AI implementation · assistants, agents, agentic workflows
Put AI to work where the work is.
Most AI initiatives stall between a promising demo and a process nobody changed. We implement AI in the operations themselves: one workflow, the people who run it, the data it needs, the model that fits, and the rules that keep humans in charge.
From assistant to agent to agentic workflow, from an agent platform to a custom MCP agent, from frontier models to Mistral or open-source models on your own infrastructure: we choose with you, build it into your tools and make sure your team can operate it.
Who this is for
- Executives who want results, not a pilot
- You have seen the demos. You want AI in production on a process that matters, with a measure agreed before we start and a team able to run it after.
- Operations teams drowning in repetitive work
- Follow-ups, reconciliations, document handling, first-level answers: the tasks are clear, the volume is real. An assistant or an agent connected to your tools can take the load, under rules you define.
- Companies with confidentiality or sovereignty constraints
- Regulated data, European hosting, no training on your content: we implement with European models such as Mistral, or open-source models deployed on your cloud or on-premise, when the frontier providers do not fit.
How we implement AI
Four decisions shape every implementation: what kind of AI the workflow needs, which platform or build serves it, which model runs it, and which data it can rely on. We take them in that order, with your team.
01
Assistant, agent or agentic workflow?
An assistant helps one person draft, search and summarise. An agent takes actions in your systems under a mandate: creates a record, sends a follow-up, prepares a document. An agentic workflow chains several of them with checks between steps. We start with the smallest form that removes a real pain point, and grow from there.
02
Platform or custom build, and which model
Off-the-shelf agent platforms such as Dust, Lindy, Hyperagent or Grok bots are a fast start when the workflow fits their connectors and permissions. When the process is specific, we build custom agents connected to your systems through MCP and coding agents. The model follows the task: Claude, GPT or Gemini, Mistral as the European option, or open-source models deployed and adapted on your own infrastructure when confidentiality or cost requires it. Reversibility is a criterion from the first day.
03
Data structured from the work, not from a data lake
We do not start with a data lake. We start with the operational pain: a follow-up nobody owns, a reconciliation done by hand, an exception that always lands on the same desk. We structure the data that this pain needs, connect it to the tools people already use and give the assistant or agent a clean, permissioned view of it. Governance, review points and a measure of use come with the first release.
What you get
- AI in production on a real process, with the people who run it trained during the build.
- A clear mandate for every assistant or agent: what it may read, what it may do, where a human reviews, how it is logged.
- The right platform and model for your constraints: agent platforms, custom MCP agents, frontier models, Mistral or self-hosted open-source models, with a documented path to change them.
- Data your business can trust: structured from operational needs, connected to existing tools, owned by you.
- Skills that stay: executives and teams learn on their own use cases; Ownward remains available for the next level of complexity.
Questions we hear before we start
What is the difference between an assistant, an agent and an agentic workflow?
An assistant answers and drafts for a person. An agent acts in your systems within a mandate you define. An agentic workflow chains several agents and checks to complete a process end to end. Most companies should start with an assistant on a real task, then move to agents once data and permissions are ready.
Should we use an agent platform or build our own?
Use a platform such as Dust, Lindy, Hyperagent or Grok bots when its connectors, permissions and pricing fit the workflow: you get results in days. Build custom, with MCP and coding agents, when the process is specific, the data is sensitive or the platform would lock you in. We often combine both.
Can we run AI without sending data to US providers?
Yes. Mistral offers European models through its API or deployed on your infrastructure, and open-source models can run on sovereign European cloud providers or on-premise. The right choice depends on the task, the volume and your policy; we set it up and document how to switch.
Our data is not ready. Do we have to clean everything first?
No. Waiting for a perfect data lake is the most common way to never start. We structure the data that one operational workflow needs, prove the value, then extend. Each step leaves cleaner, better-owned data behind it.
What about MCP?
The Model Context Protocol lets an assistant or agent connect to your systems, CRM, ERP, documents, through a scoped, permissioned interface. We use it to give AI a safe view of your data instead of copying it around, and to keep the connection independent of any single model vendor.
How do you measure success?
Before we start, we agree on what changes: time saved on a task, a queue that stops growing, an error rate, a response time. We measure it against the starting point and publish it to the team. No adoption theatre.
Related
Which task would you hand to an assistant tomorrow?
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