Available · Q3 2026 engagements

Adjoint ConsultingAI and analytics
built through evidence.

AI and analytics systems, built through diagnosis, staged testing, and clear deployment decisions.

For B2B teams facing messy data, unclear workflows, model risk, or important questions about where AI should fit.

Book a diagnostic call→See the process↓
Case study · scattered → operated system
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Case study · fictional

From ambiguity to a bounded deployment decision.

A B2B company has scattered customer, sales, and operations data. Leadership is asking about AI. The real problem is unclear decision flow, weak feedback between teams, and unreliable data handoffs.

We move through eight stages. The diagram on the right is the same system, evolving. No fake metrics. No model-first reflex. Stop is a valid outcome.

Stage 01 / 08

Pressure

Costs are rising, decisions are slowing down, and teams are working from different versions of the truth. The team is considering AI, but the actual problem is still undefined.
Do not commit to a build before the problem is defined.
Stage 01 · pressure
delayed reportsmanual reviewunclear ownershipduplicate dataspreadsheet sprawlsalessupportoperationsfinancestrategy
Stage 02 / 08

Workflow Map

Map who makes decisions, what data they use, where judgment enters, and where delays happen. The first deliverable is a decision map, not a model brief.
A concrete map of decisions, data flows, ownership, and bottlenecks.
Stage 02 · workflow map
salessupportoperationsfinancestrategyroutingprioritizehumanreviewdashboardretrievalscoring
Stage 03 / 08

Evidence Check

Inspect data quality, access rights, missing fields, current baselines, and failure costs. Separate what can be trusted from what needs repair and what should not be automated.
A clear view of what can be tested safely and what would create avoidable risk.
Stage 03 · evidence check
✓trusted baseline!missing fields✓owner assigned✗high failure cost!permission issuesalessupportoperationsfinancestrategyroutingprioritizehumanreviewdashboardretrievalscoring
Stage 04 / 08

Narrow Intervention

Choose the smallest useful intervention: dashboard, retrieval layer, scoring model, review queue, or workflow tool. The goal is the smallest system that can improve a real decision.
A pilot that can be tested without pretending the full system is already solved.
Stage 04 · narrow intervention
PILOT // sales → routing → dashboardsalessupportoperationsfinancestrategyroutingprioritizehumanreviewdashboardretrievalscoring
Stage 05 / 08

Pilot

Deploy in a controlled setting with human review. Track accuracy, speed, user trust, failure cases, and operational friction.
Evidence from real use, not just a working demo.
Stage 05 · pilot
PILOT // sales · routing · dashboardSPEED—32%ACCURACY0.91TRUST4.1/5FAILURE CASES12FRICTIONlowsalessupportoperationsfinancestrategyroutingprioritizehumanreviewdashboardretrievalscoring
Stage 06 / 08

Decision Gate

Decide whether to continue, revise, or stop. Stopping is a successful outcome when the evidence says the system is not ready.
A clear deployment decision with an exit path if the pilot does not justify expansion.
Stage 06 · decision gate
continueevidence supports rolloutrevisefix scope, reteststopdo not deploysalessupportoperationsfinancestrategyroutingdashboard
Stage 07 / 08

Deployment

Integrate with existing systems, assign ownership, create monitoring, and define rollback paths. A deployed system needs operating rules, not just model outputs.
A system that can be maintained, reviewed, and corrected.
Stage 07 · deployment
↔ CRM↔ WAREHOUSE◆ owner: ops lead◆ monitoredrollbacksalessupportoperationsfinancestrategyroutingprioritizehumanreviewdashboardretrievalscoring
Stage 08 / 08

Scale

Expand only after the pilot proves useful in real workflow conditions. Scaling means adding governance, maintenance, and the next bottleneck — not just adding users.
Broader rollout with evidence, ownership, and a clear view of the next constraint.
Stage 08 · scale
— GOVERNANCE—— MONITORING—next constraint →salessupportoperationsfinancestrategyroutingprioritizehumanreviewdashboardretrievalscoring
System map · stage 01 / 08
pressure
adjoint · process diagram
v.1.0 · 8 stages
delayed reportsmanual reviewunclear ownershipduplicate dataspreadsheet sprawlsalessupportoperationsfinancestrategy
Engagements

Five ways to start, scoped to where the problem actually lives.

Each engagement is bounded. Each one ends with a recommendation — including the recommendation to stop.

01

Diagnostic Sprint

Workflow mapping, bottleneck identification, data review, and risk assessment.

Best for: Teams that know something is wrong but do not yet know what to build.

  • Decision and workflow map
  • Data and access review
  • Bottleneck diagnosis
  • Initial risk register
  • Build / narrow / defer / stop recommendation
02

AI and Analytics Pilot

Small, testable systems tied to a specific decision or workflow.

Best for: Teams that need evidence before committing to a larger build.

  • Bounded pilot scope
  • Baseline and success criteria
  • Prototype or workflow tool
  • Human-review process
  • Pilot results and next-step recommendation
03

Model & System Evaluation

Robustness testing, failure analysis, model review, and technical assessment.

Best for: High-trust use cases where failure is expensive.

  • Model or system review
  • Baseline comparison
  • Failure-mode analysis
  • Robustness and edge-case testing
  • Deployment risk assessment
04

Deployment Support

Integration planning, monitoring, ownership, documentation, and scale decisions.

Best for: Moving from prototype to operated system.

  • Deployment plan
  • Monitoring and review process
  • Ownership model
  • Rollback path
  • Scale decision framework
05

Technical Diligence

Independent review of technical claims, code, evidence, literature, and market context.

Best for: Investors, acquisitions, vendor assessment, or executive review.

  • Technical claim assessment
  • Code and implementation review
  • Literature and field-context review
  • Technical interview synthesis
  • Evidence-based risk summary
06

Not sure where to start?

The Diagnostic Sprint is built for that case. We agree the question first; the engagement type follows from what we find.

Start with a diagnostic →
Track record

Evidence-led technical work across research, development, and deployment.

Diligence

Technical diligence across AI / ML systems, consumer software, medtech hardware, and chemistry-heavy platforms.

Research

Scientific machine-learning research on model robustness, training-set selection, and out-of-distribution failure.

Product

Product development from user interviews to functioning end-to-end fully functioning AI application.

Method

Background across computational science, technical interviews, code inspection, and evidence review.

The common thread is evidence under uncertainty: deciding what is plausible, what is proven, what can be tested quickly, and what should not be deployed yet.

About

Led by Morris Trestman.

Adjoint Consulting is led by Morris Trestman — a technical consultant, machine-learning researcher, and chemist based in Berlin. The work combines technical diligence, scientific machine learning, model robustness, and practical AI / analytics system development.

The common thread is evidence under uncertainty: deciding what is plausible, what is proven, what can be tested quickly, and what should not be deployed yet.

01LinkedIn/in/morris-trestman02GitHub/morris-trestman03Gradient-Guided Furthest Point Sampling (paper)PDF →04Public GGFPS codeRepo →
Contact

Start with a diagnostic call.

The first step is not choosing a model. It is deciding whether there is a real problem worth building around.

Book a diagnostic call→moss@adjoint.tech
Reply within 1 business day · Based in Berlin