Services / AI Development
AI development that ships to production.
AI development services that ship to production: agents, document intelligence, voice AI and automation, built into the systems you already run and measured against real workflows.
What AI development solves. Where AI earns its place in a business, and where most projects get stuck.
- 01
Too much work is still manual. Reading documents, copying data between systems and answering the same questions take hours every week.
- 02
The AI prototype never reached production. A demo worked, but it was never connected to real systems, tested on real cases or monitored.
- 03
Work crosses too many systems. One task means switching between CRM, ERP, inboxes and spreadsheets, and things fall through the gaps.
- 04
Nobody can check what the AI did. Outputs arrive without sources, logs or a way to review them, so people stop trusting them.
- 05
Customers wait for answers. Support and booking questions queue up outside business hours and at peak times.
- 06
It is not clear where AI would pay off. There are plenty of ideas, but no first use case, owner or measure of success.
AI development services. From deciding what to build to running it in production.
01 · Decide
Decide what to build.
Find the use case worth building first, and prove it before a full build.
02 · Build
Build it into your operations.
Agents, document AI, voice and products, connected to the systems you already run.
03 · Run
Run it in production.
Monitoring, evals and support once it is live, from the team that built it.
How it works. A replay of the workflow from a delivered engagement.
- 1Party namep.1
- 2Reference numberp.1
- 3Effective date§1
- 4Total amount§2Needs reviewVerified
New contract in the inbox. Extract the key fields and flag anything you are unsure of.
New document received
Running structured extractionStructured extraction complete
Classifying documentClassification complete
Checking extraction confidenceLow-confidence field flagged
Routing exceptions to manual reviewOnly the exception goes to review
- 1Update recordCRM · 1 record · 0.8s
- 2Send notificationEmail · 2 recipients · 1.1s
- 3Release paymentPayments · over approval limit · 2.4s
Resolve this request across our systems. Check with me before any payment.
New task received
Retrieving data across systemsData retrieved across systems
Reasoning and proposing actionsActions proposed
Executing low-risk actionsLow-risk actions executed
Checking action riskHigh-stakes action sent for approval
Waiting for human approvalApproved, action executed
Running evals and logging traceEvals passed, trace logged
AI solutions we have shipped. Each result comes from a delivered engagement.
AI document intelligence
Extraction and classification that sends only the exceptions to people.
>80% less manual review AI · Agent platformsAI agent platform
Agents retrieve, reason and act across systems, with human approval before high-stakes actions.
~40% less manual effort 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 · LegalResearch & review assistant
Citation-grounded answers, each one traced back to its source document.
~50% faster review time AI · InsuranceFull-stack AI platform for insurance
Fragmented policy, claims and correspondence data connected into one structured workflow.
~2.5x faster processing AI · Workflow automationWorkflow automation platform
AI extraction feeding a deterministic rules engine, with consistency checks built in.
~70% faster document prepHave an AI idea you want to test against real data?
Industries we have delivered AI in. Each one links to the engagement behind it.
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 idea to production. Four stages, with a working version reviewed at each one.
-
Discovery
Map the workflow, the systems and data involved, and what good looks like.
Discovery notes
- The workflow to improve
- Systems and data involved
- How success is measured
-
Scope & architecture
Agree the architecture, success measures and delivery milestones.
Solution design
- Architecture and model choice
- Integration points
- Delivery milestones
-
Build
Build against real data, with a working version reviewed at each milestone.
Working build
- Built against real data
- Evals on representative cases
- Review at each milestone
-
Ship & support
Roll out to production with monitoring and a defined support window.
Production rollout
- Production deployment
- Monitoring
- Defined support window
Why Avinya for AI. Built for real operations, measured against real results.
Results you can check
Every number on this page links to the engagement it came from.
People stay in control
Approval checkpoints, escalation paths and exception review are designed in from day one.
Traceable by default
Citations, audit trails and logs, so outputs can be verified rather than trusted blindly.
Production, not demos
Built to run against real systems and real data, with monitoring once live.
Full-stack delivery
Data pipelines, models, product and integrations from one team.
AI and Web3 under one team
One studio for AI systems and, where it helps, on-chain infrastructure.
Results from our AI work. Each number links to the engagement it came from.
How to start. Three ways in, depending on how well defined the problem is.
AI readiness assessment
We review the workflow, data, systems and controls. You get a written report with the first use case and a roadmap.
1 week4–6 weeksAI proof of concept
One AI use case tested against your real data, with evals, so you know whether it pays off before a full build.
4–6 weeks30 minutesTechnical call
Talk the problem through with an engineer: the systems involved, the constraints and a realistic approach.
30 minutesEngagement 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.
“We needed a platform that could handle complex construction workflows without breaking down every week. Avinya Labs delivered exactly that. What stood out even more was their commitment after launch: when a critical issue came up, the team stayed up until midnight to make sure we weren't left hanging.”
“Avinya Labs built our full-stack AI solution … Their expertise accelerated our launch and significantly reduced execution risk.”
“Avinya Labs built our AI travel chatbot … A highly capable team that delivers ahead of the curve.”
AI development FAQs.
What kinds of AI systems do you build?
AI agents, document intelligence, voice AI, chatbots, workflow automation and complete AI SaaS products, usually connected into existing business systems.
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.
How long does a project take?
It depends on scope and integrations. We agree milestones up front and review a working version at each one.
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.
Can you work with our existing product and team?
Yes. We can add AI into an existing product, work alongside your engineers, or run the build end to end.