CTOs and IT leaders
A prototype impressed everyone, then broke in production.
Evals, routing and guardrails so the system holds up under load, attack and change.
Production AI Engineering
Beyond RAG: AI that survives production
RAG is the baseline. Production AI is software engineering around a probabilistic model.
Most teams can build a RAG demo. Far fewer can tell whether a change made the system better or worse, route around a failing model, or stop a prompt injection before it reaches the core. We engineer the full system: agentic loops, evaluation, model optimization, LLMOps and guardrails, EU-hosted, with your code and no lock-in.
What separates a demo from a production system
A retrieval demo searches a folder of PDFs and returns whatever looks similar. A production AI system synchronizes graph, vector and SQL data with live APIs, routes each request through an adaptive loop, scores quality with automated evals on every deploy, and falls back to a cheaper model or another source when something fails. Production AI engineering is the discipline of building reliable software around an unreliable, expensive, non-deterministic component. That is the work we do.
- Automated evals that catch regressions before users do
- Guardrails against prompt injection and PII leaks
- Model gateways with routing, caching and fallback
- EU-hosted, model-neutral, your code, no lock-in
Demo builder vs production AI engineer
The distance between a prototype that looked good and a system that holds up under load, attack and change.
| Demo builder (RAG only) | Production AI engineer | |
|---|---|---|
| Data scope | Searches a folder of static text PDFs. | Synchronizes graph and vector stores, SQL tables and live SaaS APIs. |
| System flow | Prompt, search, answer. | Adaptive router, multi-agent loop, guardrail review. |
| Testing | Tried a few prompts and it looked good. | A CI suite of semantic test cases scored on every deploy. |
| Failure mode | Breaks silently or hallucinates freely. | Automated fallback to a cheaper model or a second source. |
RAG is table stakes. The columns on the right are where production reliability is won or lost.
Agents decide, act, observe
RAG is a linear pipeline. An agent runs a loop: plan a step, call a real tool, observe the result, and decide again, with state and a human gate on consequential actions.
State and memory carry context across steps. A human approval gate sits on consequential actions, and every tool call is bounded and audited.
The five pillars
What production AI engineering covers
Beyond prompts and retrieval, five disciplines turn a demo into a system you can run, trust and change.
Agentic AI and tool calling
Decision loops that call real tools, not a one-shot pipeline.
- Reliable JSON tool calls into real APIs
- Multi-agent roles, state and hand-offs
- Bounded, audited tool use
- No infinite loops or context drift
Evaluation and testing
Deterministic testing for non-deterministic systems. The biggest skill gap.
- Automated evals with Ragas or TruLens
- Faithfulness, answer relevance, context precision
- LLM-as-a-judge against ground truth
- CI gates on every change
Model optimization
When prompting and RAG cannot get the tone or domain logic right, change the model.
- LoRA and QLoRA fine-tuning
- Quantization for latency and cost
- Open models like Llama and Mistral
- Domain tone and behavior
LLMOps and production infra
Treat the model as a volatile, expensive backend service.
- Model gateways and routing (LiteLLM, Portkey)
- Semantic caching and fallback
- Structured output with Pydantic
- Guardrails for PII and prompt injection
Advanced context management
Structure information so the model always sees the right context.
- Programmatic prompt optimization (DSPy)
- Contextual retrieval
- Context-window budgeting
- Metadata-enriched chunks
The toolchain
The production AI stack we engineer with
Model-neutral and open by default. We pick the right tool per layer and hand it over as your code.
Orchestration
- LangGraph
- CrewAI
- Mastra
- Vercel AI SDK
Evaluation
- Ragas
- TruLens
- LangSmith
- promptfoo
Serving and ops
- LiteLLM
- Portkey
- vLLM
- Ray
Guardrails and structure
- Pydantic
- NeMo Guardrails
- Llama Guard
- DSPy
The production loop
Every change runs the same loop: build, evaluate, route, guard, observe, then feed what you learn back in.
Evaluation gates the deploy, the gateway handles routing, caching and fallback, guardrails screen inputs and outputs, and observability feeds the next iteration.
What production-grade means to you
The same system reads differently from each seat. Here is what production AI engineering delivers per role.
Enterprise and procurement
Security and audit need to know how the system fails, not just how it works.
Documented fallback paths, guardrails, audit logs and AVV and TOM readiness.
Startup CTOs and founders
You shipped fast and now quality and cost are drifting.
An eval harness and a model gateway that cut cost and stop regressions as you scale.
Agencies and partners
Your client needs production-grade AI under your brand.
Senior LLMOps and evaluation engineering, shipped white-label with the same discipline as our open-source work.
Public engineering you can inspect
Running on this site
LiveThe assistant on this site is an agentic, tool-using system we built and run in production, not a demo behind a login.
Vendure Data Hub
Open sourceA Vendure commerce plugin we built and published, public on GitHub. Two of our eleven engineered bundles are public.
View on GitHubPimcore Asset Pilot
Open sourceA Pimcore asset bundle we built and published, public on GitHub and inspectable end to end.
View on GitHubWhen this depth is overkill
- A one-off internal prototype that will never see real users or load.
- A simple single-prompt feature with no tools, retrieval or quality bar.
- A throwaway proof of concept where the goal is to learn, not to ship.
- A team that has not yet defined what a good answer looks like.
Questions teams ask about production AI
RAG is the baseline. Production also needs evaluation, guardrails, model routing, structured output and observability. We engineer the full system so it stays reliable as data, models and load change.
Prompting and RAG ground the model in current data. Fine-tuning (LoRA, QLoRA) changes its tone, style or domain logic. They solve different problems and often run together; we advise which fits each case rather than defaulting to the expensive one.
Evals are automated tests for non-deterministic systems. Frameworks like Ragas and TruLens score answers on faithfulness, relevance and context precision, often with a larger model as a judge, so a code change is measured, not guessed.
LLMOps is the infrastructure to run language models at scale: gateways for routing and fallback, semantic caching to cut cost, structured-output enforcement, guardrails for security, and observability. It treats the model as a volatile, expensive backend.
Guardrails wrap the model: input and output filters, PII masking, and policy checks intercept prompt injection and sensitive data before they reach or leave the core. Consequential actions also pass a human approval gate.
Often not. Prompting and RAG solve most cases. Fine-tuning earns its cost when you need a specific tone, domain behavior or a small, cheap open model running in your own environment. We make that call with you on evidence, not hype.
Explore the AI stack
Make your AI production-ready
Tell us where your AI prototype is today. We will map the evals, guardrails and infrastructure to take it to production.
Who you're working with
HRB 288224
Registered in Munich
15+
Years, founder-led
DE · EN · AR
Delivery languages
2
Open source on GitHub
EU
Data residency, Frankfurt
AVV/DPA
Ready to sign, Art. 28
Engagement levels
Oronts works with serious teams that need senior delivery, not low-cost outsourcing.
- Production Pilot
- from 25k EUR
- Custom software and AI projects
- from 50k EUR
- Ongoing technical retainers
- from 15k EUR/month
Exact pricing depends on scope, responsibility, delivery speed, team size, integrations, support expectations and production risk.