Custom AI Agent Development for Production

Workflow automation, knowledge retrieval (RAG), and decision-support agents. Each is built around your data access, business rules, review path, and escalation rules, not a demo that works once.

A chatbot answers inside a conversation. An agent connects data, tools, and decisions into multi-step work. The real difference is the boundary: what it can do, what it must cite, and when it hands off to a person.

Cost tracks scope: how many workflows, how much data access, how many integrations, and how strict the compliance is. Every engagement starts with a scoping session that puts a real number and timeline on the table before any build.

A focused agent usually ships in a few weeks. Larger platforms run as weekly sprints with a working build every Friday, so you see progress against the plan instead of guessing.

Yes. CRMs, ERPs, databases, internal APIs, communication platforms. Every connection gets retry logic, rate limits, structured logging, and a handoff path your team can operate.

Yes. .NET and C# on the backend, Microsoft Orleans for stateful load, and the Microsoft Agent Framework for orchestration. It is the same stack we built AIBase and Prostir on.

Azure OpenAI, OpenAI, Claude, Mistral, and Gemini. We pick the model per task and keep the design model-agnostic, so swapping providers later does not mean a rewrite.

Retrieval-augmented generation connects a model to your approved knowledge so answers come from your sources, with citations. You need it whenever an agent must be right about your data, not just fluent.

MCP is a standard way to give an agent tools and data through a controlled gateway. We build MCP backends ourselves in Prostir, so agents reach your systems through one auditable layer instead of scattered custom glue.

Yes. We deploy into your Azure, AWS, or Google Cloud tenant, and can keep data and model calls inside your boundary for regulated or private workloads.

Security is part of the architecture from the first sprint, not an audit at the end. We define where data lives, which providers can process it, what gets logged, and which controls apply. Regulated work gets explicit guardrails before build starts.

Guardrails catch known failure modes, confidence signals flag the uncertain ones, and an escalation path routes the rest to a person. Every answer is traceable, so you can see why the agent did what it did.

We test against representative inputs, ambiguous cases, and the exceptions a polished demo hides. MCAF keeps automated tests, review, and security checks tied to the code so results stay stable.

Regression tests and evaluation runs catch behavior drift, so a new model version or data source has to pass the same checks before it ships. Monitoring flags changes in production, not weeks later.

No. Agents remove repeatable work like extraction, routing, scoring, and status updates. Your team keeps exceptions, relationships, final decisions, and accountability. The goal is throughput, not blind replacement.

Yes. A focused prototype tests one workflow against real inputs first. You evaluate performance, edge cases, and operational fit before deciding whether the full production scope is worth it.

No. You provide the business context: which workflow matters, what rules apply, and what success looks like. We translate that into an agent design and explain the technical choices in plain language.

Support covers monitoring, tuning, issue resolution, and documentation. After that you either scope the next phase with us or take over with the code, decisions, and operating notes already transferred.

You own 100% of the code and the IP. We sign the assignment on day one, hand over the repository, and document the architecture so whoever takes it next can actually use it.

We are based in France, so data residency, GDPR, and EU working hours are the default, not an add-on. For teams that cannot ship a black box, that matters before the first line of code.