub-second analysis at any scale.
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
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.