Ship AI Agents That Survive Production
Take AI agents and LLM applications from proof of concept to production-grade systems. Reliability, safety, and observability are built in from day one. Covers solution design, model and architecture selection, RAG and retrieval, evaluation harnesses, runtime guardrails, observability, and LLMOps.
95%
of enterprise GenAI pilots reach no measurable return (MIT NANDA)
67%
vendor-partnered builds succeed vs 33% in-house
30%+
accuracy lift within weeks of eval-driven delivery
The Challenge
Why Pilots Stall Before Production
Pilot Purgatory
A demo that impresses in a meeting never wires into the real workflow. MIT NANDA found 95% of enterprise GenAI pilots reach no measurable P&L impact. The gap is an integration and governance problem, not a model-quality problem, so a standalone chatbot stays a standalone chatbot.
No Way to Tell If It Works
Brittle exact-match tests miss semantic failures. Teams correct a bug today and repeat it tomorrow somewhere else because nothing captures the learning. Without an evaluation harness and tracing, quality is a guess and regressions ship silently.
Unsafe Outputs Reach Users
Hallucination, prompt injection, jailbreaks, and PII leakage hit hardest in customer-facing features, where visibility is highest and guardrails matter most. With no runtime layer in front of the model, every off-policy response is a live incident.
The Market Context
The Pilot-to-Production Gap, in Numbers
Market Headline
of enterprises see no measurable return on GenAI. The other 5% run delivery as a discipline, not a demo.
Source: MIT NANDA, The GenAI Divide: State of AI in Business 2025
Vendor-partnered builds succeed
vs 33% for in-house builds (MIT NANDA)
In-house-only builds succeed
Partnered delivery succeeds about 2x as often
Accuracy lift from eval-driven delivery within weeks
Evaluation-first delivery pattern, AI eval platforms
How AI Agents Execute
How I Take an Agent From Pilot to Production
Five steps that turn a working prototype into a system you can trust in front of customers. Each step ships a concrete artifact you keep.
Solution Design and Model Selection
DesignI define the use case against real data and real systems, then select the model and architecture with build-versus-buy discipline. Commodity tasks route to small cheap models, and custom build is reserved for what is genuinely core to you.
Build and Ground With RAG
BuildI engineer the agent and wire it into live workflows, then build a retrieval pipeline with reranking and citations so responses are grounded and traceable to a source. The result integrates into your systems rather than sitting beside them.
Evaluate With LLM-as-Judge
EvaluateI stand up an evaluation harness with LLM-as-judge across RAG, agent, safety, and custom evals, backed by a labelled dataset. Semantic quality scoring replaces brittle exact-match tests and commonly delivers a 30%+ accuracy lift within weeks.
Guardrail at Runtime
GuardrailOffline evals become runtime guardrails that validate inputs and outputs before they reach the user, targeting hallucination, prompt injection, jailbreak, and PII leakage. Humans stay accountable for high-visibility outputs through approval workflows.
Launch With LLMOps
OperateI launch with tracing, monitoring, and cost attribution running, then hand over the observe, evaluate, guardrail, improve loop as one discipline. Trace collection runs asynchronously, so there is no latency cost to the application.
The Approach
Reliability, Safety, and Observability, Built In
Three engineering disciplines layered into the agent from day one, each carrying its own concrete practices rather than bolted on after launch.
Layer 1
Reliability
Grounded retrieval and disciplined model selection so the agent holds quality as traffic and edge cases grow.
- ✓RAG and retrieval pipelines with reranking and citations for grounding accuracy
- ✓Model and architecture selection with build-versus-buy discipline
- ✓Model routing so easy tasks run on small cheap models and cost stays controlled
- ✓Integration into live systems and workflows, beyond a standalone chatbot
Layer 2
Safety
A runtime layer in front of the model that enforces policy on every input and output before it reaches a user.
- ✓Runtime guardrails against hallucination, prompt injection, jailbreak, and PII leakage
- ✓Offline evals converted into production guardrails, closing the loop
- ✓Human accountability and approval workflows for high-visibility outputs
- ✓Governance mapping for EU AI Act scope and ISMS categorisation
Layer 3
Observability
Full tracing and measured signal so agent behaviour is a number you can act on instead of a guess.
- ✓Tracing and monitoring for multi-step agents, with latency and cost attribution
- ✓Evaluation harness with LLM-as-judge across RAG, agent, safety, and custom evals
- ✓Failure-pattern analysis that prescribes the fix, not just the symptom
- ✓Async trace collection that adds no latency to the application
Architecture
The Production AI Stack
The layers I assemble so an agent is grounded, governed, and measured end to end. Hover any layer to see the tools I reach for.
Data and Retrieval
Model and Routing
Guardrails and Policy
Evaluation and Tracing
Application
Hover any layer to explore details and technology options
Engagement Model
From Proof of Concept to Production
A four-step engagement that takes an AI agent or LLM application from a working prototype to a reliable, observable, production-grade system.
Solution Design and Architecture
Week 1-2
Define the use case, select models and architecture, and design the retrieval and integration approach against real data and real systems.
- ✓Architecture and model-selection decision, with build-versus-buy rationale
- ✓RAG and integration design grounded in your data sources
Build and Ground
Week 3-6
Engineer the agent or LLM application, wire it into live workflows, and build the retrieval pipeline so responses are grounded and citable.
- ✓Working agent integrated into real systems, not a standalone demo
- ✓Grounded retrieval pipeline with reranking and citations
Evaluate and Guardrail
Week 6-9
Stand up the evaluation harness and tracing, then convert offline evals into runtime guardrails that intercept unsafe or off-policy outputs.
- ✓Evaluation harness with LLM-as-judge and a labelled dataset
- ✓Runtime guardrails and tracing wired into the request path
Production Launch and LLMOps
Week 9-12
Launch with monitoring, cost controls, and the observe, evaluate, guardrail, improve loop running, then hand over an operable system.
- ✓Production-deployed system with monitoring and cost attribution
- ✓LLMOps runbook and a measurement baseline for ongoing improvement
Phase 01
Solution Design and Architecture
Define the use case, select models and architecture, and design the retrieval and integration approach against real data and real systems.
- ✓Architecture and model-selection decision, with build-versus-buy rationale
- ✓RAG and integration design grounded in your data sources
Phase 02
Build and Ground
Engineer the agent or LLM application, wire it into live workflows, and build the retrieval pipeline so responses are grounded and citable.
- ✓Working agent integrated into real systems, not a standalone demo
- ✓Grounded retrieval pipeline with reranking and citations
Phase 03
Evaluate and Guardrail
Stand up the evaluation harness and tracing, then convert offline evals into runtime guardrails that intercept unsafe or off-policy outputs.
- ✓Evaluation harness with LLM-as-judge and a labelled dataset
- ✓Runtime guardrails and tracing wired into the request path
Phase 04
Production Launch and LLMOps
Launch with monitoring, cost controls, and the observe, evaluate, guardrail, improve loop running, then hand over an operable system.
- ✓Production-deployed system with monitoring and cost attribution
- ✓LLMOps runbook and a measurement baseline for ongoing improvement
Technologies we work with
Battle-tested tools across the modern cloud-native stack
Agents and RAG
Evaluation and Observability
Guardrails and Safety
FAQ
Explore More
More of the AI catalog
AI services that work together across Engineering, In-Product, and Business Operations. Pick what fits your next move.
Let's Talk
Get Your AI Agent From Pilot to Production
Book a conversation about the agent or LLM application you want to ship. I will give you an honest read on what it takes to make it reliable, safe, and observable in production.
Based in Düsseldorf, Germany, working with clients across Europe