AI Engineering & AI Implementation

Projects

Built, not claimed

One released case. Further projects are under NDA.

360 Health & Performance

Scaling expert knowledge into an auditable AI system

Operationalising an expert methodology as an AI agent for sensitive, data-driven advisory processes. Used by athletes from the Bundesliga and the PGA Tour. Deterministic ML analytics produces numbers and scores. The LLM is intentionally limited to synthesis and communication. Observability makes quality, cost and error behaviour visible.

24/7

System availability

≤ 2ms

Kubios HRV inference

100%

Task Completion Rate

Transferable pattern: expert knowledge + sensitive data + sign-offs + operations. That is exactly where it is decided whether AI is more than a model.

trivago Image Concepts

End-to-end MLOps for intent-aware hotel image classification

100M+

Images Processed
Production inventory tagging

90+%

Classification Precision
across all classes

End-to-End

MLOps Ownership
R&D, training, hosting & deployment

NDA project

Cloud AI platform for multiple production use cases

Model routing, evals, monitoring, cost attribution and governance structures for scaled AI programmes.

Details after sign-off

Transferable patterns

Pattern: AI in recruiting & document-heavy processes

Transferable build pattern for recruiting and service workflows: CV routing and candidate triage with structured outputs, recruiter copilots on approved data sources, eval sets for answer quality and bias risks, clear roles and permissions, audit trails for decision-relevant steps.

Not a replacement for human decisions, but a controllable workflow with measurable quality.

Details in conversation

Book a call Further projects are under NDA. Details only after sign-off.

When we step in

We step in when AI is supposed to be more than a pilot

Typical situations:

01 Many AI ideas, but no clear prioritisation.

02 A use case or pilot is meant to become sign-off-ready and operable.

03 AI agents or multi-agent systems need to be integrated into real processes.

04 Quality, drift and error rates are not measured systematically via evals.

05 Cost cannot be attributed per use case, model or workflow (AI FinOps).

06 Audit, data protection and security need documentation & controls, not slides.

07 GenAI is already used uncontrolled in recruiting, customer service or document processes and needs structure, evals and sign-offs.

08 An existing process produces friction, errors or cost and is supposed to be not only automated but re-cut with AI.

The model is rarely
the problem

AI projects rarely fail because of the model. They fail because of unclear processes, weak data flows, missing sign-offs, lack of measurability or uncontrollable LLM cost.

That is why we often start at the workflow, not at the model. When the process is cut wrong, no LLM is good enough. We set model boundaries deliberately: deterministic analytics produces numbers, the LLM communicates.

Systems thinking

How we build AI systems that hold up in production

Measurable business value only emerges when data foundation, use cases, engineering, quality, cost and governance work together.

Understand processes first. Then build systems

We do not start at the model. The focus is on processes, data access, risks and economic goal. After that, agents, RAG workflows, evals, FinOps structures and operational documentation are built.

Use cases and process cut before tool selection.
Engineering before demo.
Evals before gut feel.
FinOps before cost explosion.
Governance across all layers.

Mental model, not a waterfall. In real projects the layers are sharpened iteratively.

Book a call

Services

Four entry points for AI that has to go into production

Four entry points for AI initiatives that need to show impact and go into production. We build the technical foundation, prioritise viable use cases, re-cut processes where it is economically sensible, deliver AI systems in engineering sprints and review existing AI agents in operation for quality, cost and controllability.

No mandatory path. The entry point depends on where the AI initiative currently stands.

Core offering

01 Use Cases

AI Use Case Workshops

AI consulting: from ideas to prioritised initiatives

For teams with many AI ideas, running GenAI experiments or processes that visibly produce friction. We evaluate use cases by business value, data situation, process impact, technical feasibility, regulatory risk, integration effort and production readiness.

Where necessary, we re-cut the underlying process rather than gluing AI on top of existing workarounds.

Typical when business units ask for AI, first tools are being tried out or a process is so manual that pure automation is not enough.

Output Prioritised use case list, top candidates, process and data-flow map, risk and feasibility assessment, clear decision: build, rebuild or deliberately leave.

Use Case Discovery · Process analysis · AI Readiness · Governance

02 Build

AI Engineering Sprints

AI implementation of production-near use cases

For prioritised AI initiatives that should be translated into a robust system quickly. We build AI agents, multi-agent systems, RAG workflows and internal AI apps with a focus on integration, evals, observability and operations.

Typical when a use case is clear and a production-near system should emerge from concept, pilot or PoC.

Output Running AI system, system architecture (IaC, Terraform), integrations, evals, observability, guardrails, audit trails, cost attribution, runbook and operational documentation for handover to internal teams.

AI Agents · Multi-Agent Systems · RAG · Bedrock · Azure OpenAI · Evals

03 Optimisation

AI FinOps & Production Review

MLOps review for AI agents and LLM systems in operation

For organisations with existing AI agents, RAG systems or LLM workflows in productive or production-near operation. We review quality, cost, architecture, observability, model usage, tool calls, error behaviour and governance structures.

Typical when LLM cost rises without cost attribution, answer quality fluctuates without evals, agents are barely traceable without audit trails or security, data protection and audit need reliable evidence.

Output Technical review, cost analysis, eval findings, architecture risks, optimisation plan, FinOps measures, governance gaps and concrete implementation recommendations.

AI FinOps · LLMOps · RAG · Evals · Cost Attribution · Monitoring · Governance

04 Foundation

Cloud & Data Platform Engineering

AI consulting for cloud and data foundation

When data access, cloud architecture or security baseline are not yet production-ready, we build the foundation. Supporting, not as a mandatory prerequisite — most AI initiatives start directly at Use Cases or Build.

Typical when AI initiatives are blocked by data access, permissions, infrastructure, security sign-offs or missing operational standards.

Output Cloud / data architecture, IaC (Terraform), integration paths, IAM / security baseline, monitoring foundation and technical operational documentation.

AWS · Azure · Terraform · Data platforms · APIs · IAM · Observability

Many projects combine several entry points: platform foundation, use case selection, engineering sprint and a later production review.

Book a 30-minute AI check Request the AI bottleneck scorecard

30 minutes. One process, one pilot or one use case list. At the end, the next step is defined — technically, economically and internally sign-off-ready.

References

Earlier engagements were delivered under Cloudsail Digital Solutions; that brand has been retired.

Further references from DAX and Fortune 500 programmes are available under NDA, on request.

“Embedding AI lifted 360 to a new level. Users keep control and see early what matters. Miki and the team listened deeply and delivered with great expertise. Stellar work.”

Justin Buckthorp · Founder & CEO, 360 Health & Performance

“They thought along openly and understood the problems we wanted to solve very quickly.”

Elio Santana · Technical Lead, Concentrix Tigerspike (Sydney)

Fit

Who we work with

We work with organisations where AI cannot be tried out casually and then somehow shoved into production later.

Typical environments

Insurers and banks
Energy utilities and municipal utilities
Recruiting and talent acquisition, e.g. CV routing, candidate triage, recruiter copilots
Customer service and service operations with sensitive data
Document- and sign-off-heavy B2B processes
Industry and manufacturing companies
Publicly regulated sectors and critical infrastructure
Health-adjacent and performance-oriented systems with sensitive data

Right when decisions, processes or data are sensitive, regulatorily relevant or business-critical and AI should be operated productively, auditably and economically.

Not right when a strategy deck is enough, a demo is the goal or hourly rates are being sought from a table.

About us

We are engineers,
not slide teams

We combine AI Engineering, cloud architecture and production operations for regulated and process-critical workflows in which data, decisions or processes are sensitive.

Founded by Mickey (Mikolaj) Graf. 13+ years AI, cloud and distributed systems. Experience from startups, mid-market, DAX corporations and Fortune 500 programmes. Focus: production-ready AI systems, multi-agent architectures and platforms under real compliance and scaling requirements.

Collaboration in an AI working group on the technical and operational implementation of the EU AI Act.

“The tech side is only half the deal. If the system is not signed off internally, it is worthless.”

Built so far

AI coaching agent for 360 Health & Performance with AWS, Bedrock, Custom ML, Terraform and LLM observability
Multi-agent systems with governance layer for EU AI Act readiness
AI / ML infrastructure at trivago, including image recognition pipelines over 100M+ images
AWS / IoT modernisation with latency reduction from 60s to 1–2s
E-mobility platforms for 100,000+ users in European charging networks
IT, security and audit are involved from the first use case workshop. No architecture decision without a sign-off path.

Anti-pitch

What we do not do

No pure AI strategy without engineering.
No demos without a production path.
No body leasing.
No slides that die after the workshop.

Built for audit.
Designed for production.

FAQ

Questions that CTOs, CIOs & CAIOs ask in first conversations

What gets clarified most often in first conversations — compact, without consultant prose.

What is AI Engineering?

AI Engineering is the engineering-grade delivery of AI systems: architecture, data flows, integrations, evals, deployment and operations. In contrast to PoCs and demos, AI Engineering targets AI implementation that runs sign-off-ready in production — with measurable quality, controllable cost and reliable evidence for security, data protection, procurement and audit.

Where does Cognitrace step into an AI initiative?

We step in where the initiative currently stands: at cloud and data platforms, at the selection of viable use cases, at delivery in AI Engineering Sprints or at the review of existing AI agents in operation. Not every project starts with a workshop — often there are already production-near workflows where cost, quality or evidence are unclear.

What is the difference between AI Use Case Workshops and AI Engineering Sprints?

AI Use Case Workshops clarify which AI initiatives are technically, economically and regulatorily sensible (business value, data situation, integration effort, risk profile, production readiness). AI Engineering Sprints deliver one prioritised use case as a running system — no slide deck, no isolated demo.

What does an AI Engineering Sprint actually deliver — a PoC or a production-near system?

A sprint does not end with slides or a demo. We deliver a running system on a clearly defined use case — including architecture decision, integrations, eval setup, deployment path and handover documentation. Whether immediate go-live happens or hardening comes first depends on the risk profile, data access and internal sign-offs. The architecture is designed for production from day one.

When is Cloud & Data Platform Engineering the right entry point?

When AI initiatives are blocked by data access, permissions, security sign-offs, missing interfaces or unstable deployment structures. Productive AI agents need clean data flows, IAM, logging/monitoring, CI/CD, cost structure and clear operational responsibilities.

How does an AI system integrate with existing infrastructure (SAP, on-prem, cloud, legacy)?

Integrations happen over approved interfaces and operational paths: SAP connections, existing data platforms, APIs, databases, event systems and secure on-prem / cloud connections. Vendor lock-in is reduced: models, vector stores, tools and orchestration remain replaceable as far as possible.

How are compliance, GDPR, EU AI Act and auditability accounted for?

Compliance is not an appendix but an architecture decision. GDPR conformity is evidenced via data flows, roles and permissions, deletion concepts and audit trails. EU AI Act is classified per use case (e.g. high-risk in recruiting, critical infrastructure, insurance pricing). Evidence emerges during engineering, not after the fact.

How is quality measured in operation (drift, hallucinations, error rates)?

We work with evals instead of gut feel. For critical use cases, test sets, metrics and monitoring on output quality, error behaviour, latency, cost and tool usage are created. Drift is detected via continuous re-evaluation against gold-standard data. On critical paths, guardrails, human-in-the-loop and structured outputs are applied.

What happens after an AI Engineering Sprint — who operates the system?

The standard is handover to the internal team with runbook, deployment path (CI/CD), monitoring and eval setup. When there is no internal AI Ops capacity yet, we take over operations temporarily — until the system is operable on its own. For existing systems, AI FinOps & Production Review serves as a regular health check.

How is ongoing cost made transparent and controlled?

Cost is modelled per use case and workflow before go-live (model usage, token volume, tool calls, data volumes, infrastructure). In operation, cost attribution per use case / model / workflow as well as FinOps measures emerge to make cost drivers visible and reduce them.

When does AI FinOps & Production Review make sense?

When AI agents or LLM workflows are already running productively / production-near and cost rises, quality fluctuates or evidence is missing. We review architecture, observability, eval findings, drift, cost drivers and governance gaps and deliver a prioritised optimisation roadmap with concrete measures for models, prompts, tools and infrastructure.

Is AI built into existing processes or are processes redesigned?

Both. Sometimes integration into an existing flow is enough. Often, however, impact only emerges when the process is re-cut with AI: different handovers, different data points, different review and sign-off steps. We decide that in the use case workshop based on impact, risk, data situation and regulatory requirements. We do not rebuild anything just because it is technically interesting.

Which internal roles are typically needed?

Typically a business owner for the process, a technical contact for data / system access and occasionally security / data protection / operations. The internal effort depends on the initiative; in early phases short coordination and access clarification is enough, in sprints tests and sign-offs are involved in a targeted way.

Contact

Is an AI initiative stuck before production?