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Proof Before Promotion

We measure in dollars, not demos.

Otomat's firm-level record includes $30M+ in annualized EBITDA impact across 12+ operating companies and 3 PE fund partnerships, with actionable roadmaps in <30 days and 70%+ adoption within 90 days. The anonymous track record below separates those aggregate figures from the narrower delivery evidence each workstream supports.

Discuss Your Operating CaseJump to the Track Record
A CFO and operating, engineering, and evaluation peers review a financial measurement boundary.

Representative operating interface · no client data

Value and evidence review

  1. Baseline
  2. Observed
  3. Modeled
  4. Projected
  5. Synthetic
  • Observed
  • Modeled
  • Projected
  • Synthetic

Finance review

Digital analyst bench

  1. EvidenceWorking
  2. FinanceWorking
  3. ReconciliationWorking
  4. MeasurementWorking
$30M+
Annualized EBITDA impact
12+
Operating companies transformed
3
PE fund partnerships
<30 days
To an actionable roadmap
70%+ within 90 days
User adoption

These are owner-supplied aggregate results across Otomat's work. They should not be allocated to an individual client, system, or case without the underlying commercial evidence and publication permission.

How We Measure

A result needs a baseline, an owner, and a confidence label.

  1. 01

    Baseline the old workflow

    Document volume, time, cost, error, throughput, conversion, quality, risk, and active-user behavior before the intervention. If a metric did not exist, define how it will be captured.

  2. 02

    Define the intervention

    Record exactly what changed: system, workflow, roles, approvals, policies, data, and operating cadence. “We launched AI” is not an intervention definition.

  3. 03

    Measure use and operating movement

    Track who uses the new workflow, how often, for what eligible population, with which exception patterns, and how the operating metric changes.

  4. 04

    Translate to financial impact

    Connect operating movement to revenue, gross margin, operating expense, working capital, or avoided loss with finance-reviewed assumptions.

Label the confidence

Realized
Recorded in the business or financial system for the completed measurement window.
Observed
Measured in production operations but not fully recognized in financial reporting.
Modeled
Calculated from observed operating movement and explicit assumptions.
Projected
Expected before sufficient post-launch evidence exists.
Synthetic validation
Proven against a controlled test corpus, not real-world outcome data.

Proof Trace

Open the system. Follow the evidence.

Each proof entry can be traced from the operating goal to the evidence boundary without pretending a delivery artifact is a client outcome.

Board, finance, evaluation, and engineering peers test one proof boundary.

Representative operating interface · no client data

Proof trace

Evidence review

Digital analyst bench

  1. GoalWorking
  2. RulesWorking
  3. Model roleWorking
  4. ApprovalWorking
  5. EvaluationWorking
  6. DeploymentWorking
  7. BoundaryWorking
Representative proof path. Individual workstream records remain anonymous and bounded by their published evidence.
  1. 01Operating goal and workflow
  2. 02Deterministic rules and source boundaries
  3. 03Model or automation role
  4. 04Human approvals and exception paths
  5. 05Evaluation and adversarial tests
  6. 06Deployment evidence
  7. 07What the record proves—and what it does not
Discuss the Pattern With Our Team

Selected Track Record

Big operating goals. Systems built to carry them.

These records show how our team works from management objective through scope, product, engineering, deployment, and operating practice. The firm-level aggregate record is not allocated to any individual entry.

Client and product identities are withheld throughout. We remove names, locations, company scale, counterparties, proprietary metrics, and distinctive facts that could identify the organization. The specificity stays where it belongs: the objective, our mandate, the system, and the proof boundary.

01

Retail returns and fraud-prevention operations platform

Retail, e-commerce, reverse logistics, and fraud prevention

Built, deployed, and production-hardened

Client and product identity withheld

  • Management objectiveMake return decisions more consistent and evidence-based while strengthening policy enforcement, exception handling, and operator review.
  • Our mandateWe worked from stakeholder feedback and management priorities through product scope, workflow design, multi-tenant governance, full-stack engineering, deployment, and production validation.
  • Built and put into practiceA multi-tenant operating platform spanning policy-aware intake, product-evidence review, role-based decision support, explainable scoring, governed overrides, and operational analytics.
  • What this demonstratesEnd-to-end product and engineering ownership where AI judgment is bounded by policy and human control.
Delivery evidence
Source code, live services, production certification, and UI, API, and log evidence were reviewed.
Public boundary
No ROI, loss reduction, fraud accuracy, throughput, adoption, or named-customer result is claimed.
02

Multi-stakeholder healthcare operating system

Healthcare services and workforce operations

Release candidate shipped; final cutover client-controlled

Client and product identity withheld

  • Management objectiveCreate one governed operating layer for workforce coordination, internal exceptions, service-recipient visibility, and enterprise reporting.
  • Our mandateWe led executive discovery, translated management goals into a quantified product plan, scoped the operating model, and carried the work through application design, engineering, testing, and hosted release.
  • Built and put into practiceA standalone system with separate workforce, operations, service-recipient, and client applications; deterministic eligibility and provenance controls sit ahead of AI-assisted ranking and conversation.
  • What this demonstratesManagement-to-production ownership across a regulated, multi-stakeholder workflow with deterministic safeguards ahead of model judgment.
Delivery evidence
Executive-discovery artifacts, product specifications, source code, hosted surfaces, release evidence, and adversarial controls were reviewed.
Public boundary
Hosted and production-shaped does not mean live regulated-data cutover, broad adoption, clinical impact, or client economics.
03

Government program-integrity review system

Government, public services, fraud prevention, and program integrity

Designed, built, and deployed as a controlled prototype

Client and product identity withheld

  • Management objectiveHelp review teams prioritize complex work, connect evidence across systems, and preserve human oversight for consequential decisions.
  • Our mandateWe defined the product and architecture, sequenced the implementation, designed analyst and executive workflows, built the full stack, and prepared the system for pilot-oriented evaluation.
  • Built and put into practiceA controlled analyst workbench combining case triage, graph-assisted entity review, research workflows, evidence assembly, executive briefing, and human-governed AI orchestration.
  • What this demonstratesComplex public-sector product design, full-stack engineering, graph and research orchestration, and explicit human decision rights.
Delivery evidence
Product and architecture artifacts, source code, hosted demonstration surfaces, and active backend services were reviewed.
Public boundary
The system uses synthetic demonstration data and is not represented as a production-validated agency deployment, compliance certification, or measured recovery result.
04

Event-driven market-intelligence platform

Information services and market intelligence

Built and deployed as a read-only intelligence product

Client and product identity withheld

  • Management objectiveConvert fragmented, time-sensitive event data into validated and auditable intelligence that can be delivered safely through standard customer interfaces.
  • Our mandateWe shaped the product position and roadmap, designed the data and analysis architecture, built the application and integration surfaces, and established release, evaluation, and fail-closed deployment gates.
  • Built and put into practiceA cloud-hosted intelligence product with signal validation, secure API and agent access, provenance controls, authenticated delivery, and fail-closed handling for unready data paths.
  • What this demonstratesStatistically governed product engineering, secure interfaces, customer scoping, and disciplined separation of insight from execution.
Delivery evidence
Source code, live read-only endpoints, customer-facing capability matrices, deployment controls, and production checks were reviewed.
Public boundary
Some delivery and data paths remain gated. No signal performance, profitable edge, customer return, commercial adoption, or execution activity is claimed.
05

Creator intelligence and partnership workflow

Marketing technology and creator operations

Implemented and deployed; outbound action remains human-controlled

Client and product identity withheld

  • Management objectiveFind credible creator and content signals, assemble reviewable evidence, and turn qualified opportunities into a governed partnership workflow.
  • Our mandateWe carried the venture from product thesis and cost model through workflow design, system architecture, full-stack engineering, asynchronous operations, release automation, and deployment validation.
  • Built and put into practiceAn authenticated operator platform with source-aware discovery, asynchronous audio and video analysis, evidence records, opportunity matching, human-reviewed outreach drafting, deal-state tracking, and operating-health visibility.
  • What this demonstratesA commercially oriented AI product built end to end, with multimodal analysis, durable workers, evidence review, and human control at the action boundary.
Delivery evidence
Source implementation, automated tests, cloud services, release workflows, and authenticated production smoke evidence were reviewed.
Public boundary
Outbound sending remains gated. No completed partnership, detection accuracy, conversion, revenue, adoption, or ROI result is claimed.
06

Veterinary workflow and documentation platform

Animal health and veterinary operations

Built, deployed, and reachable at the time of review

Client and product identity withheld

  • Management objectiveUnify consultation capture, structured documentation, research, client education, and practice-workflow outputs in one professional-facing application.
  • Our mandateWe owned product implementation, experience design, full-stack engineering, voice and document workflows, deployment automation, and live release support.
  • Built and put into practiceAn authenticated workflow layer covering consultation capture, structured documentation, research, client education, history, voice interaction, and hosted backend services.
  • What this demonstratesMulti-step domain workflow engineering across voice, structured records, document outputs, history, authenticated interfaces, and live infrastructure.
Delivery evidence
Application routes, source code, deployment automation, hosted frontend, backend health, and dated end-to-end evidence were reviewed.
Public boundary
Platform deployment does not establish that every capability is production-validated, or prove clinical accuracy, patient outcomes, adoption, time savings, practice revenue, or compliance certification.
07

Multi-tenant content-operations engine

Marketing and content operations

Built and deployed

Client and product identity withheld

  • Management objectiveScale brand-aligned, multi-channel content work while preserving tenant isolation, traceability, cost visibility, and approval controls.
  • Our mandateWe defined the product and operating model, designed the multi-tenant architecture, built the web, API, worker, and agent layers, and carried the system through test, deployment, and end-to-end release validation.
  • Built and put into practiceA multi-tenant platform coordinating brand strategy, trend analysis, ideation, image and video workflows, asset operations, channel adaptation, and parallel brand, compliance, and quality review with a fail-closed final gate.
  • What this demonstratesMulti-agent and multimodal product engineering with durable asynchronous operations, tenant controls, traceability, and hard approval gates.
Delivery evidence
Canonical product specifications, source implementation, tests, deployed web, API, and worker services, and successful end-to-end release evidence were reviewed.
Public boundary
No content-volume, campaign-speed, conversion, productivity, cost-savings, brand-performance, autonomous-publishing, or financial outcome is claimed.
08

Private-markets investment workflow platform

Private equity, deal teams, and portfolio operations

Designed and built; hosted application surface live

Client and product identity withheld

  • Management objectiveAccelerate and standardize investment analysis, committee preparation, knowledge retrieval, collaboration, and portfolio oversight.
  • Our mandateWe translated investment-team workflows into the product roadmap, designed the domain architecture, and owned the application, document intelligence, orchestration, controls, infrastructure, and continuing enhancement program.
  • Built and put into practiceA secure platform spanning document and financial intelligence, source-grounded search, deal research, committee preparation, model and memo workflows, portfolio monitoring, collaboration, and administrative controls.
  • What this demonstratesDeep domain product work and broad engineering ownership across the investment lifecycle, from source documents to committee and portfolio workflows.
Delivery evidence
Engagement scope, active application surfaces, source implementation, hosting, and deployment infrastructure were reviewed.
Public boundary
Current feature-by-feature backend release status is not asserted. No investment return, time saving, analytical accuracy, adoption, or portfolio-company result is claimed.
09

Governed retention-communications control plane

Consumer financial services

Scoped, designed, and built; synthetic management preview deployed

Client and product identity withheld

  • Management objectiveImprove retention and reactivation through governed educational communications and explicit preference feedback while preserving consent, policy, and auditability.
  • Our mandateWe led discovery and phased scoping, made the product and architecture decisions, built the synthetic vertical slice and management evidence experience, and incorporated leadership decisions into the operating contract.
  • Built and put into practiceA governed control plane with consent-first eligibility, approved-content decisioning, deterministic rendering, persistent holdouts, immutable manifests and outbox records, fake-provider handoff, signed feedback, verified webhook normalization, reconciliation, and a management evidence dashboard.
  • What this demonstratesManagement-to-engineering ownership of a governed high-scale system where deterministic policy and immutable evidence control every downstream action.
Delivery evidence
Canonical scope, decision records, source implementation, local policy and load checks, production-readiness evidence, and a hosted read-only synthetic preview were reviewed.
Public boundary
Customer systems, member data, model calls, provider traffic, and sends remain gated. No retention, reactivation, deliverability, legal, compliance, scale, or production-send outcome is claimed.
10

Private-markets AI operating advisory

Private equity operating strategy and diligence

Ongoing advisory, training, and opportunity assessment

Client and product identity withheld

  • Management objectiveIncrease staff AI fluency and create a repeatable way to evaluate automation potential, competitive disruption, and value-creation opportunities in prospective and portfolio companies.
  • Our mandateWe work with leadership on role-based training, workflow guidance, operating-roadmap reviews, and structured assessments that connect company goals to practical AI opportunities, risks, and build priorities.
  • Built and put into practiceA role-based enablement and diligence framework with onboarding, workflow guidance, progress instrumentation, and a repeatable assessment method for automation opportunity and disruption risk.
  • What this demonstratesThe operating layer around the software: executive alignment, staff capability building, opportunity diligence, prioritization, and a path from company goals to implementation.
Delivery evidence
The ongoing advisory mandate is confirmed by the engagement owner; the underlying training, company, deal, and assessment artifacts remain confidential.
Public boundary
No client, fund, executive, target, transaction, portfolio company, assessment conclusion, attendance, adoption, EBITDA estimate, or realized outcome is disclosed or implied.

Publication Standard

Inspectable proof beats a dramatic headline.

Every future Otomat case study should publish these fields with client permission. If a case cannot support them, we publish it as a delivery note—not an outcome case study.

  • Client context and anonymization level
  • Workflow and baseline
  • System and operating-model change
  • Production date and user cohort
  • Adoption denominator and measurement window
  • Operating result
  • Financial translation
  • Confidence label
  • Risks, exclusions, and unresolved limitations
  • Named reviewer or evidence owner

Direct answers

Frequently asked questions

Are Otomat's results independently audited?
The figures on this page are owner-supplied firm-level results and are not presented as an independent public audit. Individual case entries state the narrower system, test, deployment, or architecture evidence available for publication. Qualified buyers can request a deeper evidence review subject to client confidentiality.
Why do some cases have no ROI figure?
Because a deployed system does not automatically prove a financial result. We publish an ROI or EBITDA number only when the baseline, measurement window, attribution, assumptions, and client permission support it.
What does 70%+ adoption within 90 days mean?
It is Otomat's aggregate firm-level adoption figure. Each engagement must define the eligible user cohort, the behavior that counts as active adoption, and the 90-day measurement window before reporting a comparable result.
Can we review more detailed proof?
Where permissions allow, yes. A working session can include a deeper review of architecture, release evidence, evaluation design, adoption methodology, and financial measurement without exposing another client's confidential data.

Board-Ready Evidence

Bring the result your board needs to trust.

We will work backward from that result to the workflow, baseline, system, and evidence required to make it real.

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