AI Development / AI agents
AI agent development for real business workflows.
Agents that retrieve data across your systems, reason over it, and act, with a human approval checkpoint before anything high-stakes.
AI agent development services. From choosing the right workflow to running agents safely in production.
AI agent strategy
Find the workflows where an agent removes real manual effort, and define exactly what it may and may not do.
Custom AI agent development
Agents built around your processes, tools and data, rather than a generic template.
Agent architecture & tool design
Retrieval, reasoning and tool use designed so every action is scoped and permissioned.
System integration
Agents connected to CRM, ERP, ticketing and knowledge bases through their APIs.
Multi-agent orchestration
Specialised agents that hand work to each other, like the multi-agent voice platform we built for healthcare operations.
Human approval checkpoints
High-stakes actions route through a person before they execute, not after.
Evals & verification
Agents check their output against source systems before it is treated as final.
Observability & audit trails
Every agent action is logged and traceable, so nothing is a black box.
Deployment & support
Production rollout with monitoring, a defined support window, and ongoing tuning.
How it works. An illustrative replay based on a delivered engagement, using sample data.
- 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 agent solutions we have shipped. Each result comes from a delivered engagement.
AI 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 · Document intelligenceAI document intelligence
Extraction and classification that sends only the exceptions to people.
>80% less manual review AI · TravelCustomer support assistant
A support assistant grounded in live inventory data, available around the clock.
~35% fewer repeat contacts AI · LegalResearch & review assistant
Citation-grounded answers, each one traced back to its source document.
~50% faster review time AI · Workflow automationWorkflow automation platform
AI extraction feeding a deterministic rules engine, with consistency checks built in.
~70% faster document prepWhy teams put agents to work. The gains come from removing hand-offs, not from replacing judgement.
Less repetitive work
Agents take on the lookups, cross-checks and routine updates people currently do by hand.
Review where it counts
People review exceptions and high-stakes actions instead of every single item.
Around-the-clock coverage
Agents keep working outside business hours, as our travel support assistant does.
Connected systems
One agent can work across CRM, ERP, ticketing and documents instead of people switching tabs.
Auditable by default
Every step is logged, so any decision can be traced and reviewed later.
Faster turnaround
Work that used to wait in a queue moves as soon as the data is there.
Have a workflow ready for an agent?
Industries we have delivered AI in. Each one links to the engagement behind it.
From workflow to production agent. Four stages, with a working version reviewed at each one.
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Discovery
Map the workflow, the systems involved, and which actions carry risk.
Discovery notesDone
- The workflow to automate
- Systems and data involved
- Actions that need approval
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Scope & architecture
Define the agent's tools, permissions, approval checkpoints and delivery milestones.
Agent designDone
- Tools and permissions
- Approval checkpoints
- Delivery milestones
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Build
Build the agent against a working version, with evals reviewed at each milestone.
Working agentDone
- Agent built against real data
- Evals on representative cases
- Review at each milestone
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Ship & support
Roll out to production with monitoring, audit trails and a defined support window.
Production rolloutDone
- Production deployment
- Monitoring and traces
- Defined support window
Why Avinya for AI agents. Governance is part of the build, not an add-on.
Human approval built in
Approval checkpoints are part of the workflow from day one, rather than bolted on afterwards.
Every action logged
Agent actions are logged and auditable, so nothing happens in a black box.
Verified against source
Agents verify their own output against source systems before it counts as final.
Autonomy where it is earned
Agents act on their own for low-risk steps and defer to people on high-stakes ones.
Built for real operations
Designed to run against real production systems, not a sandboxed demo.
AI and Web3 under one team
One studio for agents, data pipelines and, where it helps, on-chain infrastructure.
Results from our AI agent work. Each number links to the engagement it came from.
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.”
“Avinya Labs built our AI travel chatbot … A highly capable team that delivers ahead of the curve.”
AI agent development FAQs.
What is an AI agent, and how is it different from a chatbot?
A chatbot answers questions. An agent can also take actions: it retrieves data, reasons over it, and completes steps in your systems. We design agents to act on low-risk steps and to ask for human approval on high-stakes ones.
How do you keep AI agents safe?
Every tool an agent can use is scoped and permissioned, high-stakes actions route through a person before they execute, outputs are verified against source systems, and every action is logged so it can be audited.
Which systems can your agents connect to?
Any system with an API or a structured export, typically CRM, ERP, ticketing and knowledge bases. We confirm the exact integration points 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.
How long does it take to build an AI agent?
It depends on scope and integrations. We agree milestones up front and review a working version at each one.
How do you measure whether an agent works?
With evals on representative cases before launch, and with traces and monitoring once it runs in production.
Ready to turn your vision into reality?
Tell us the workflow, the systems involved, and where manual effort sits today. We'll map what an agent can safely take on.
Or email contact@avinyalabs.co