--- title: AI Agent Development Services | Avinya Labs description: AI agent development by Avinya Labs: agents that retrieve data across your systems, reason over it and act, with human approval before high-stakes actions, evals, and full audit trails. url: /service-pages/service-ai-agent-platforms.html --- [AI Development](/service-pages/service-ai-development.html) / 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. [Book a call](https://calendly.com/abbylester/30-mins-meeting) [See the work](/service-pages/service-ai-agent-platforms.html#solutions) [~40%Less manual effort](/case-studies/enterprise-ai-agent-platform.html) [~50%Fewer manual handling cases](/case-studies/multi-agent-voice-ai.html) [>80%Reduction in manual document review](/case-studies/enterprise-document-intelligence.html) ## 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. ## AI agent solutions we have shipped. Each result comes from a delivered engagement. AI · Agent platforms ### AI agent platform Agents retrieve, reason and act across systems, with human approval before high-stakes actions. ~40% less manual effort Link: /case-studies/enterprise-ai-agent-platform.html AI · Healthcare ### Multi-agent voice AI Live voice agents for scheduling, verification and follow-up calls, escalating to a person when needed. ~50% fewer manual handling cases Link: /case-studies/multi-agent-voice-ai.html AI · Document intelligence ### AI document intelligence Extraction and classification that sends only the exceptions to people. >80% less manual review Link: /case-studies/enterprise-document-intelligence.html AI · Travel ### Customer support assistant A support assistant grounded in live inventory data, available around the clock. ~35% fewer repeat contacts Link: /case-studies/ai-travel-chatbot.html AI · Legal ### Research & review assistant Citation-grounded answers, each one traced back to its source document. ~50% faster review time Link: /case-studies/ai-legal-assistant.html AI · Workflow automation ### Workflow automation platform AI extraction feeding a deterministic rules engine, with consistency checks built in. ~70% faster document prep Link: /case-studies/ai-workflow-automation.html ## Why 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? [Book a call](https://calendly.com/abbylester/30-mins-meeting) ## Industries we have delivered AI in. Each one links to the engagement behind it. [Business operations Case study →](/case-studies/enterprise-ai-agent-platform.html) [Healthcare Case study →](/case-studies/multi-agent-voice-ai.html) [Legal Case study →](/case-studies/ai-legal-assistant.html) [Insurance Case study →](/case-studies/ai-platform-insurance-broker.html) [Travel Case study →](/case-studies/ai-travel-chatbot.html) [Field operations Case study →](/case-studies/ai-workflow-automation.html) ## From workflow to production agent. Four stages, with a working version reviewed at each one. ### 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 ### Scope & architecture Define the agent's tools, permissions, approval checkpoints and delivery milestones. Agent designDone - Tools and permissions - Approval checkpoints - Delivery milestones ### 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 ### 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. ~40% Less manual effort AI agent platform → Link: /case-studies/enterprise-ai-agent-platform.html ~50% Fewer manual handling cases Voice AI → Link: /case-studies/multi-agent-voice-ai.html >80% Reduction in manual document review Document intelligence → Link: /case-studies/enterprise-document-intelligence.html ## Engagement models. Pick the shape that fits where you are. Ongoing ### Dedicated team A dedicated team that works as an extension of yours, from first release to scale. Fixed scope ### Project-based A defined scope, milestones and delivery date for a specific product or workflow. Advisory ### Consulting & advisory Architecture and readiness advice before you commit to a build. ## What our clients say. AI platform for an insurance broker > “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.” SandraProject lead, Insurance broker Result~2.5x Faster processing speed [Read case study →](/case-studies/ai-platform-insurance-broker.html) AI chatbot for a travel platform > “Avinya Labs built our AI travel chatbot … A highly capable team that delivers ahead of the curve.” LauraCo-founder, Travel platform [Read case study →](/case-studies/ai-travel-chatbot.html) ## 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. [Book a call](https://calendly.com/abbylester/30-mins-meeting) [See the work](/work/index.html) Or email [contact@avinyalabs.co](mailto:contact@avinyalabs.co)