SERVICES · FROM THESIS TO WORKFLOW
From an AI thesis to a working workflow.
A small set of services, matched to real decision moments — from pre-deal risk assessment, through a discovery sprint and agentic architecture, to a pilot hardened for production. Every engagement ends the same way: with a concrete, actionable map of the next step.
OFFER
Seven engagements — matched to where the organization is now.
We do not sell a long list of AI services. Each entry answers a specific decision moment — pick the one that best describes where you are right now.
AI value audit
For operating partners at PE funds, CEOs and COOs of portfolio companies, and management teams in large organizations that need a pragmatic AI plan. An ordered map of AI opportunities tied to business processes, an assessment of value, risk and feasibility, a shortlist of candidates for automation and agents, and a recommendation for the first pilot. A result that reads clearly for the board and is ready for execution.
Map the highest-value opportunitiesAI/IT due diligence
For PE funds, search funds, strategic investors and portfolio teams assessing acquisition risk or a scaling plan. An assessment of AI maturity and automation readiness, a review of technical risk, a review of data and cloud architecture where access is available, an assessment of agentic feasibility, and a value-creation map for the first 100 days. A result delivered as an annex to the investment memo or a summary for the board.
Assess the risk before the dealAgentic workflow discovery sprint
For enterprise teams exploring agents, owners of mid-sized companies with a repeatable bottleneck, and operators who need a narrowly scoped pilot. The scope of a single agentic workflow ready to build — trigger, data sources, actions, human approvals, audit trail, risks, qualitative measures and an implementation backlog. Scope before the build starts, not a demo in place of a strategy.
Map the first workflowAgentic architecture for enterprise
For CIOs, CTOs, transformation leads, innovation teams and organizations with demanding governance and compliance. A target, supervised architecture for agents across the whole organization — data sensitivity classification, model and deployment strategy, RAG and knowledge-layer architecture, a governance, audit and observability model, and an implementation map. A shared pattern instead of per-team silos.
Design the architectureRAG and knowledge systems
For advisory and professional-services firms, knowledge-intensive departments, RFP/proposal teams, and support and sales organizations. An ordered knowledge layer that supports search, AI assistance, RFP responses, onboarding and decisions — a knowledge-domain model, a source-feeding plan, metadata and confidence scoring, a retrieval and filtering design, and an optional interviewing agent that captures institutional knowledge.
Turn knowledge into AI contextAutomation pilot to production
For operators in SMBs and the mid-market, enterprise departments with one painful workflow, and portfolio companies under pressure to improve operations. A working AI-supported workflow — from a narrowly scoped pilot, through a human approval path and integration points, to a backlog that hardens it for production with monitoring, permissions and handover. We harden only once the value is real.
Build the first workflow that pays offAI operating model + team enablement
For companies moving beyond single pilots, portfolio companies that need repeatable AI adoption, and enterprise departments that need governance which does not block progress. A practical AI operating model — how to identify, approve, build, supervise and improve workflows with AI. A use-case intake model, data and risk classification, build/buy/partner decisions, a governance cadence, roles and responsibilities, a training plan, and a measurement-dashboard concept. AI adoption as a repeatable practice, not a one-off project.
Build a repeatable AI modelHOW WE WORK
One line of work — from question to scale.
Whichever engagement you choose, the work runs along the same line — so that strategy, architecture and implementation are one project, not three independent stages.
Diagnosis
We understand the context — the investment thesis, the operational bottleneck or the transformation goal — and point out where AI genuinely creates value and where it introduces risk.
Scope
We choose a narrow, achievable scope for the first move — one workflow, one decision, one architecture pillar — with clear boundaries and success criteria.
Architecture
We design the technical and organizational side of the solution — data, integrations, model roles, the human approval path, governance and the audit trail.
Build / pilot
We build a narrowly scoped pilot or the first iteration of the architecture — with real data, a real process owner and real risk.
Measurement
We check the effect on real work — whether the process is faster, lighter, less dependent on a single person; whether the decision actually changes; whether the architecture holds up under load.
Handover / scale
We harden for production, hand over to the team and — if the value is confirmed — scope the next pilot or the next pillar to scale.
WHAT YOU ALWAYS GET
A shared minimum in every engagement.
Whatever the scale — from an audit to an architecture — you always get five elements. It is the minimum that makes the work achievable and defensible.
Process mapping
A description of the work the team actually does — with the owner, inputs, decisions and outputs — so AI has something to build on.
Data and integration review
What is usable today, what is missing, where the foundations are to connect an agent or a model — with no hiding of technical debt.
A risk and governance perspective
Data classification, permission boundaries, human approval points and an audit trail — designed in from the start, not bolted on at the end.
Clear (qualitative) success metrics
What it means for this move to have succeeded — described qualitatively, in the language of process and decisions, not a marketing slogan.
A clear next step
What to do next week, next quarter and, if scaling is needed — so the meeting ends with an action, not a slide.
Knowledge base
Go deeper in the knowledge base
Decision guides that help you choose the scope and the approach.
- What is agentic AI — and how it differs from a chatbot and from automation
- Assistant, workflow, or AI agent — how to choose the level of autonomy
- Build vs buy: build your own agent or buy off the shelf
- What AI in production really costs: tokens, inference, maintenance
- The EU AI Act for businesses: how to organize your knowledge and data to be ready
LET'S START
Bring a process, a deal or a bottleneck.
Show us one workflow, one investment decision, or a place that is blocking growth — together we will pick the engagement that gives an actionable answer fastest.