Agentic AI pilot case study
A concrete example of how we frame pilots in regulated environments.
TAPE Nº 01“Agentic AI Consulting”SP · Super AI Labs · Zürich
Agentic AI consulting for teams moving beyond chat demos.
Agentic AI can be valuable, but only when the workflow, tool permissions, retrieval quality, evaluation, and human oversight are designed deliberately.
We help clients move from vague agent ambitions to clear use cases, grounded architecture, pilot delivery, and production readiness criteria.
Practical outputs, not generic AI strategy
- 01
Use-case shaping and prioritization based on business value, data readiness, and operational risk.
- 02
System design for retrieval, tools, memory boundaries, escalation paths, and human approval steps.
- 03
Pilot implementation, evaluation design, failure analysis, and production-readiness recommendations.
- 04
Integration planning for internal systems, identity, content access, and monitoring.
Who this service is for
- 01
Organizations exploring internal copilots, analyst assistants, case-worker support, or multi-step knowledge workflows.
- 02
Teams that already tested LLMs and now need orchestration, retrieval, tool use, and guardrails to make the system dependable.
- 03
Programs in pharma, public sector, finance, and insurance — accountability-heavy environments (nFADP, FINMA, GxP) where human review and auditability matter.
Designed for reliability, grounding, and operational handover
The exact stack depends on the problem, but these are the design principles we usually optimize for.
Grounded agent workflows
Grounded agent workflows with retrieval, structured tool calls, and clear task boundaries.
Human oversight by default
Human-in-the-loop checkpoints for sensitive actions, ambiguous outputs, and exception handling.
Inspectable behavior
Evaluation and observability patterns that make agent behavior inspectable rather than mysterious.
Hands-on delivery experience
Hands-on experience with multi-agent and RAG-oriented delivery rather than only prompt experimentation.
Process, tech, and governance aligned
A consulting mindset that keeps business process design, technical implementation, and governance aligned.
Controllable, supportable systems
A practical bias toward agent systems that reduce work while staying controllable and supportable.
Questions we hear early in the conversation
These are the kinds of questions that usually matter before a team commits to scope, architecture, and rollout.
- What is the difference between a chatbot and an agentic system?
- A chatbot answers prompts. An agentic system retrieves information, uses tools, follows workflow steps, and often coordinates with humans or downstream systems.
- Do all AI use cases need agents?
- No. Many problems are better solved with simpler retrieval or automation patterns. We help clients choose the lightest architecture that can still deliver results.
- Can you help us evaluate whether an agent is safe enough to deploy?
- Yes. Evaluation, failure modes, human-review design, and operational boundaries are a core part of how we approach agentic AI delivery.
- Can you also roll agents out across our organization?
- Yes. Consulting is where we shape the use case and validate a pilot. When you are ready to deploy agent swarms into production across departments, that continues as our implementation of AI agents service, with the same team.
Case studies, articles, and next steps
If this is close to what your team needs, these pages are the best next places to look.
Implementation of AI agents
Take a validated pilot into production with agent swarms deployed across departments.
Discuss an agentic AI use case
Start with a scoped conversation around feasibility, architecture, and rollout risk.