Industries / Supply chain
AI agents for supply chain, working together.
Specialised agents that monitor, coordinate and handle routine work across ERP, suppliers, carriers and warehouses. Your team owns the exceptions and the strategy.
What we can build for supply chain. Not one assistant, but specialised agents that hand work to each other.
Demand sensing and replenishment
Agents that spot demand changes, check stock across warehouses and propose replenishment.
Supplier risk monitoring
Continuous tracking of supplier performance, with risks and alternatives raised before they cause delays.
Purchase order exceptions
Contract mismatches, missing confirmations and late orders detected, with routine fixes handled automatically.
Disruption recovery
When shipments slip, agents compare routes and carriers and draft customer updates for approval.
Warehouse balancing
Stock rebalanced between sites, including expiry-sensitive inventory.
Production scheduling support
Schedules adjusted for demand, capacity, maintenance and material availability, for planners to approve.
Customer order updates
Accurate order promises, early delay notices, and triage of claims and returns.
Trade document intelligence
Data extracted from invoices, confirmations and shipping documents, with exceptions routed to people.
Built on your systems
Agents work across your ERP, supplier portals and carrier data, with an audit trail for every action.
Match the autonomy to the task. Routine work runs on its own. People own the exceptions and the strategy.
- 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.
Demand sensing and replenishment
Agents detect changes and propose orders. Planners approve.
L3 Supervised agentSupplier risk monitoring
Continuous monitoring, with risks and alternatives flagged to the team.
L3 Supervised agentRoutine purchase order exceptions
Low-risk, repeatable fixes handled automatically and monitored.
L4 AutonomousDisruption recovery and customer updates
Agents propose new routes and draft updates. The team approves.
L3 Supervised agentCompliant shipments
A fixed, auditable workflow for regulated goods, with agents tracking approvals.
L1 WorkflowL3 Supervised agentSourcing strategy and key suppliers
Strategic decisions stay with people, supported by the agents' analysis.
Human-led
Related work from other industries. We have not yet delivered a supply chain project. These engagements built the same kinds of systems.
AI agent platform
Agents retrieve, reason and act across systems, with human approval before high-stakes actions.
~40% less manual effort AI · Document intelligenceAI document intelligence
Extraction and classification that sends only the exceptions to people.
>80% less manual review AI · Workflow automationWorkflow automation platform
AI extraction feeding a deterministic rules engine, with consistency checks built in.
~70% faster document prepHave a supply chain process that needs constant manual follow-up?
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 process, the systems and data involved, and what good looks like.
Discovery notes
- The systems, partners and data involved
- Where manual follow-up sits today
- Approval rules
-
Scope & architecture
Agree the architecture, success measures and delivery milestones.
Solution design
- Agent roles and handoffs
- Approval checkpoints
- Success measures
-
Build
Build against real data, with a working version reviewed at each milestone.
Working build
- Working agents on real data
- Team review of every flow
- Exception tests
-
Ship & support
Roll out to production with monitoring and a defined support window.
Production rollout
- Production rollout
- Monitoring
- Support window
Why Avinya for supply chain. Agent systems that act across business systems, already in production elsewhere.
Agents across systems
Our agent platform retrieves, reasons and acts across business systems, with about 40% less manual effort in one engagement.
Human approval by design
A human checkpoint before any high-stakes action.
Exceptions, not everything
Our document systems route only the exceptions to people.
Honest about fit
We tell you which tasks need an agent and which need a simple workflow.
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 full-stack AI solution, delivering scalable infrastructure, optimized AI workflows, and a seamless user experience. 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.”
“Avinya Labs built our AI travel chatbot … A highly capable team that delivers ahead of the curve.”
AI for supply chain FAQs.
Have you delivered a supply chain project before?
Not yet. We have built the same kinds of systems in other industries: agent platforms that act across business systems, document intelligence and workflow automation. Those engagements are linked on this page.
Why several agents instead of one assistant?
Supply chain work spans planning, procurement, logistics and customer service. Specialised agents can each do one job well and hand work to each other, with people approving the decisions that matter.
Which systems can the agents work with?
ERP, supplier portals, carrier data and warehouse systems, depending on what you run. Integrations are agreed during discovery.
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.
Which AI models do you use?
We evaluate models against your real workflow and choose per task. The architecture is designed so models can change later without rebuilding the product.
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