Avinya Labs

Solutions / Customer support AI

Support that answers first time, and knows when to hand over.

Chat and voice agents that answer from live data around the clock, handle the routine requests, and pass everything else to your team with the conversation already captured.

Book a callSee the results

Web chat

Can I move my booking to the 14th?
Yes, the 14th and the 15th are both open. Which would you like?Source: change policy
The 14th, please. Can you refund the price difference?
Done, you are booked for the 14th.
The refund is outside policy, so I am passing you to our support team with this conversation.

What the agent did

  1. CRMFound the booking
  2. Inventory2 dates open, checked live
  3. KnowledgeFound the change policy
  4. BookingMoved to the 14th
  5. HandoverRefund needs a person
Illustrative conversation, sample data
~35%Fewer repeat contacts~50%Fewer manual handling cases~40%Less manual effort

Why support gets stuck. Volume, stale answers and handovers that lose the context.

Demand peaks when you are offline

Requests arrive at night and at weekends, when nobody is there to answer.

Stale answers create repeat contacts

An answer based on yesterday's availability brings the customer straight back.

Routine requests crowd out real ones

Scheduling, status and policy questions take the time complex cases need.

Handovers start from zero

When a bot gives up, the customer repeats everything to a person.

By hand

  1. Read the messagePerson
  2. Find the bookingPerson
  3. Check availabilityPerson
  4. Check the policyPerson
  5. Refund outside policyPerson

5 steps done by a person

With an agent

  1. Read the messageAgent
  2. Find the bookingAgent
  3. Check availabilityAgent
  4. Check the policyAgent
  5. Refund outside policyYour team

1 decision made by a person

Illustrative, based on the sample request above

Where our support agents already work. Each row is a delivered engagement. Select one to read it.

TravelChat agentAnswers booking and support questions from live inventory, around the clock.~35% fewer repeat contactsHealthcareVoice agentHandles scheduling, verification and follow-up calls, escalating to a person when needed.~50% fewer manual handling casesBusiness operationsWorkflow agentRetrieves data across business systems and routes high-stakes actions to a person.~40% less manual effort

Controls built in, not bolted on. Each one is running in a system we delivered.

Answers from live data

Availability and booking answers come from live inventory, so they stay current.

Proven in: Travel chatbot →

Handover to a person

When a conversation goes outside what the agent should handle, it hands over to a human operator.

Proven in: Voice AI →

Approval before high-stakes actions

Refunds, record changes and anything over a limit wait for a person.

Proven in: AI agent platform →

Answers you can trace

Answers from documents link back to the source they came from.

Proven in: Legal AI assistant →

Role-based access and audit trail

People see only what their role allows, and every record change is logged.

Proven in: Insurance AI platform →

Exception-only review

Confident results go through. Uncertain cases go to a person instead of being guessed.

Proven in: Document intelligence →

What we build support agents with. Chosen per channel. Models can change later without a rebuild.

Channels

Where customers ask

  • Web chat
  • Voice

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

AI evaluation and observability

Quality and cost, measured

  • Model evaluation
  • Prompt testing
  • Hallucination detection
  • Latency monitoring
  • Cost tracking
  • Output quality assessment
  • AI observability
  • Langfuse

Used in our delivered platforms

From one support journey to production. We start with the requests you get most, and a working agent you can review.

  1. Discovery

    Map the support journeys, the systems and data involved, and what good looks like.

    Discovery notes

    • Top request types
    • Systems and data access
    • When to hand over to a person

  2. Scope & architecture

    Agree the architecture, success measures and delivery milestones.

    Solution design

    • Agent architecture
    • Handover rules
    • Success measures

  3. Build

    Build against real data, with a working version reviewed at each milestone.

    Working build

    • Working agent on real conversations
    • Team review of every flow
    • Accuracy tests

  4. Ship & support

    Roll out to production with monitoring and a defined support window.

    Production rollout

    • Production rollout
    • Conversation monitoring
    • Defined support window

Case spotlight. A support assistant on live inventory.

AI · Travel

A travel chatbot that cut repeat contacts by about 35%, answering from live inventory around the clock.

Travel support requests spike outside normal business hours, and answers grounded in stale inventory data create repeat contacts when availability has already changed.

A chatbot that answers booking and support questions using live inventory data, with 24/7 coverage that doesn't depend on staffing.

Read the case study
~35%Fewer repeat contacts
24/7Coverage
LiveInventory-grounded answers

Questions buyers ask about support AI.

Will customers get stuck talking to a bot?

No. The agent hands over to your team when a request goes outside what it should handle, with the conversation already captured.

Does it work on the phone as well as chat?

Yes. We have delivered both: a chat assistant for a travel platform and voice agents for healthcare operations.

Where do the answers come from?

From your live systems and documents, not a static script. Answers from live inventory cut repeat contacts by about 35% in our travel engagement.

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 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.

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.

Ready to turn your vision into reality?

Tell us the requests your team handles most. We'll show you what an agent would answer, and when it would hand over.

Book a call See the work

Or email contact@avinyalabs.co

Tell us about your project

Rather talk it through? Book a call · or email contact@avinyalabs.co

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