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.
Astra Short on time? Ask about this page in plain words.
Sound familiar? Four problems we are usually called in to fix.
Your AI pilot never left the demo
It worked on sample data, then stalled on real systems, real edge cases and real users.
- Built against your real data and systems
- Evals on representative cases before launch
- Monitoring once it is live
Your team still rekeys documents by hand
Contracts, claims and forms are read and typed in line by line, and errors surface weeks later.
- Extraction and classification at intake
- Only the exceptions reach a person
- Every field traceable to its source
Calls and messages pile up after hours
Bookings, verifications and follow-ups wait for office hours, and customers go elsewhere.
- Voice agents and chatbots on live data
- A clear handover to a person
- Every conversation logged
Work breaks between too many tools
People copy data between CRM, inbox and spreadsheets, and every hand-off is a chance to drop it.
- Agents that read, decide and act across systems
- Approval before any high-stakes step
- Audit trail on every action
Your AI should run in production, not in a slide deck.
Let’s talkAI 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. Chosen per task, not per trend.
01 / 08
Models
The language models behind each feature, picked per task on cost, speed and quality.
- OpenAI
- Anthropic Claude
- Google Gemini
- Meta Llama
- Mistral AI
- OpenRouter
- Hugging Face
02 / 08
Agents and orchestration
How agents plan, call tools and hand work to each other.
- LangChain
- LlamaIndex
- CrewAI
03 / 08
Machine learning
Custom models for prediction, vision and classification.
- PyTorch
- TensorFlow
- scikit-learn
- OpenCV
04 / 08
Vector search and databases
Where your data lives, and how answers stay grounded in it.
- Pinecone
- Qdrant
- PostgreSQL
- Redis
- MongoDB
05 / 08
Data pipelines
Moving and preparing data so models see the right, fresh inputs.
- Databricks
- Snowflake
- Apache Spark
- Apache Kafka
06 / 08
Cloud and infrastructure
Where it runs, in your cloud or ours.
- AWS
- Microsoft Azure
- Google Cloud
- Docker
- Kubernetes
07 / 08
Evals and operations
Tracing, testing and monitoring once it is live.
- Langfuse
- MLflow
- GitHub
08 / 08
Product and apps
The product your users actually touch.
- Python
- FastAPI
- React
- Next.js
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.
4–6 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.
30 minutesTechnical call
Talk the problem through with an engineer: the systems involved, the constraints and a realistic approach.
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.
AI SaaS for construction
“Avinya Labs delivered exactly what we needed. When a critical issue came up, the team stayed up until midnight so we weren't left hanging.”
Guides on this topic. Plain answers to the questions buyers ask first.
Enterprise AI Deployment: Why Projects Stall in Pilot
The eight causes we see most, and a roadmap that gets AI live.
Read the guide → GuideOperational AI Systems: A 2026 Guide for Enterprises
How AI moves from pilots into the daily operations of a business.
Read the guide → GuideAI Agent Development Cost: What Drives the Price (2026)
What drives the price of an AI agent: scope, complexity, iterations and retainer.
Read the guide →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.
What do you look for in an AI development company offering custom AI development?
Evidence that the AI works on your own data, a clear owner for every decision it makes, and a plan for monitoring after launch. Our AI development services, and custom AI development generally, are built around those three checks, as are the standards of any AI development company worth hiring. Ask every AI development company for these checks, and ask for AI development services that include monitoring. Good custom AI development ends with a team that can run the system, which is why our AI development services include handover and training, and we map risks using the NIST AI Risk Management Framework.
Free tool
How ready is your business for AI?
Twenty questions, about four minutes. You get a score across five areas and what to fix first.