Automely builds custom AI agents that go beyond automation — they reason about tasks, plan multi-step workflows, use tools, access data, and adapt their approach based on outcomes. From single-purpose task agents to complex multi-agent systems running entire business processes autonomously, we design, build, and deploy AI agents that create measurable operational leverage for businesses in the USA, UK, and EU.
Fixed-scope pricing • LangChain, AutoGen, CrewAI expertise • USA & UK timezone • NDA on day one0+
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An AI agent is software that perceives its environment, reasons about what to do, takes actions using tools and APIs, and learns from outcomes — without requiring a human to direct each step. Unlike traditional automation, which follows fixed rules and breaks when conditions change, an AI agent can adapt, plan, and handle novel situations.
The business case is straightforward. Every company has processes that are too complex for simple rule-based automation but too repetitive and time-consuming to keep running manually. AI agents fill that gap. They handle research, analysis, data extraction, report generation, customer communication, scheduling, code review, and hundreds of other cognitive tasks at scale.
Automely builds AI agents using the most capable frameworks available — LangChain, LangGraph, AutoGen, CrewAI, and custom architectures — integrated directly into your existing systems so agents work within your actual business context, not a disconnected sandbox.
WHAT WE BUILD
We build every type of AI agent your business needs — from focused single-task agents to orchestrated multi-agent systems running entire departments.
We design and build bespoke AI agents tailored to your specific business process, data environment, and automation goals. Every agent is built from scratch around your use case — not adapted from a generic template. We define the agent's goal, tool access, memory architecture, reasoning approach, and integration points before writing a single line of code. Best for: Businesses with a specific, well-defined workflow they want to automate with genuine AI reasoning.
Build a Custom AI Agent →Some processes require not one agent but a team of agents working in coordination — each specialised, each handling a distinct role, all orchestrated toward a shared goal. We build multi-agent systems using AutoGen, CrewAI, and LangGraph that can handle research pipelines, report generation workflows, competitive analysis, and complex business processes that require parallelisation. Best for: Operations teams, agencies, and SaaS companies that need entire workflows automated rather than individual tasks.
Build a Multi-Agent System →We build agents that can research topics autonomously — searching the web, reading documents, extracting structured data from unstructured sources, synthesising findings, and producing formatted reports. Research that takes a human hours takes an agent minutes. Best for: Consulting firms, agencies, investment teams, and any business where research and analysis is a significant operational cost.
Automate Your Research Workflows →An AI copilot sits alongside your team — embedded into the tools they already use, ready to assist with complex queries, document generation, data analysis, and decision support. Unlike a chatbot, a copilot has access to your internal data, your company knowledge base, and your workflows. Best for: SaaS companies adding AI assistance to their products, and internal operations teams building AI into their daily tooling.
Build an AI Copilot →We build AI agents designed for natural language interaction — customer-facing or internal. These agents understand intent, maintain conversational context across sessions, access real business data via tools, and handle complex multi-turn interactions that go far beyond what a scripted chatbot can do. Best for: Customer support operations, sales teams, and businesses with high volumes of inbound queries that require intelligent, contextual responses.
Build a Conversational AI Agent →We deploy your AI agents into production — integrating them with your CRM, ERP, Slack, email, databases, and APIs. We configure monitoring, logging, error handling, and human-in-the-loop escalation so your agents operate reliably in the real world. Best for: Any business that has an AI agent in development or in concept and needs production-grade integration and deployment.
Deploy Your AI Agent →HOW WE WORK
We follow a structured six-stage process for every AI agent we build. Each stage has a clear deliverable so you always know where the project stands.
01
We map the business process the agent will handle — inputs, outputs, decision points, exception cases, tool requirements, and success criteria. We define the agent's goal in precise, testable terms. Vague goals produce unreliable agents. Deliverable: A detailed agent specification document — goals, tools, data access, success criteria, and known constraints.
02
We design the agent's reasoning approach (ReAct, Plan-and-Execute, or custom), memory architecture (in-context, episodic, or vector-backed), tool ecosystem, and multi-agent communication patterns if applicable. Deliverable: An agent architecture diagram with reasoning model, tool inventory, memory design, and integration map.
03
We select the optimal foundation model for your agent's task profile — balancing capability, latency, cost, and compliance requirements. We design and test the prompt architecture that produces the most reliable agent behaviour. Deliverable: LLM recommendation with reasoning, prompt architecture, and baseline performance benchmarks.
04
We build the agent — implementing reasoning loops, tool use, memory retrieval, output parsing, and error handling. We connect all required tools: APIs, databases, web search, code execution, file access, and custom business logic. Deliverable: A working agent in your staging environment with all tools connected and baseline behaviour validated.
05
We test the agent against real-world scenarios, adversarial inputs, and edge cases. We measure goal completion rate, hallucination frequency, latency, and cost per task. We iterate until performance meets production standards. Deliverable: A test report with quantified performance metrics, failure analysis, and refinement log.
06
We deploy the agent to production with full observability — logging every decision, tool call, and outcome. We set up alerting for anomalous behaviour and run a monitored stabilisation period before handing over operational ownership. Deliverable: A live agent in production with monitoring dashboards, alerting configuration, and an operational runbook.
Building a working AI agent demo is easy. Shipping an AI agent that works reliably in production — with real data, real edge cases, and real users — is where most projects fail. The difference is engineering discipline, not AI capability.
Without This
With Automely
Agent hallucinates or makes decisions based on wrong data
We design data access architecture before building — every agent knows exactly what it can and cannot query
Agent works in demos but breaks with real-world inputs
We build production-hardened agents with edge case handling, fallback logic, and human escalation paths
Agent loops indefinitely or fails to reach goals
We design clear goal specifications, termination conditions, and progress monitoring for every agent
Multi-agent coordination breaks down under load
We architect inter-agent communication with message queuing, state management, and conflict resolution
No visibility into what the agent is doing or why
Every agent we build has full observability — decision logs, tool call records, and performance dashboards
Agent deployed but team cannot maintain or update it
We document all agent behaviours, provide source code ownership, and offer training and maintenance contracts
TECH STACK
Every tool below is used in live production agent deployments — not just evaluated in testing.
OpenAI GPT-4o
🔮
Anthropic Claude 3.5
Google Gemini 1.5
🌬️
Mistral Large
🦙
LLaMA 3
⚡
Groq
Below are examples of AI agent and automation projects delivered by Automely. Every agent is built to production standards with measurable business impact.
INDUSTRIES
Automely has built AI agents for clients across the industries below. We understand the domain-specific constraints, data environments, and compliance requirements unique to each sector.
Marketing & Creative Agencies
AI research agents, content brief generators, competitive analysis agents, SEO audit agents, and client reporting agents. Deliver more for clients without scaling headcount.
AI Agents for Marketing
SaaS & Software Companies
AI copilots embedded into products, customer onboarding agents, churn prediction agents, and internal developer productivity agents. Add AI capability to your product without rebuilding it.
AI Agents for SaaS
FinTech & Banking
Regulatory compliance agents, KYC automation agents, fraud investigation agents, and financial report generation agents — all designed for FCA and SEC compliance environments.
AI Agents for FinTech
Healthcare
Clinical documentation agents, patient intake agents, diagnostic support agents, and appointment scheduling agents — HIPAA-compliant and integrated with EHR systems.
AI Agents for Healthcare
Logistics & Supply Chain
Route optimisation agents, supplier risk agents, inventory monitoring agents, and automated procurement agents that work within your existing WMS and ERP.
AI Agents for Logistics
Professional Services
Legal document review agents, due diligence research agents, proposal generation agents, and matter management agents for law firms, accountancy practices, and consultancies.
AI Agents for Professional Services
FREQUENTLY ASKED QUESTIONS
What is an AI agent?
An AI agent is a software system that perceives its environment, reasons about what actions to take, uses tools to execute those actions, and adapts its behaviour based on outcomes — all without requiring a human to direct each step. Unlike traditional automation that follows fixed rules, an AI agent can handle novel situations and complex multi-step tasks.
What is the difference between an AI agent and a chatbot?
A chatbot responds to conversational inputs in a linear, turn-by-turn manner. An AI agent plans and executes multi-step workflows autonomously — using tools, accessing data, making decisions, and completing goals that may require dozens of actions across multiple systems. A chatbot answers. An agent acts.
How long does it take to build an AI agent?
Most AI agents take four to twelve weeks to build, integrate, and deploy to production. Simple single-task agents can be delivered in four to six weeks. Complex multi-agent systems with deep enterprise integrations typically take eight to twelve weeks. Automely provides a fixed timeline in the project scope.
Can you integrate AI agents into our existing systems?
Yes. Every AI agent we build is integrated into your existing systems — CRMs, ERPs, databases, communication platforms, and proprietary software. Integration is not an afterthought — it is designed into the agent architecture from day one.
Are AI agents scalable for enterprise use?
Yes. Automely designs multi-agent architectures specifically for enterprise scale — with distributed task execution, message queuing, state persistence, and horizontal scaling. Our production agents have handled thousands of concurrent task executions.
Do you provide source code ownership?
Yes. You own 100% of the source code, agent configurations, prompt engineering, and any fine-tuned models. We do not lock you into a proprietary platform. Everything we build is yours.
What happens if an AI agent makes a mistake?
Every agent we build includes human-in-the-loop escalation paths for decisions above a defined confidence threshold, full decision logging for audit and review, automatic error recovery and retry logic, and alerting when the agent encounters situations outside its designed parameters.
Whether you are starting from a blank page or trying to productionise an agent that is not working reliably, Automely has the expertise to get it done.
No lock-in contracts • NDA on day one • USA & UK timezone overlap guaranteed
