Industries / Travel
AI for travel support, around the clock.
Booking and support answers grounded in live inventory, so travellers get the right answer at 3am and do not have to ask twice.
What we built for travel. An AI chatbot for a travel platform.
Live inventory answers
Booking and availability answers drawn from live inventory data, not stale copies.
24/7 support
Coverage that does not depend on staffing hours.
Booking assistance
Help with booking questions and support requests in one conversation.
What else we can build for travel. Capabilities we offer. We have not delivered these in this industry yet.
Voice agents
Phone support for bookings and changes, with a handover to staff.
Disruption updates
Proactive messages when plans change, with rebooking options.
Itinerary assistants
Trip details, changes and recommendations in one place.
Agent assist
Suggested answers and booking context for your support team.
Match the autonomy to the task. Routine questions can be answered directly. Exceptions go to people.
- L1WorkflowA fixed, auditable path.
- L2AssistantAI drafts. A person reviews.
- L3Supervised agentAI acts and flags exceptions.
- L4AutonomousRoutine, low-risk, monitored.
- Human-ledPeople decideAI supports the analysis.
Availability and booking questions
Answered from live inventory, with anything unusual flagged.
L3 Supervised agentChanges and refunds
AI prepares the options. Staff approve.
L2 AssistantComplaints and special cases
Handled by people, with the conversation context attached.
Human-led
Our travel work, and related systems. Each result comes from a delivered engagement.
Customer support assistant
A support assistant grounded in live inventory data, available around the clock.
~35% fewer repeat contacts AI · HealthcareMulti-agent voice AI
Live voice agents for scheduling, verification and follow-up calls, escalating to a person when needed.
~50% fewer manual handling cases AI · Agent platformsAI agent platform
Agents retrieve, reason and act across systems, with human approval before high-stakes actions.
~40% less manual effortDo your support requests spike when your team is offline?
The technology behind our AI systems. The right stack depends on the model, the data, the architecture and where it runs.
Generative AI and large language models
Models, chosen per task
- OpenAI
- Anthropic Claude
- Google Gemini
- Meta Llama
- Mistral AI
- Cohere
- Hugging Face
- OpenRouter
- LangChain
- LlamaIndex
AI agents and orchestration
Tools, steps and handover
- LangGraph
- AutoGen
- CrewAI
- Semantic Kernel
- Agentic workflows
- Tool calling
- Function calling
- Multi-agent systems
Vector databases and semantic search
Grounded answers from your content
- Pinecone
- Weaviate
- Milvus
- Qdrant
- Chroma
- pgvector
- Elasticsearch vector search
AI integration and APIs
Connecting to your systems
- REST APIs
- GraphQL
- Webhooks
- Microservices
- API gateways
- SDK integrations
- Third-party AI APIs
Cloud and AI infrastructure
Where it runs
- AWS
- Microsoft Azure
- Google Cloud
- Amazon SageMaker
- Azure Machine Learning
- Google Vertex AI
- Databricks
AI evaluation and observability
Quality and cost, measured
- Model evaluation
- Prompt testing
- Hallucination detection
- Latency monitoring
- Cost tracking
- Output quality assessment
- AI observability
- Langfuse
Show the full stack 12 more layersShow fewer layers
Programming languages
What the code is written in
- Python
- Java
- JavaScript
- TypeScript
- C++
- R
- Go
- SQL
- Swift
- Kotlin
AI and machine learning frameworks
Training and running models
- TensorFlow
- PyTorch
- Scikit-learn
- Keras
- XGBoost
- LightGBM
- JAX
- Hugging Face Transformers
Natural language processing
Understanding text
- Text classification
- Sentiment analysis
- Named entity recognition
- Text summarization
- Semantic search
- Question answering
- Embeddings
Computer vision
Understanding images and video
- OpenCV
- YOLO
- Detectron2
- Image classification
- Object detection
- OCR
- Image segmentation
- Facial recognition
- Video analytics
Speech and voice AI
Listening and speaking
- Speech-to-text
- Text-to-speech
- Voice recognition
- Voice assistants
- Conversational voice AI
- Audio processing
Data engineering and processing
Moving and preparing data
- Apache Spark
- Pandas
- NumPy
- Apache Kafka
- Apache Airflow
- Databricks
- dbt
Databases and data storage
Where the data lives
- PostgreSQL
- MySQL
- MongoDB
- Redis
- Microsoft SQL Server
- Amazon DynamoDB
- Elasticsearch
- Snowflake
Containers and orchestration
Deploying and scaling
- Docker
- Kubernetes
- Amazon EKS
- Azure Kubernetes Service
- Google Kubernetes Engine
- Terraform
MLOps and model operations
Keeping models healthy
- MLflow
- Kubeflow
- DVC
- Model versioning
- Model monitoring
- CI/CD pipelines
- Automated retraining
- Performance tracking
Backend development
Services and business logic
- Node.js
- Django
- FastAPI
- Flask
- Spring Boot
- .NET
- Express.js
Frontend and application development
What people use
- React
- Next.js
- Angular
- Vue.js
- React Native
- Flutter
- Android
- iOS
Development and collaboration tools
How we ship
- Git
- GitHub
- GitLab
- Bitbucket
- Jenkins
- Jira
- Postman
Used in our delivered platforms
From first use case to production. Four stages, with a working version reviewed at each one.
-
Discovery
Map the support journeys, the systems and data involved, and what good looks like.
Discovery notes
- The questions customers ask
- Your inventory and booking systems
- When to hand over to staff
-
Scope & architecture
Agree the architecture, success measures and delivery milestones.
Solution design
- Assistant architecture
- Handover rules
- Success measures
-
Build
Build against real data, with a working version reviewed at each milestone.
Working build
- Working assistant on live data
- Team review
- Accuracy tests
-
Ship & support
Roll out to production with monitoring and a defined support window.
Production rollout
- Production rollout
- Monitoring
- Support window
Why Avinya for travel. We have put a travel assistant on live inventory.
Delivered, not theoretical
The work on this page is in production, with results from the engagement.
People stay in control
Human review and approval checkpoints are part of the design.
Traceable by design
Sources, audit trails and monitoring planned from the start.
We stay after launch
Monitoring, tuning and a defined support window.
Engagement models. Pick the shape that fits where you are.
Dedicated team
A dedicated team that works as an extension of yours, from first release to scale.
Project-based
A defined scope, milestones and delivery date for a specific product or workflow.
Consulting & advisory
Architecture and readiness advice before you commit to a build.
What our clients say.
“Avinya Labs built our AI travel chatbot, enabling real-time support, smart travel recommendations, and seamless booking assistance. A highly capable team that delivers ahead of the curve.”
“Avinya Labs built our full-stack AI solution … Their expertise accelerated our launch and significantly reduced execution risk.”
“We needed a platform that could handle complex construction workflows without breaking down every week. Avinya Labs delivered exactly that.”
AI for travel FAQs.
Where do the answers come from?
From live inventory data, so availability answers stay current. That cut repeat contacts by about 35% in our delivered engagement.
Does it work outside office hours?
Yes. It covers support 24/7, independent of staffing.
What happens with complex requests?
They go to your team, with the conversation context attached.
Where should we start?
With one task where the data exists and the risk is contained. We map it, agree what good looks like, and build a working version you can review before anything scales.
How do you handle our data?
Access is scoped to what the system needs, with role-based permissions and audit trails where records change. We agree data handling during discovery.
Ready to turn your vision into reality?
Tell us the problem, the data, and where manual effort sits today. We'll map the path from MVP to scale.
Or email contact@avinyalabs.co