AI Agent Development Services
Built for Production, Not Demos
We design, build, and deploy custom AI agents — conversational agents, autonomous task agents, RAG knowledge systems, and multi-agent pipelines — that work reliably in the real world, not just in a sandbox.
Eight Types of AI Agents We Develop
From single-purpose conversational agents to complex multi-agent orchestration systems — built on the frameworks and models best suited to your use case.
Customer support, internal helpdesk, and sales assistants that hold context across long conversations and integrate with your CRM, ticketing system, and knowledge base.
Self-directed agents that plan and execute multi-step tasks without supervision — research, data collection, report generation, and workflow completion from a single prompt.
Document Q&A, internal knowledge bases, and research assistants powered by Retrieval-Augmented Generation. Your proprietary data, always current, every answer cited.
End-to-end process automation for approval chains, data entry, cross-system orchestration, and report generation — replacing brittle rule-based RPA with reasoning-capable AI.
Orchestrated teams of specialised agents working in parallel — one plans, one researches, one writes, one reviews — for complex tasks no single agent can handle reliably alone.
Agents that connect to your databases, write and run queries, interpret results, and surface actionable insights in plain language — no dedicated BI analyst required.
Phone and voice-enabled agents for inbound customer service, outbound follow-up, and appointment booking — integrated with Twilio, Vonage, or your existing telephony stack.
Domain-specific model fine-tuning on your proprietary data — terminology, compliance constraints, tone, and domain knowledge baked into the model itself rather than the prompt.
Our AI Agent Technology Stack
We are framework- and model-agnostic — we pick the right tools for each project, not the ones we happen to favour.
Our AI Agent Development Process
From scoping to production monitoring — a structured process that reduces rework and gets reliable agents into the hands of real users faster.
Define the agent's goals, decision boundaries, tool access, and success metrics. Map the workflows it will replace or augment and agree on evaluation criteria before writing a line of code.
Choose the right LLM, orchestration framework (LangGraph, AutoGen, CrewAI), memory strategy, tool integrations, and RAG pipeline design. Architecture decisions made before development save weeks of rework.
Build the agent core, connect APIs and databases, implement RAG pipelines, and wire up all required tool calls. We deliver working increments early so you can validate direction throughout development.
Evaluate accuracy, hallucination rates, latency, cost per query, and edge-case handling. Adversarially test for prompt injection, unexpected behaviour, and failure modes before any user sees the agent.
Deploy to your cloud infrastructure (AWS, GCP, Azure) with monitoring, logging, rate limiting, cost controls, and human escalation paths configured from day one.
Track production performance, collect failure cases, tune retrieval and prompts, and evolve the agent as requirements grow. Agents improve with use — we set up the feedback loops that make that happen.
AI Agents That Actually Work in Production
We have been building production AI systems since before agentic AI was a mainstream term. Here is what that experience means for your project.
We work with GPT-4o, Claude 3.5/3.7 Sonnet, Gemini 1.5 Pro, Llama 3, and Mistral — selecting the right model for your cost, performance, latency, and data-residency requirements.
We design, build, and tune complete RAG pipelines — chunking strategy, embedding models, vector store selection, hybrid search, re-ranking — not LLM wrappers that hallucinate because retrieval is broken.
Rate limiting, cost monitoring, fallback logic, human escalation paths, structured logging, and observability — built into the agent architecture, not bolted on after the first production incident.
Deep experience with LangChain, LangGraph, AutoGen, CrewAI, and Semantic Kernel. We choose the framework that fits the task complexity and your team's long-term maintenance needs.
Data stays in your infrastructure. We architect for prompt injection prevention, PII redaction, role-based access controls, and audit logging — not as afterthoughts, but as design requirements.
We build agents that run reliably in production for months — not impressive demos that fall apart on edge cases. Evaluation-driven development ensures what ships actually works.
AI Agents Across Every Industry
We build AI agents for eCommerce, SaaS, healthcare, and finance teams — with the domain context that makes the difference between a generic agent and one that works for your users.
- Product recommendation agents
- AI-powered customer support
- Inventory & pricing analysis
- Post-purchase follow-up agents
- Automated user onboarding agents
- Developer documentation assistants
- Code review & QA agents
- Customer success automation
- Patient intake & triage agents
- Clinical document summarisation
- Appointment scheduling agents
- Medical knowledge bases (RAG)
- Contract review & summarisation
- Compliance checking agents
- Financial data analysis agents
- Due diligence automation
AI Agent Development FAQs
Answers to the questions we hear most often before a project starts.
Start Your AI Agent Project
Tell us what you want to automate or augment — we’ll scope the right agent architecture and share a realistic estimate within 48 hours.
Let’s Build an AI Agent That Runs in Production
From a focused single-purpose agent to a full multi-agent system — we scope, build, and ship AI that works in the real world.