AI that does the work,
built to run in production.

We build AI agents that act autonomously toward a goal — inquiry handling, document processing, automation of routine work. We design where a human should check (human-in-the-loop) to fit your operations, so it runs safely even on the premise that accuracy is not perfect. We use AI-driven development and MCP daily in building and operating our own SaaS, and we bring that hands-on knowledge straight to your project.

• Mapping the workflow and scoping what to agentize
• Implementing tool execution and the autonomous loop
• Designing human-in-the-loop checkpoints
• Defining success criteria (accuracy, tolerance)
• Fallback and escalation on failure
• Execution logging and review

We connect to internal databases, groupware and SaaS via MCP (Model Context Protocol), creating a state where AI can handle them safely. Without rebuilding existing systems, and with the data AI may touch pared down to the minimum, we provide a connection layer with permissions and audit logs in place. MCP is a technology we use heavily in our own daily development, and because it is standardized, we can flexibly add or swap out what we connect to.

• Connecting to internal DBs / groupware / SaaS
• Least-privilege read/write permission design
• Recording and retaining audit logs
• A connection layer that avoids rebuilding
• API rate and timeout control
• Adding and swapping connected systems

We build a mechanism (RAG) in which AI answers by referencing internal documents and knowledge. Every answer cites its sources so staff can verify correctness on the spot, and it is embedded so mistakes can be caught. Document updates are reflected incrementally, and what can be referenced is controlled by access rights, keeping information that should not be shown from slipping in.

• Ingesting internal documents and knowledge
• Vector search and re-ranking
• Citing sources
• Reflecting updates (incremental indexing)
• Reference control by access rights
• Evaluating answer accuracy

We monitor accuracy and response time after rollout and keep up with updates to prompts, data and models. By keeping the build free of any lock-in to a specific LLM product, we preserve the ability to swap in a better model when one appears. We collect and review error cases, make cost and token usage visible, and use that to expand into the next task.

• Monitoring accuracy and response time
• Ongoing prompt and data improvement
• Model swapping (no lock-in by design)
• Visibility into cost and token usage
• Collecting and reviewing error cases
• Expanding to the next task

For internal help desks or customer inquiries, it drafts answers by referencing FAQs, past cases and manuals. If a person makes the final check before sending, you can adopt it safely even without perfect accuracy.

It searches across meeting notes, policies and specs, summarizes the key points, and presents them with source links. It cuts down on "I can't find where it was written" and shortens time spent searching.

It handles work that mixes rules and judgment — routing requests, data entry and transcription, drafting standard emails. We design it to escalate only the hard-to-judge cases to a person.

It flags omissions and inconsistencies in estimates, contracts and applications by matching them against a checklist. It mechanically runs a first-pass screen on checks that are easy to miss by eye alone.

It gathers figures from multiple systems and generates a draft of your recurring reports. It brings work that used to mean manual aggregation and formatting every time down to a review-and-tweak.

It cleans up meeting transcripts and extracts decisions and to-dos. It can pick up owners and deadlines too, so it is useful for post-meeting sharing and preventing things from slipping through.