Financial Institution
500K
customers
On-premise
Result
AI & Automation · Rasa Chatbot Development
Rasa — open-source AI you own and control.
Rasa AI development services delivering custom NLP pipelines with Rasa, Rasa Pro implementation, and on-premise deployment for enterprises that need full control over their chatbot infrastructure, data, and conversation models.
Rasa AI development services
Enterprise Rasa chatbot solutions
CALM architecture development
Custom NLP pipelines with Rasa
On-premise infrastructure
Rasa Pro implementation partner
100%
On-premise deployment
Complete data sovereignty — nothing leaves your infrastructure.
50+
NLU components
Modular pipeline with 50+ pre-built and custom components.
95%+
NLU accuracy
Train custom intent models on your domain data.
500K+
Users served
Battle-tested in banking, healthcare, and government.
Why MetaDesign
Rasa certified development agency for on-premise conversational AI.
Hire Rasa developers — open-source conversational AI company delivering self-hosted, fully customisable solutions with zero vendor lock-in.
Teams shipping with MetaDesign
Selected work
Case studies.
Enterprise IT
40%
ticket deflection
Zero
external calls
OWN.
TRAIN.
DEPLOY.
CONTROL.
Banking Chatbot
Hire Rasa developers who build on-premise chatbots for account queries, transaction history, and fraud alerts — meeting banking data residency requirements.
Healthcare Assistant
HIPAA-compliant patient engagement bot with symptom triage and appointment booking — all data on-premise.
Government Services
Rasa AI development services for citizen-facing chatbots handling permit applications, tax queries, and service requests — deployed on government cloud.
Internal Helpdesk
IT support and HR chatbot running inside your firewall — zero external API calls.
Our Approach
Rasa Chatbot Development: Our Proven Process
01
Assess
Evaluate use case, data requirements, and infrastructure for Rasa deployment.
02
Design
Conversation flows, intent taxonomy, entity extraction, and response templates.
03
Train
Custom NLU model training with your domain data and iterative accuracy improvement.
04
Deploy
On-premise or private cloud deployment with Docker/Kubernetes.
05
Improve
Conversation-driven development using real user interactions to improve the model.
01
Assess
Evaluate use case, data requirements, and infrastructure for Rasa deployment.
02
Design
Conversation flows, intent taxonomy, entity extraction, and response templates.
03
Train
Custom NLU model training with your domain data and iterative accuracy improvement.
04
Deploy
On-premise or private cloud deployment with Docker/Kubernetes.
05
Improve
Conversation-driven development using real user interactions to improve the model.
Customer Value
Six places it pays back
in the first sprint.
Real outcomes our clients report within the first engagement cycle.
Faster time-to-market
Production-ready teams that ship from week one — no ramp-up lag.
Reduced technical risk
Architecture reviews, code audits, and security scans baked into every sprint.
Measurable velocity
Cycle time, PR throughput, and defect density tracked from day one.
Cost predictability
Fixed-price or capped T&M — no surprise invoices, ever.
Continuous improvement
Retros, post-mortems, and process refinement every sprint.
Knowledge transfer
Your team grows. Documentation, pair programming, and workshops included.
By the numbers
400+
Engineers worldwide
200+
Active clients
20yr
Pure-play software
94%
Client retention
Engagement models
Three ways to work with our Rasa Chatbot team.
Pick the shape that matches your risk and timeline. We will tell you which one is wrong for your situation — even if it’s the bigger one.
Rasa Chatbot Pod
A dedicated squad of senior engineers, tech leads, and QA delivering milestone-driven results with weekly demos and eval-gated merges.
- Dedicated engineering squad
- Weekly sprint demos
- Fixed monthly cadence
- Direct technical lead communication
Hire Rasa Chatbot Engineers
Senior specialized engineers embedded into your existing team within two weeks. Your stack, your tools, your standups.
- 2-week onboarding ramp
- Time-zone aligned collaboration
- Direct Git & Jira integration
- Flexible monthly contracts
Turnkey / MVP Build
Go from requirements to production-ready launch in 6–10 weeks with fixed scope, upfront pricing, and guaranteed delivery.
- Fixed-price & fixed-scope
- End-to-end architecture & build
- Complete source code ownership
- Post-launch warranty & support
FAQ
Asked first, every time.
Don't see yours here? Send us the question — a principal engineer will reply within 24 hours.
Our Rasa AI development services deploy Rasa on your infrastructure — no conversation data sent to third parties. Essential for regulated industries. You also get full control over NLU models, no per-message pricing, and no vendor lock-in.
Yes. As a Rasa Pro implementation partner, we deploy enterprise Rasa chatbot solutions used by Deutsche Telekom, Adobe, and major banks. With proper infrastructure (Kubernetes, Redis, PostgreSQL), it handles millions of conversations.
Yes. We build hybrid architectures using CALM architecture development — Rasa handles intent routing and conversation management while LLMs generate dynamic responses for open-ended queries.
Basic FAQ bot: 3-4 weeks. Complex multi-domain assistant with custom NLP pipelines: 6-10 weeks. Hire Rasa developers from MDS and we deliver iteratively with conversation-driven development.
CALM (Conversational AI with Language Models) Architecture merges traditional intent-based routing with LLM flexibility. This allows the bot to handle unpredictable user inputs gracefully while maintaining strict adherence to business logic.
A certified Rasa Pro implementation partner has proven expertise in deploying complex, scalable, and secure enterprise chatbots, ensuring you maximize ROI and avoid costly architectural mistakes.
Yes. One of Rasa's greatest strengths is data privacy. Our Rasa AI developers can deploy the entire conversational AI infrastructure strictly on-premise or within your secure private cloud.
We utilize Retrieval-Augmented Generation (RAG) alongside custom entity extraction models in Rasa to ensure the chatbot grounds its responses exclusively in your secure, proprietary enterprise knowledge base.
Maintenance involves continuously reviewing conversation logs, refining NLU training data, updating fallback actions, and retraining models to improve accuracy as user behavior evolves.
Yes, Rasa's contextual dialogue management allows the bot to remember past interactions, manage context switching, and execute complex, multi-step transactions seamlessly.
Deploy Rasa chatbots on your own infrastructure.
Tell us about your project. We'll come back with a plan, a timeline, and the right team — no obligations.