An AI-native business embeds AI into every layer as a full participant, not just a tool. AIBase makes this possible by providing a platform to build, deploy, and manage AI agents that handle real work—like answering customers, automating documents, and running workflows across your systems.
We built AIBase to remove the need for deep engineering or vendor lock-in, so even small teams can launch AI solutions without rebuilding infrastructure each time.
Key capabilities
Let you define multi-step AI processes visually, with branching logic and human-in-the-loop approval gates alongside automatic retry on failure. One legal firm uses agent workflows to route document review through AI classification, human verification, and client notification. Nobody manually tracks the status.
Connects AI agents to your existing processes. AIBase adds AI to the steps your team already follows. Adoption happens step by step, not as a forced replacement.
Spans more than 20 channels - Telegram, WhatsApp, Slack, Email, SMS, web chat, and more - through a single agent interface. Your AI agent responds on whatever channel the customer prefers. Context and conversation history stay consistent across all channels.
With agentic RAG lets AI agents access, reason over, and cite your documents, knowledge bases, and data sources. Every answer links back to the exact document and section it came from.
Lets you build teams of specialized AI agents that hand tasks to each other. A support agent routes technical questions to a specialist, escalates billing disputes to a billing agent, and sends complaints to a human manager. You define the delegation rules.
Filter and validate everything that enters the AI pipeline. They block prompt injection attempts, enforce data format rules, and keep sensitive information within compliance boundaries before any agent processes it.
DotPilot is a local-first desktop application that runs AI agents on your own machine. It is built on Microsoft Orleans and the Microsoft Agent Framework, giving you a single control plane for managing multiple AI agents while sensitive data stays on your machine.
Key capabilities
It runs AI agents on your own machine. It is built on Microsoft Orleans and the Microsoft Agent Framework. It gives you a single control plane for managing multiple AI agents, coordinating their workflows, and connecting them to cloud AI services while sensitive data stays on your machine.
Your code, documents, and project context never leave your machine unless you explicitly route them to a cloud provider. DotPilot includes adapters for Codex, Claude Code, GitHub Copilot, and local runtimes. You pick whichever AI service fits the task without switching between five different tools.
DotPilot replaces manual copy-paste coordination with an automated pipeline. Agents hand off tasks, share context, and report results through one desktop interface.
DotPilot replaces manual copy-paste coordination with an automated pipeline. Agents hand off tasks, share context, and report results through one desktop interface.
Key capabilities
It makes AI-generated code safe for production. It enforces repository-resident knowledge, layered verification, and explicit quality gates on every commit. Without this discipline, AI coding assistants produce output that passes basic tests but fails under real-world edge cases - piling up technical debt that appears weeks after launch.
It defines what the AI agent knows about the project - architecture rules, coding standards, test requirements. It verifies every output through automated gates and keeps a persistent knowledge layer so context is not lost between sessions.
Every product on this page was built with it. The framework is fully open source because sharing the methodology builds more trust than keeping it proprietary.
PrompterOne is a browser-based teleprompter built with .NET and Blazor, covering scripting, rehearsal, and live recording. It launched in seven days, runs fully client-side, and keeps all scripts on the user’s device.
We also created TPS, a Markdown-based format for teleprompter scripts with timing, emotion cues, and multi-speaker support, plus an SDK for major platforms.
Key capabilities
Understands TPS markup natively. It gives presenters a structured authoring environment where delivery cues like pacing, emphasis, and speaker transitions are first-class elements, not freeform annotations bolted onto plain text.
Uses Rapid Serial Visual Presentation to display words one at a time. It applies an ORP (Optimal Recognition Point) focal point, phrase-aware pauses from TPS markup, and adjustable speed controls. Presenters practice pacing and delivery cues before going live, without the full teleprompter display.
Renders scrolling script text with mirror toggles for beam-splitter setups and fullscreen mode for dedicated prompter monitors. Camera background overlays let presenters see themselves while reading. That capability normally requires dedicated hardware like the Elgato Prompter.
Provides local recording through MediaRecorder, WebRTC transport via VDO.Ninja and LiveKit for live streaming, Web Audio processing, and Canvas-based overlay compositing. All of it runs inside the browser with no plugins or desktop software, using MediaDevices, WebRTC, MediaRecorder, Web Audio, and Canvas APIs through Blazor's JavaScript interop layer.
A complete, working production app that anyone can inspect, built under real time pressure using the same AI native workflow we apply to client projects. The seven-day timeline is documented in the commit history.
Code quality, architecture decisions, and test coverage are all visible in the repository. If you want to judge whether our AI native methodology produces shippable software, PrompterOne is the most direct evidence we have
Beyond the main products, we maintain a set of specialized tools that support specific capabilities across our projects:
Building AIBase, DotPilot, and MCAF means we hit the hard problems of AI-native development before our clients do. Every bug we fix in our own platform, every workflow we improve in DotPilot, every quality gate we add to MCAF—all of it lands in client delivery the same week because the tools are identical.
Vendors who only build for others can always blame the client when things go wrong. We build for ourselves first. That means our tools either work in production or our own operations break. The gap between what we sell and what we use closes completely.
AIBase is in active development and available for early access at [aibase.fr](https://aibase.fr). If you want to deploy AI agents for your business, contact us. We will walk you through the platform and discuss whether it fits your use case.
DotPilot and MCAF are both open source and available on GitHub. You can use them freely in your own development workflow. If you need help setting them up or want customized versions for your team, our dev teams can handle that as a service engagement.
Every improvement we make to AIBase, DotPilot, or MCAF directly benefits client projects. We use the same tools on every engagement. A performance fix found while running AIBase in production gets applied to your project in the same sprint. That feedback loop is why a fix found on Monday lands in a client project by Friday.
Book a 15-minute call and we'll show you how AIBase, MCAF, and DotPilot work together to deliver first production versions in 4–6 weeks.
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