AI Workflows
Multi-step processes that need to run reliably across systems - with observability, error handling, and human review built in from the start.
A specialized AI Agency from Germany
Reliable AI, engineered into your software
We build production-grade AI workflows, agents, chatbots, and RAG systems for software companies and operations teams that need them to run reliably at scale.
What we build
Production-grade AI systems, designed to integrate deeply with the systems you already run.
AI Chatbots
Chatbots that connect to your real systems - CRMs, helpdesks, internal tools, product APIs - and behave predictably across channels.
AI Agents
Agents that plan, reason, and take real actions in real systems - with guardrails, audit trails, and human review where it matters.
RAG
RAG systems that retrieve reliably from real knowledge bases - with permission-aware retrieval, source-verifiable answers, and evaluation built in.
Sequential Processes
AI Workflows
Most processes that benefit from AI aren't a single call to a model. They span multiple systems, involve conditional logic, require oversight at specific points, and have to handle exceptions without breaking. The reliability of that kind of process is determined less by the model than by the engineering around it — the orchestration, the error handling, the evaluation, the monitoring.
We build workflow systems for three main settings:
- AI features inside software products. When a user-facing feature calls a model as part of its logic, every request has to behave predictably — traceable, recoverable, and observable down to the token level.
- High-volume batch processes. When AI is applied across thousands or millions of records, the system needs proper queueing, rate limits, retries, and cost controls to run stably over time.
- Cross-system business processes. When an existing workflow gets extended with AI-assisted steps, the AI layer has to slot into a deterministic pipeline cleanly — with clear boundaries between what's probabilistic and what isn't.
AI Chatbots
Conversational AI that understands your customers
Most chatbot projects don't fail because the model can't hold a conversation. They fail because the bot has to connect to real systems — customer records, ticketing, internal tools, knowledge bases — and the integration layer was never built properly. That layer is where most of the engineering work actually lives.
The chatbot systems we build usually fall into a few categories:
- Customer-facing assistants that do more than answer questions — creating tickets, updating records, triggering workflows, or escalating to humans when appropriate, with the permissions and logging those actions require.
- Internal assistants grounded in a team's own documentation and tools, with access controls applied at the retrieval layer so users only see what they're allowed to see.
- Multi-channel deployments across web, WhatsApp, Teams, Slack, and custom interfaces, where session and context state has to follow the user between channels without breaking.
AI Agents
Autonomous Intelligence
Agents that work well in a demo often behave unpredictably in production. They introduce failure modes that more traditional software doesn't have — partial actions, unexpected tool outputs, loops, cost runaway, subtle misinterpretation of intent. Making them dependable is mostly engineering work, and it has to be done deliberately.
We design agent systems around a few principles:
- Multi-step planning with explicit state. The agent decomposes a goal into steps, but every step is observable, recoverable, and logged. When something fails mid-process, the system knows where it was and what to do about it.
- Tools designed for agent use. Rather than exposing raw APIs and hoping for the best, the interfaces the agent can call are scoped, permissioned, and designed around the kinds of mistakes an agent is likely to make.
- Human review at the steps that matter. Not on every action — which defeats the point — but at checkpoints chosen based on the actual risk profile of the process, with review interfaces built around how the people using them actually work.
- Full audit trails by default. Every tool call, every decision, every outcome is traceable. That's what makes agent systems operationally defensible rather than black boxes.
RAG
Retrieval-Augmented Generation
RAG is often described as a simple pattern: embed documents, retrieve the relevant ones, pass them to the model. It works well on small, clean knowledge bases. It gets considerably harder as the knowledge base grows, the documents become more varied, the access rules matter, and users start checking whether the answers are correct.
We focus on the parts that most determine whether a RAG system holds up in practice:
- Retrieval that combines multiple signals. Semantic search alone is rarely enough at scale. The systems we build typically combine vector search with keyword search, metadata filters, and re-ranking — tuned against real queries, not synthetic benchmarks.
- Permission-aware retrieval. Access rules are enforced at the retrieval layer, not as a post-filter. A RAG system that can surface documents a user shouldn't see is a compliance problem, and retrofitting permissions later is painful.
- Answers that can be verified. Every response is grounded in specific source documents, with citations users can follow back. Trust in a RAG system is earned through this transparency, not asserted.
- Evaluation as infrastructure. Without a way to measure whether a retrieval change is actually an improvement, teams spend weeks tuning parameters and end up roughly where they started. We build the evaluation harness before the rest of the system depends on it.
Get In Touch
Tell us about your project
We'll respond within two working days, usually with a few questions and an honest first read on whether we're a good fit.
Write to us
info@bariton.aiCall us
+49 174 2833434Location
Bariton AI Engineering
Bertolt-Brecht-Strasse 7
45968 Gladbeck
Germany
Remote-first team