Solutions / Enterprise AI agents

AI agents that finish the work, and ask before anything that matters.

Agents that read a request, gather what they need across your systems, and act on it. Low-risk steps run on their own. Payments, records and customer messages wait for a person.

Book a call See the results

TicketingRead request #4821, refund over limitDoneWorking
CRMRead 1 customer accountDoneWorking
ERPMatched 2 invoicesDoneWorking
KnowledgeFound the refund policyDoneWorking
ApprovalRefund is over the limit, asked FinanceApprovedNeeds approval
PaymentsReleased refund, logged to audit trailDoneWorking

~40%Less manual effort >80%Reduction in manual document review ~50%Fewer manual handling cases

Why most automation stalls. Scripts, chatbots and copilots each stop one step short of the work.

Rules break on messy inputs

Scripts handle fixed forms well. Emails, PDFs and free-text requests need reading, not matching.

Chatbots answer, then stop

A chatbot can explain the refund policy. It cannot check the invoice and issue the refund.

People become the integration

Tickets, accounts, invoices and policies sit in separate tools, so someone copies between them all day.

Full automation is a risk

Software that moves money or changes records with no checkpoint is how mistakes reach customers.

By hand

Read the requestPerson
Look up the accountPerson
Match the invoicesPerson
Check the refund policyPerson
Release the refundPerson

5 steps done by a person

With an agent

Read the requestAgent
Look up the accountAgent
Match the invoicesAgent
Check the refund policyAgent
Release the refundApproval

1 decision made by a person

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

Business operationsWorkflow agentRetrieves data across business systems, proposes actions, and routes high-stakes ones to a person.~40% less manual effort Document-heavy operationsDocument agentExtracts and classifies high-volume documents, sending only exceptions to people.>80% less manual review HealthcareVoice agentHandles scheduling, verification and follow-up calls, escalating to a person when needed.~50% fewer manual handling cases LegalResearch agentSearches case law and filings, with every answer traced to its source.~50% faster review time InsuranceData platformConnects policy, claims and correspondence data into one workflow.~2.5x faster processing TravelSupport agentAnswers booking and support questions from live inventory, around the clock.~35% fewer repeat contacts Field operationsWorkflow automationFeeds AI extraction into a rules engine, so the same input gives the same output.~70% faster document prep

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

Approval before high-stakes actions

Payments, record changes and anything over a limit wait for a person before they execute.

Proven in: AI agent platform →

Escalation to a person

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

Proven in: Voice AI →

Exception-only review

Confident results go through. Low-confidence cases go to a review queue instead of being guessed.

Proven in: Document intelligence →

Answers you can trace

Every answer links back to the document it came from, so reviewers check the source, not a summary.

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 →

Rules where results must repeat

AI reads the input; a deterministic rules engine produces the output, so it comes out the same every time.

Proven in: Workflow automation →

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

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 workflow to production. We start with a single workflow and a working agent you can review.

01
Discovery

Map the workflow, the systems it touches, and where people step in today, the systems and data involved, and what good looks like.
Discovery notes
- Workflow map
- Systems and access list
- What the agent may and may not do
02
Scope & architecture

Agree the architecture, success measures and delivery milestones.
Solution design
- Agent architecture
- Approval rules
- Success measures
03
Build

Build against real data, with a working version reviewed at each milestone.
Working build
- Working agent on real data
- Evals against real cases
- Review at each milestone
04
Ship & support

Roll out to production with monitoring and a defined support window.
Production rollout
- Production rollout
- Audit trail and monitoring
- Defined support window

Case spotlight. An agent platform in production.

AI · Agent platforms

An agent platform that cut manual effort by about 40%, with a person approving every high-stakes action.

Autonomous execution across business systems only works if there is a real checkpoint before consequential actions, not full automation without oversight.

An agent platform that retrieves relevant data, reasons over it, and proposes an action, routing anything high-stakes through a human approval step before execution.

Read the case study

~40%Less manual effort

Built inHuman approval checkpoints

1Agent platform across systems

Questions buyers ask about agents.

What should you ask an enterprise AI agents vendor?

Ask which actions the agent can take alone, which need approval, and how every action is logged. Enterprise AI agents should work inside your permissions, and enterprise AI development should end with monitoring, not just a launch. We map these risks with the NIST AI Risk Management Framework.

How is an AI agent different from RPA or a chatbot?

RPA follows fixed scripts, and a chatbot answers questions. An agent reads the request, gathers what it needs across your systems, and proposes or takes the next step, within limits you set.

What stops an agent from taking a wrong action?

Every action is scoped and permissioned. Low-risk actions can run automatically; high-stakes ones go to a person for approval before they execute, and every action is logged.

What about made-up answers?

Agents work from your systems and documents, and check their output against source systems before it is treated as final. Where answers come from documents, each one traces back to its source.

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.

Tell us what's slowing the team down.

Or book a call · contact@avinyalabs.co