The person who scopes it builds it
Founder-led delivery end to end. No junior handoff between the case and the code.
Otomat — Return Engineering
AI, underwritten.
I’m Adam Roseman — M&A banker, private-equity investor, CEO, and the AI engineer who writes the production code himself. I help private-equity firms and middle-market companies find the goals that drive value, align on the result and its measure, and then I build the production AI that makes it real. Paid on the operating case. Happy to be paid on outcomes.
Underwritten like an investor. Built like an engineer. Run like an operator.
Co-founder, with Shaquille O'Neal, of Steady (Inc. 5000 #86) · Senior Operating Advisor, Corbel Capital Partners · CTO / Chief AI Officer, PE-backed healthcare platform · Visiting Lecturer, Stanford GSB · Congressional testimony on AI income verification
Enterprise and regulated builds
Hands-on AI engineering since late 2022, including financial services and healthcare controls.
Business judgment on every call
CEO, founder, PE GP, and banker lenses — so the work is judged by value moved, not features shipped.
Two doors in
Bring the project — or bring the problem.
We have a project.
You arrive with a workflow, a platform, or a system in mind. I pressure-test the goal and the operating case, tighten the scope to what the numbers earn, and build it.
We know AI matters. We don't know what to do.
You arrive with a goal, a bottleneck, or a board asking questions. I find the value pools, align your team on the result and its measure, prioritize the first build — and then build it.
The entry point differs. The standard doesn’t: no build until the value and the measure are clear. Start with a Return Review →
The method
Return Engineering
Most AI engagements start with a model, a platform, or a list of use cases. Return Engineering starts with a business goal worth aligning around.
Identify the goals that drive value
Start with the business goal worth aligning around — not a model, a platform, or a list of use cases.
Align leadership on the result — and its measure
The sponsor, the operating owner and the builder agree what changes, who owns it, and how everyone will know.
Underwrite the operating case
Build, buy, process change, or no build — compared on the baseline, the full cost and the downside, before anyone commits.
Build the smallest production system that moves the measure
Secure AI around your existing systems, with permissions, exceptions and acceptance tests included.
Embed, track adoption, iterate
Train operators and maintainers, measure the change, and keep improving through approved releases until the change is real.
The Return Test
Name the lever — revenue, gross margin, operating cost, working capital, capacity, risk avoided. Show baseline → change → dollar effect, with the evidence labeled: measured in production, client-reported, or modeled estimate. State the clock: time-to-value and payback.
The Multiple Test
Finish the multiplication. ΔEBITDA × your multiple = enterprise value created. And build the kind of capability a buyer re-rates: recurring revenue, gross margin, growth, proprietary data.
Underwritten like an investor. Built like an engineer. Run like an operator. Read the full method →
Proof, labeled
Return Receipts
Each receipt is one shipped system: the lever, what changed, and the evidence label on the claim. Clients are anonymized by sector until permission lands.
Receipt 01
A PE firm's diligence platform
Lever: Operating cost + capacity
A small diligence team now clears contract and procurement analysis that used to wait on outside support. The system is live in production; the output has been used in an investment-committee discussion.
Evidence label: Client-reported
Receipt 02
A PE-backed healthcare platform
Lever: Risk avoided + operating cost
A caregiver safety system gated in code: recall ≥0.98 per language, false-alarm ≤0.01, latency ≤1550ms — thresholds enforced before any release ships.
Evidence label: Measured in production
Receipt 03
A veterinary AI platform (VetPulse — ours, named)
Lever: Capacity
67 voice-command evaluations run against production twice nightly; failures are reported, not rounded up — 66/67 is published as 66/67.
Evidence label: Measured in production
Receipt 04
A deal-sourcing research product (ReturnCatalyst — ours, named)
Lever: Revenue capacity
Source-backed company research with permission handling and job-completion reporting a sales team can rely on.
Evidence label: Measured in production
Receipt 05
A venture-backed prediction platform
Lever: Revenue — the core product
A highly scalable prediction engine built on large-scale datasets. Not a side tool or a pilot — the engine is the client's core product.
Evidence label: Client-reported
Receipt 06
A venture-backed AI company serving retailers
Lever: Capacity — full product & engineering
The full product and engineering role for a B2B AI business serving retailers: roadmap, build, and the production system its customers run on.
Evidence label: Client-reported
Receipt 07
A venture-backed AI marketing-services company
Lever: Capacity — full product & engineering
Full product and engineering for an AI business providing B2B marketing services — scoped, built, and shipped end to end.
Evidence label: Client-reported
Receipt 08
A PE-backed consumer financial services company
Lever: Revenue + retention
An AI-powered acquisition and retention system, designed and built end to end — product and engineering.
Evidence label: Client-reported
The wider evidence library — including what has not been tested and where acceptance remains unverified — is on the Results page.
The contrast
Not a consultancy. Not a one-product SaaS vendor.
Consultancies start with capabilities and bill hours. Vendors start with a product and sell lock-in. Model companies send engineers to deploy their platform after the use case is chosen.
OpenAI and Anthropic send forward-deployed engineers who are primarily engineers. Adam Roseman is a highly advanced forward-deployed engineer who has also worn the CEO and finance hats for a very long time — he can judge the goal and the P&L case, choose any model or recommend no build, and personally build the production system.
Otomat starts with your goal. Model-independent by design — the right model for the case, or no build at all. The person who scopes it builds it. No junior handoff between the case and the code.
Locked on the bottom line. Never locked in. We build the AI. You keep the upside.
Most AI agendas stop where the work begins.
Leadership gets a story. The team gets a pilot. Nobody owns the workflow, the economics, or the stop decision — and finance cannot reconcile the result. Otomat starts with a defendable business case and a named owner, then builds until operators can run the task without the builder.
Goal and owner
Which operating constraint matters, and who can change it? Observe the whole task, including exceptions and approvals.
Economics and alternatives
Establish the baseline, full delivery and review cost, and the build/buy/process-change/no-build choice. Capacity released is not automatically cash saved.
Acceptance before rollout
Agree the held-out tasks, permission tests, operator competence and stop decision before calling the workflow ready.
How the work connectsMeet the founder →
From the investment case to the operating workflow.
We connect operating assessment, application engineering and adoption around the business objective. Each mandate names the delivery responsibilities, client decision owners and evidence needed to advance. Capacity and specialist coverage are agreed in scope.
Start from the investment case
Connect the company or sponsor objective to a workflow, accountable owner, baseline and decision gate before choosing a system.
Follow the work into engineering
Define sources, integrations, permissions, evaluation and human review. Test portability and document replacement costs rather than promising zero dependency.
Make operating responsibility explicit
Agree release evidence, training, support and backup coverage for the engagement. Assess controls in the actual workflow and risk context.
Readiness beats urgency
What makes a mandate ready?
A stalled pilot, an acquisition plan or a recurring operating bottleneck can create a useful entry point. We test the prerequisites before committing to implementation.
Owner
Management can act
A sponsor priority needs an operating counterpart who can change the workflow and protect time for review.
Access
The dependencies are visible
Data rights, interfaces, procurement and security review belong in the plan before the build begins.
Evidence
Success can be judged
Set the baseline, representative tasks and acceptance rules. Include mistakes, review burden and full cost.
Capacity
The team can sustain it
Agree primary and backup owners, training, support boundaries and a manageable improvement backlog.
Buy a mandate with clear boundaries.
Start with a decision and a first workflow. Continue through adoption, operating measurement and approved improvements where the evidence supports it.
Operating assessment
A bounded decision: observe the task, map data and controls, establish the baseline and recommend build, buy, process change or no build.
Embedded transformation
A company mandate with explicit engineering capacity, integration, operator practice and an acceptance plan for prioritized workflows.
Fractional Chief AI Officer
A part-time (fractional) Chief AI Officer engagement: embedded with leadership to look at the entirety of the business and tackle workflows across the whole business, not one department at a time.
Sponsor operating mandate
A portfolio decision cadence with management participation, opportunity qualification and common governance. Company engineering is separately scoped.
Technical acceptance
Own the workflow. Test the dependencies.
Separate business meaning, data contracts and permissions from provider adapters. Model choice should follow the task, client constraints and measured tradeoffs. No architecture removes every supplier dependency or shared failure mode.
Inspect the application layer →Test model replacement
Run the same held-out tasks on a qualified alternative. Record quality, privacy, latency, full cost and migration effort. Portability is a test to perform, not a promise of effortless switching.
Control data and actions
Specify allowed sources, fields and tools. Test prohibited access and actions alongside permitted control cases. Record scope, version and residual risks.
Improve through approved releases
Feedback proposes a candidate. Independent evaluation, security checks, an accountable human decision and rollback are required before production changes.
Leave the client capable
Deliver source and configuration under agreed rights, dependency inventory, export/restore instructions and a maintainer/backup recovery exercise. Third-party services retain their own terms.
Continuing accountability
Know what the retainer buys.
Agree the mandate, included capacity, decision cadence, support responsibilities and exit terms before starting. Continuing work earns its place through adoption, monitoring and a prioritized improvement backlog.
Explicit capacity
Engineering, company assessments, working sessions and response commitments are scoped. A sponsor retainer does not include unlimited builds across a portfolio.
Decision-ready reporting
Show implementation, usage, accepted outcomes, forecast and unresolved dependencies separately. Include review effort, model/vendor costs and client time.
Optional outcome terms
If agreed, outcome-linked fees need a baseline, attribution, measurement period and value-recognition rule. Released capacity, working capital and EBITDA are distinct; financial returns are not guaranteed.
The working relationship
One operating agenda. Named responsibilities.
A credible mandate connects the sponsor who approves the investment, the manager who owns the workflow and the people who build and maintain it.
The engagement charter names the Otomat delivery lead, engineering responsibility, client operating owner and backup. Confirm the actual staffing and availability for the agreed scope before work starts.
Independent adversarial testing, enterprise penetration testing, incident response and legal interpretation require appropriately qualified specialists. They are separately scoped rather than implied by an application build.
Read about Otomat →Leadership decides
Defend a go/no-go decision using the baseline, full cost, risk and client dependencies.
Operators demonstrate
Complete a fresh normal task and exception case, with correct escalation and manageable review effort.
Maintainers take over
Change, test and restore a version. Have the backup repeat the recovery runbook independently.
Industry workflow libraryStart with the work.
Start with the work.
Qualify the sector context.
Explore documented patterns and capability-led applications. Sector breadth describes where to investigate a mandate; the evidence state shows what has actually been documented.
PE & Portfolio OpsDocumented patternLogistics & Supply ChainCapability-ledFraud Prevention & RiskCapability-ledHealthcareDocumented patternRetail & ConsumerCapability-ledOnline MarketplacesCapability-ledGovernmentCapability-ledHospitalityCapability-ledFinancial ServicesCapability-ledMedia & EntertainmentCapability-ledAnimal HealthCapability-ledMarket IntelligenceCapability-led
Explore All IndustriesFrequently Asked Questions
Who is Otomat for?
Private equity sponsors and middle-market company leaders in the US and Europe. The work connects operating goals, application engineering and workforce capability. Fit depends on the workflow and available evidence, not the industry label alone.
Who actually does the work?
Founder Adam Roseman performs the hands-on AI engineering himself; the person who scopes the work builds it, trains the team and maintains it. For the past three years he has led full-scale AI transformation and restructuring for organizations from startups to billion-dollar enterprises, including regulated environments. He previously co-founded and led as CEO an AI and machine-learning fintech that turned billions of unstructured transaction records into financial intelligence (No. 86 on the Inc. 5000), earlier founded a media data business (Ad Age Global 40 Under 40), and began as an M&A investment banker with private-equity investor experience. Engineering capacity, specialist coverage and backup responsibilities are agreed for each engagement.
How does an engagement start?
Start with your operating goal, accountable owner and first workflow. A Return Review is a working session about constraints and next decision—not free diligence or a commitment to a build. If there is a fit, the next step is a scoped assessment or operating mandate for review.
How is this different from an AI consultancy or software vendor?
Otomat underwrites the case, builds secure AI around your existing systems, and stays through adoption—strategy, engineering, training and maintenance led by one senior AI engineer. The discipline connects investment judgment, operating judgment and engineering judgment into one delivery process with explicit acceptance and handover.
We already bought Copilot or ChatGPT Enterprise; where does Otomat fit?
A tool rollout deploys a product. AI restructuring changes the workflow, ownership, controls, adoption and measurement around a business goal. Installing software alone does not establish operating change. The work may recommend using an existing tool, a process change or a custom build depending on the evidence.
Do you guarantee a return?
No. There is no promise of a particular financial return or rollout date. Fees, capacity, exclusions and any performance component are negotiated for the specific mandate. Agree the baseline, measurement window, attribution and client dependencies before committing to a build.
What happens after launch?
Handover requires that the client primary and backup each perform a fresh task, diagnose an exception, release an approved change and restore a known version. Workforce capability, controlled improvement and operating measurement are acceptance tests. Training attendance alone does not establish independence.
The door
The Return Review
A working session, not a sales call. Bring one goal — or one problem. We underwrite it live: the lever, the baseline, the measure, the build / buy / process-change / no-build call, and what it would be worth at your multiple.
You leave with the case either way. If it doesn’t earn a build, I’ll tell you in the room.