AI agents and RAG systems that hold up in production.
From document processing to workflow agents — designed for your data, shipped with guardrails, evaluation, and human oversight.
Most AI pilots never leave the demo.
A prototype that impresses on ten sample documents is easy. One that handles your real volumes, cites its sources, stays inside cost bounds, and knows when to hand off to a human — that is an engineering project. It is the difference between a demo and a system, and it is exactly what we build.
What we build
Custom AI Agents
Autonomous agents that execute multi-step workflows: data extraction, document processing, customer support triage, code generation, and decision support — with guardrails, observability, and human-in-the-loop controls.
LLM Integration
Connect foundation models (OpenAI, Anthropic, open-source) to your proprietary data. We build RAG pipelines, fine-tuning workflows, and prompt engineering systems that deliver accurate, domain-specific results.
Intelligent Automation
Replace manual, repetitive processes with AI-powered automation — invoice processing, report generation, data classification, anomaly detection — with confidence scoring and exception handling.
AI-Enhanced Analytics
Transform raw data into actionable insights with natural language querying, automated report generation, and predictive analytics dashboards.
AI that works in the real world
The gap between AI demos and production AI is enormous. We bridge it.
Mibrex builds custom AI agents, LLM integrations, and intelligent automation that plug directly into your existing business workflows — not standalone toys, but production systems that handle real data at scale.
Our AI approach
1. Understand the workflow
We don’t start with technology — we start with your process. What’s manual? What’s slow? Where do errors happen? AI should solve real problems, not create new ones.
2. Choose the right tool
Not every problem needs a large language model. We select the right AI approach — rule-based, classical ML, or LLM — based on accuracy requirements, latency constraints, and cost.
3. Build for production
Every AI system we deploy includes observability (token usage, latency, accuracy), guardrails (input validation, output filtering, hallucination detection), fallbacks, and human-in-the-loop escalation paths.
4. Integrate, don’t isolate
AI features are built into your existing application using our Accelerator Modules — not deployed as separate tools your team has to context-switch to use.
Technology Stack
| Layer | Technology |
|---|---|
| AI/ML | Python, LangChain, LlamaIndex, Hugging Face |
| LLMs | OpenAI, Anthropic, Mistral, open-source models |
| Vector DBs | Pinecone, Weaviate, pgvector |
| Backend | Django REST Framework, Celery, Redis |
| Infrastructure | Kubernetes, Docker, GPU instances (AWS/GCP) |
| Monitoring | LangSmith, custom dashboards, Prometheus |
Why Mibrex for AI
We build AI the way we use it.
We use AI every day
Our entire development process is AI-augmented. Our engineers work with AI coding agents daily — we understand AI capabilities and limitations from hands-on experience, not just theory.
Enterprise-grade architecture
AI prototypes are easy. Production AI systems that handle sensitive data, scale under load, and fail gracefully are hard — that’s where 15 years of enterprise software experience makes the difference.
Security first
Our ISO 27001 certification means your data — including the data flowing through AI pipelines — is handled with auditable security controls.
Ideal for — is this the right AI engagement?
AI Transformation is right for you if…
Built for teams with real workflows, not curiosity projects.
— You need to automate document processing, customer support, or internal workflows
— You are a SaaS company adding AI features to an existing product
— You tried AI tools but couldn’t get them to production quality
What to expect from the build
Engineered AI — with the discipline that implies.
— Agents amplify your team — judgment stays with people
— The right tool, not always an LLM — sometimes rules and ML win
— Accuracy improves with feedback — it starts good, not perfect
Frequently asked questions
Do you use LLMs for everything?
No. We select the right approach — rule-based, classical ML, or LLM — based on accuracy requirements, latency constraints, and cost. Sometimes simple automation wins.
How do you prevent hallucinations?
RAG pipelines ground answers in your documents with citations; evaluations measure accuracy continuously; and confidence scoring routes uncertain results to human review.
Can AI integrate with our existing systems?
Yes — that is our default mode. AI features are built into your existing application using our Accelerator Modules, not deployed as standalone tools.
Put AI to work on a real workflow.
Describe the process — a senior engineer will tell you within 24 hours whether agents, RAG, or something simpler fits it best.