Agents with approvals & logs
Custom agentic workflows with human-in-the-loop, audit log, and escalation, embedded in your APIs, identity, and data stores. No shadow IT on subscriptions.
AI · Agents · Data · Secure delivery
AI in your organization, not AI in a browser tab.
ChatGPT on a laptop is not an AI strategy.
We build orchestrated agent workflows that fit your systems: with approvals, logs, and tests at every interface.
Hosting where you need it: on-prem, Microsoft, Google, or hybrid.
AI tooling
- Claude
- OpenAI
- Cursor
- Ollama
- OpenClaw
Get data without GDPR stress
Structured extraction and enrichment with source rights, retention, and quality filters. You get data that holds up in reviews, not just in a notebook.
Locally hostable when it matters
EU hosting, VPC, or on-prem for sensitive data that cannot use a public model endpoint. Open weights where it makes sense; frontier models where it does not.
Application areas
Where AI creates immediate leverage for you
Two fields where companies save real effort and gain quality with AI today: scalable content with clear guardrails and automated processes that reliably take over repeatable work.
Content production
Scalable content with quality gates and brand guardrails: research, draft, review, and publish as a pipeline, not ChatGPT copy-paste. With sources, versions, and consistent voice.
Process optimization
Relieve repetitive workflows in sales, operations, and back office with agents and automation: faster, documented, with clear approvals instead of shadow IT. You gain time back without giving up control.
Orchestration · Agency · Integration
More than a chat: agents that collaborate and fit in.
In production environments, coordination counts: multiple specialized agents or sub-workflows (planning, execution, review), shared policies, tool access by permission, and traceable handoffs. The orchestration layer decides who uses which model or API when, bound to identity, logs, and approvals. The same applies to on-prem installs, Microsoft 365/Azure estates, and Google Cloud; the architecture stays maintainable, only the integration changes.
- Agentic routing: break down tasks, bind tools, escalate to humans
- Unified interfaces to data, APIs, and identity, without shadow AI in individual subscriptions
- Operations and traceability: the same story for review and rollout, regardless of hyperscaler or private cloud
From distributed tools to one outcome
Architecture
What AI orchestration really needs
What we clarify before the first agent. Specialized agents, clear handoffs, a shared policy and tool layer: without this foundation, agentic AI quickly becomes a collection of unmaintainable scripts. We define roles, escalations, and interfaces before code exists so extension stays possible and nothing depends on individuals.
Orchestration
Orchestration and knowledge layer
More than a coding tool. Coding agents like Claude Code are strong tools, but session-bound and without institutional memory. Above them sits a continuous layer: knowledge graph with requirements, decisions, and implication chains, synchronization of parallel agents, revision-safe audit log, and connections to deployment, triggers, and communication channels. That turns "the AI did it that way" into a traceable, maintainable development process.
Hosting model is secondary; governance is not.
We plan AI where your data and identities already live: data center, VPC, Microsoft tenant, or Google project. The questions stay the same: who may do what? Where are the logs? What does an approval path look like?
- On-prem & private cloud
- Azure / Microsoft 365
- Google Cloud
- Hybrid & VPC
- EU region & data residency
Scale & automation
Scale without breaking the pipeline.
AI pipelines get expensive and risky when logs, drift, and data flows are not designed in. We automate exactly where errors hurt, with observability (e.g. Sentry), reproducible deployments, and evals that catch hallucinations before customers do.
Delivery layers
- 01
Sources & rights
Clarify domains, APIs, crawling boundaries, and retention before code flows; privacy and procurement are at the table from day one.
- 02
Extraction & quality
Structured schemas, validation, and drift monitoring. Outliers are reported, not silently washed into the index.
- 03
Orchestration & approvals
Human-in-the-loop, clear escalation, and traceable logs for operations and review, still reproducible weeks after an incident.
- 04
Tests, evals & rollout
Interface tests, AI evals against hallucinations, and load paths on critical queries, from staging to production, reproducibly.
Architecture, not a stock image
What a production multi-agent looks like for us.
Roles, tools, policies, and eval thresholds live in code, versioned and extensible. No single-person memory, no "we no longer know what the AI is doing".
$
agents:
- id: planner
role: breaks tasks into sub-goals
model: claude-opus-4.7
- id: executor
role: calls tools, writes to systems
tools: [crm, jira, slack]
policy: tools-allowlist
- id: reviewer
role: validates against eval-set
block_on_fail: true
human_in_loop: required
audit_log: enabled
Roles, tools, and eval thresholds live versioned in the repo, not in individual developers' heads.
Deployed in your environment, not handed to the model vendor.
Your AI logic runs where your data lives, not in a SaaS box whose terms may change tomorrow. We map roles, approvals, and escalation so legal, IT, and business units pull in the same direction.
- End-to-end automation with measurable SLAs
- Live data pipelines + drift monitoring instead of silent models
- Eval and test loops for every AI interface
- Load and regression tests for critical data paths
Workshops & training
So your development does not stay external at every step
Hands-on on prompting, agent design, RAG patterns, evals, and operations: formats and training are covered in detail under consulting & workshops. Here the focus is productive systems in production.
Consulting & AI law
One language with legal and engineering
We translate risk classes and technical obligations together with your legal team into backlog items that developers and reviewers understand equally well.
FAQ
Common questions about AI in the enterprise
Do you build only chatbots or full agent systems?
Both, depending on the use case. The focus is orchestrated workflows with approvals, logs, and tests, embedded in your APIs, identity, and data stores.
Does AI have to run in the cloud?
No. We plan where your data and identities live: on-prem, VPC, Microsoft tenant, or Google project. Governance stays the same; only the integration changes.
How is this different from consulting and workshops?
AI solutions deliver productive architecture and implementation. Consulting enables your team with formats, playbooks, and training. Both complement each other but are positioned separately.
Bring AI in-house without giving up control.
In an intro call we clarify use case, data situation, and compliance frame. You then get agents and workflows that fit your stack, teams that can operate them safely, and data that makes answers verifiable.
Joel Burghardt
Managing director
Sven Hoffmann
Client advisor & senior developer
