Software that doesn't just answer — it acts.
We build autonomous AI agents and multi-agent systems that execute real business workflows by voice or chat — model-agnostic, securely integrated into your existing tools, and adopted on-site until your team actually uses them.
An AI agent is software that perceives context, reasons over it, and takes actions across your systems to complete a goal — not just reply with text. Where a chatbot answers a question, an agent plans and executes the steps: it retrieves data, decides within set rules, calls other tools, and reports the result.
Multi-agent systems coordinate several specialised agents to run complex, end-to-end workflows.
Key areas
Conversational agents
Workflow / process agents
Analytical / decision agents
RAG-grounded agents
2026 is the year agents move from demo to production. Gartner expects 40% of enterprise apps to embed task-specific AI agents this year, up from under 5% in 2025, and analysts project the agent market will grow from ~$7.8B to over $52B by 2030. Only ~17% of organisations have deployed agents so far while 60%+ plan to within two years — the early movers set the standard in their category.
Six types of agentic AI — from conversational agents to full multi-agent orchestration
Autonomous agents that understand natural-language instructions and act on them — by voice or text — across your systems in real time.
Agents that execute multi-step business processes across your tools without manual handoffs — from data retrieval through action to reporting.
An orchestrator coordinates specialised agents to run complex, end-to-end workflows. Each agent owns a domain; the orchestrator governs the process.
Agents that call your APIs, databases and apps — including via the Model Context Protocol (MCP) — to read live data and take real actions within limits.
Embedded AI copilots inside your own product, web or mobile app — giving your users a powerful assistant that works within your context and data.
Approval gates, action limits and full audit trails on every agent action — so agents operate within defined policy and humans control sensitive steps.
Support, sales and internal-knowledge assistants.
Automate a defined business process end to end.
Monitor data and recommend or trigger actions within boundaries.
Answer and act using your own documents.
A control layer governing how agents collaborate, escalate and comply.
Orchestration: LangGraph, LangChain, LlamaIndex, CrewAI · Models: GPT, Gemini, Claude, Llama · Retrieval: Pinecone, Milvus, FAISS · Build: Python, Node.js, Flutter, React
From workflow discovery through on-site adoption — six stages that close the gap most vendors leave open
Map the workflow, data sources, tools and success metric. Define what the agent must do, what data it needs and how success is measured.
Define what the agent may do and where a human must approve — guardrails, role permissions and escalation rules designed before any code is written.
Connect the agent to your data, tools and systems via APIs, databases and the Model Context Protocol (MCP) — no rip-and-replace required.
Develop the agent, then test against real use cases for accuracy, safety and edge-case handling. Iterate until the bar is met.
Production rollout with monitoring, logging, observability and alerting so you have full visibility into every agent action.
We stay until your team is actually using it — tracking the 6-month utilisation rate and resolving friction as it appears in daily use.
Adoption-first, model-agnostic, already shipped in production
We measure the 6-month utilisation rate, not just delivery. We embed on-site after go-live and stay until your team is genuinely using the agent.
Agents work through the systems you already run via MCP and APIs — no rip-and-replace. We choose the best model (GPT, Gemini, Claude, Llama) for your accuracy, privacy and cost.
Real agentic products in production — including a GenAI voice-and-chat project-management agent built on Flutter, Node.js and Gemini. Not slideware.
Role-based permissions, human approval gates, full audit trails, data-residency options and evaluation harnesses — security and guardrails built in from day one.
Run your day by talking or typing ("show my tasks", "mark task 100 complete"). Built with Flutter, Node.js, Gemini, Pocketbase.
AI agents operating on live ERP data through the Model Context Protocol — see MCP Integration for details.
A scoped agent delivered at a fixed price — ideal for validating agentic AI quickly.
Flexible build-and-iterate engagement for evolving agent requirements.
An embedded team for enterprise multi-agent programmes running 3–6 months.
A focused agent ships in 4–8 weeks · Enterprise multi-agent systems run 3–6 months
Role-based permissions, human approval gates, full audit trails of every agent action, data-residency options (cloud or on-prem), and evaluation/guardrails to keep behaviour within policy.
Everything you need to know about AI agents and agentic automation
A chatbot answers questions; an AI agent takes action — it executes multi-step tasks across your systems and reports back.
Several specialised agents coordinated by an orchestrator to complete a complex workflow together.
Yes — we build role-based permissions, approval steps and full audit trails so agents act only within defined limits.
A focused agent typically ships in 4–8 weeks; enterprise multi-agent systems run 3–6 months.
We're model-agnostic — GPT, Gemini, Claude or open models, chosen for your accuracy, privacy and cost needs.
Yes — agents call your APIs, databases and apps, including via the Model Context Protocol (MCP).
We ground agents in your data (RAG), constrain actions with guardrails, require human approval for sensitive steps, and continuously evaluate.
Yes — for data-sensitive and government workloads.
Production AI agents built for real business workflows
Book a discovery call and we'll map the workflow, the data and the outcome together. No commitment required.
