Your team spending hours on tasks that follow the same logic every time?
Tried off-the-shelf AI automation but it can't handle your specific edge cases?
Most AI agent projects fail before they start. Nobody asked what the agent actually needs to do. Your team is repeating the same decisions dozens of times a day — routing tickets, qualifying leads, pulling data from one system and pasting it into another. Every one of those tasks can be handled by an AI agent: software that perceives its environment, decides what to do, and takes action without waiting for a human.
We are an AI agent development company that builds custom agents around your actual workflows. Task automation agents, decision agents, multi-agent pipelines, and enterprise integrations. Agents that work in production, not just in demos.
Custom agents built around your specific workflows and data
Multi-agent systems that pass work between specialized agents
Integrated with your CRM, ERP, support tools, and APIs
Fixed project cost -- scoped before development starts
A chatbot responds to input. An AI agent acts on it.
A chatbot takes your message and returns text. An AI agent takes your input, reasons about the right next step, calls external tools, retrieves data from your systems, executes a sequence of actions, and delivers an outcome. The difference is not the interface. It is the architecture.
Three things make a real agent: memory (it retains context across turns and sessions), tools (it can call APIs, query databases, trigger workflows), and autonomy (it decides what to do next without a human approving each step). A chatbot has none of these by default.
Chatbots vs. AI agents: the key differences
| Chatbot | AI Agent | |
|---|---|---|
| Output | Text response | Completed action |
| Memory | Single session (usually) | Persistent across sessions |
| Tools | None | APIs, databases, workflows |
| Decision-making | Pattern matching | LLM reasoning |
| Best for | FAQ, basic triage | Workflow execution, automation |
Most businesses discover they need an agent -- not a chatbot -- once they try to automate anything beyond a simple Q&A.
Not every workflow needs the same type of agent. The wrong agent type is one of the most common reasons AI automation projects stall after the prototype phase.
Answer three questions: How often does this task happen? How much does it vary? What does a good outcome look like?
Customer support agents handle Tier-1 inquiry volume at scale. They classify intent, retrieve answers from your knowledge base, take actions (update a record, issue a refund, escalate a ticket), and close loops without human involvement. Best for teams handling 200+ support interactions per week.
Voice agents support phone, IVR, and real-time decision workflows. They transcribe, reason, and respond in conversation -- and can trigger downstream actions mid-call. Best for healthcare intake, financial services verification, and logistics dispatch where voice is the primary channel.
Operations agents automate multi-step internal workflows end to end. They trigger on events (a new order, a failed payment, a system alert), execute logic across multiple systems, and report outcomes. Best for reducing manual process overhead in finance, HR, and supply chain.
Sales and outreach agents qualify inbound leads, schedule meetings, follow up on sequences, and surface intent signals from CRM data. They reduce the time between lead and conversation -- without adding sales headcount.
Research and data agents gather information from documents, APIs, and the web, synthesise it, and return structured outputs. Best for due diligence, competitive monitoring, regulatory tracking, and any task where a human currently spends time reading and summarising.
AI agents we've deployed have resolved 60% of inbound support queries without human transfer, reduced support ticket volume by 40%, and cut per-client onboarding time from 3 hours to 30 minutes.
Voice decision-support agent -- healthcare. We built a voice AI agent for a remote patient monitoring platform. The agent conducts structured voice check-ins with patients, flags anomalies against clinical thresholds, and routes urgent cases to nurses -- without any human dispatcher in the loop. See the AI in remote patient monitoring case study.
Conversational AI agent -- enterprise customer support. We built a conversational AI agent case study for an enterprise support operation. The agent handled Tier-1 classification, knowledge base retrieval, and ticket resolution. 60% of inbound queries resolved without human handoff.
Operations agent -- logistics. We built a multi-step operations agent that monitors shipment status across carriers, detects exceptions, contacts relevant parties, and updates internal records -- eliminating a manual process that previously required a dedicated coordinator role.
Most automation projects fail for the same reason: they are designed around the happy path. The workflow works when inputs are clean and edge cases don't show up. In practice, edge cases show up constantly.
Five failure modes we see repeatedly:
Wrong use case. Automating a task that requires human judgment -- not just human time. An agent can execute a process. It cannot replace a relationship or make a judgment call that depends on context the agent was never given.
No context management strategy. The agent loses thread after a few turns. Without explicit memory architecture, agents behave inconsistently at scale. This requires design decisions before writing a single line of code.
Missing guardrails. No human-in-the-loop for high-stakes decisions. Sending a message to a customer, updating a financial record, processing a refund -- these need confidence thresholds and escalation paths, not just a model that usually gets it right.
Integration underestimation. Roughly 40% of agent build cost is integrations, not AI. Teams budget for the model and underestimate the effort to reliably connect agents to CRMs, ERPs, helpdesks, and custom internal APIs.
No evaluation framework. Shipped without red-teaming or latency benchmarks. An agent that works on 200 test cases may fail unpredictably on case 201. Production-readiness requires systematic evaluation before go-live.
AI agents handle edge cases because they reason rather than pattern-match. When an input doesn't fit a predefined rule, a rule-based system breaks. An AI agent evaluates the situation, decides the most appropriate response, and either handles it or escalates with context.
Agents that execute a complete workflow end-to-end -- from trigger to completion -- without human involvement in the middle steps. Ticket routing, document processing, data extraction and enrichment, report generation, and approval workflows.
Agents that understand intent, hold context across turns, and take action based on what the user asks. Customer support agents that resolve issues, sales agents that qualify leads, and internal assistants that answer operational questions by querying your systems.
Systems where multiple specialised agents collaborate -- one agent researches, another validates, another formats and sends. Multi-agent architectures handle complex workflows that a single agent can't execute reliably.
Agents that evaluate incoming requests or events, apply your business logic, and route outcomes to the right destination -- the right team, the right system, the right response template. Built around your actual rules, not generic defaults.
Agents that gather information from multiple sources -- web, databases, documents -- synthesise it, and produce a structured output. Competitive monitoring, due diligence research, regulatory tracking, and market intelligence.
Agents embedded into your enterprise systems -- CRM, ERP, ITSM, helpdesk -- that act on system events, enrich records, trigger workflows, and surface insights without requiring a separate interface. The AI layer your existing tools don't have.
Walk us through the process. We'll tell you how an agent would handle it and what it costs to build.
We start by mapping the workflow the agent will handle -- every input type, every decision point, every edge case, every escalation trigger. Most clients discover edge cases they hadn't thought of during this step. That's the point.
Before full development, we build a working prototype of the core agent behaviour. You test it against real inputs. We measure accuracy and identify gaps. This takes 2 weeks and costs a fraction of the full build.
We build the production agent -- reasoning layer, tool integrations, guardrails, logging, and monitoring. We connect it to your systems and deploy it into your environment. You see working software every two weeks.
No competitor in this space publishes pricing. We do.
Single-agent MVP: $20,000--$50,000 (4--8 weeks). A focused agent handling one workflow end to end. Scoped, built, and deployed. Fixed price.
Production agent with integrations: $60,000--$150,000 (10--16 weeks). An enterprise-grade agent with CRM, ERP, or helpdesk integrations, guardrails, monitoring, and production deployment. Multi-workflow capable.
What drives cost: number of integrations (each adds 1--2 weeks of build time), model selection (GPT-4o vs. open-source affects both capability and ongoing API cost), compliance requirements (HIPAA, SOC 2 add audit logging and access controls), and evaluation depth (red-teaming and latency benchmarking before go-live).
We scope every project before pricing it. You know the cost before we start.
We build on production-proven frameworks. For single agents: LangChain and the OpenAI Assistants API for tool use and function calling. For stateful multi-agent workflows: LangGraph and CrewAI. For enterprise multi-agent orchestration: AutoGen.
LLM layer: GPT-4o, Anthropic Claude, and Mistral for open-source deployments. Model selection depends on task complexity, latency requirements, and data residency constraints.
Agent memory: vector databases (Pinecone, Weaviate, pgvector) for semantic retrieval, combined with RAG pipeline development when agents need to reason over large document corpora.
Cloud deployment: AWS Bedrock, Azure AI Studio, and GCP Vertex AI. We deploy to your existing cloud account -- you own the infrastructure from day one.
For generative AI development capabilities beyond agents, including fine-tuning and content automation, see our full AI practice.
What clients say
Three-year average engagement. Founders and operators describing the work in their own words. No marketing varnish.
“I definitely recommend RaftLabs, especially to solo founders like me. Their clear communication and detailed discussions have always helped me make better decisions.”
Generative AI Development -- Custom LLM applications, fine-tuned models, and content automation
AI Chatbot Development -- Conversational AI trained on your knowledge base
RAG Development -- Grounding agents in your data with retrieval-augmented generation
AI Workflow Automation -- End-to-end workflow automation with AI at the decision layer
Business Process Automation -- Automating structured processes across your operation
Tell us the process you want to automate. We'll scope the agent and give you a fixed cost.
Frequently asked questions
An AI agent development company designs, builds, and deploys software that can perceive inputs, reason about what to do, and take autonomous action. Unlike a software agency that builds tools for humans to operate, an agent development company builds systems that act on their own — completing workflows, making decisions, and integrating with your existing systems without requiring a human at every step.
An AI agent is software that perceives input -- from a user, a system event, or data -- reasons about what to do next, and takes action. Actions can include sending a message, updating a record, calling an API, running a search, triggering a workflow, or handing off to a human. Unlike a chatbot that only responds, an agent can plan, execute multi-step tasks, and use tools to accomplish goals. Modern AI agents are powered by large language models that provide the reasoning layer.
A chatbot responds. An AI agent acts. A chatbot takes your input and returns text. An AI agent takes your input, reasons about the right next step, calls external tools or APIs, retrieves data from your systems, executes steps in sequence, and delivers an outcome -- not just a response. A chatbot tells you a flight is delayed. An agent rebooking your flight, notifies the hotel, and updates your calendar.
A focused agent for a single workflow typically runs $20,000--$50,000. A multi-agent system with full enterprise integration typically runs $60,000--$150,000. Cost depends on the number of workflows, the complexity of integrations, and whether you need custom fine-tuning of the underlying model. We scope every project before pricing it -- you know the cost before we start.
A focused single-workflow agent typically takes 4--8 weeks from kickoff to production. A multi-agent system with enterprise integrations typically takes 10--16 weeks. We build a working prototype in the first 2 weeks so you can validate the agent's behaviour before committing to the full scope.
Industries with high-volume, repeatable decision workflows see the strongest returns: financial services (loan processing, fraud triage, compliance monitoring), healthcare (patient intake, documentation, appointment scheduling), logistics (shipment tracking, exception handling, carrier communication), SaaS (customer support triage, onboarding automation, usage monitoring), and professional services (research, document review, client intake). If your team does the same task more than 50 times a week, it is a candidate for an agent.
Use a no-code tool if your workflow is standard, your data is clean, and you don't need custom integrations. Build a custom agent if your workflow has edge cases that no-code tools can't handle, your data lives in proprietary systems, you need the agent to reason rather than just follow rules, or you need it embedded in your existing product. Most enterprise workflows fall in the custom category.
Ask: (1) Can you show me a shipped agent -- not a demo? (2) What is your process for diagnosing the right use case before building? (3) How do you handle agent failures and escalations? (4) What does fixed-price delivery mean in your contracts? (5) Who owns the code and models after delivery? (6) How do you measure agent accuracy before go-live? Vendors who can't answer these concretely are building demos, not production systems.