Agent Opportunity Sprint
€1,500
A fixed-fee strategy sprint to score candidate workflows, map trust boundaries, and choose the right first pilot.
- Workflow readiness scoring
- Trust-boundary mapping
- Recommended first pilot
- Implementation roadmap
AI systems practice
Useful AI agent systems for teams that want leverage, not theater
I help founders, CTOs, and agencies turn promising AI workflows into usable systems. That usually means choosing one repeated workflow, defining the trust boundary, integrating the right context and tools, and shipping a version people can actually rely on.
Best fit for founder-led and CTO-led teams that want one useful workflow first, then a repeatable path to expand.
Internal AI Agent Build
Starting at €8,000
Done-for-you internal workflow systems for support, operations, research, and knowledge work with review steps built in.
- Workflow and output design
- Context, retrieval, and tool integration
- Review queues, approvals, and guardrails
- Launch, instrumentation, and iteration
Agent-Enabled Product Engineering
Starting at €10,000
AI product implementation for teams adding copilots, structured drafting, or bounded action workflows inside real products.
- Feature architecture
- Laravel and product integration
- Evaluation, fallback, and logging
- Production rollout support
How I think about useful AI systems
Workflow first
The first decision is not model choice. It is choosing one repeated workflow with clear value, a real owner, and a reviewable output.
Assist before autonomy
Most teams get more value from systems that prepare, classify, summarize, and recommend than from pretending the first version should behave like an autonomous employee.
Trust boundary early
I want clear rules about what the system can observe, draft, recommend, or trigger before implementation starts.
Production constraints included
Queues, persistence, costs, retries, logging, fallback paths, and approval steps are part of the design, not cleanup tasks after the demo works.
Example systems I can help design and build
Support prep agent
Pulls ticket history, customer context, and relevant docs to draft a structured reply and next action for a human to approve.
- Inputs: ticket, CRM, docs
- Output: draft + recommendation
- Review: support rep approves
Explore example →
Internal research agent
Takes a question, gathers sources across docs and notes, and returns a cited brief with open questions and a recommended next step.
- Inputs: docs, notes, transcripts
- Output: cited brief
- Review: operator checks synthesis
Explore example →
Agent-enabled product feature
Adds structured AI help inside a SaaS product with application rules, bounded actions, and customer-facing fallback paths.
- Inputs: product state, user request
- Output: draft or recommendation
- Review: user and app rules gate actions
Explore example →
Built for different buyers
For founders
Clarify where AI helps the business, avoid vague AI detours, and choose the right first system.
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For CTOs
Define architecture, trust boundaries, evaluation, and rollout sequence before the team disappears into experiments.
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For agencies
Package practical AI offers and bring in implementation depth when delivery needs to survive client reality.
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Production stack I care about
Model and prompt layer
Context and retrieval layer
Tool and integration layer
Workflow logic and output schema
Review and approval layer
Observability, evals, and fallback behavior
Why work with me on agents
10+ years shipping production software for teams, agencies, and product companies
Hands-on Laravel implementation background when agent systems need to connect to real stacks
Experience building AI-enabled products, including a medical coding system with tool orchestration and evaluation loops
Strong fit for teams that want strategy, implementation, and rollout thinking from the same person
Best next step if you are early
If your team is still choosing where AI should actually live, start with the strategy sprint. If you already have a repeated workflow with a clear owner and review step, we can jump into a build conversation.
Start with one workflow. Make it genuinely useful.
Send me the workflow, the available inputs, what a good output looks like, and where a human should stay involved. I'll tell you whether it sounds like a strategy sprint, an internal build, or an AI product feature.