Service
AI Agent Development.
Custom AI agents that read, reason, and act — answering support tickets, qualifying leads, drafting documents, or orchestrating multi-step tasks autonomously, with a human in the loop where it matters.
What is an AI agent?
An AI agent is software that uses a large language model to understand a goal, decide on the steps, use tools such as APIs, databases, email and your CRM, and complete tasks with limited human supervision, for example resolving support tickets or qualifying leads. Qodexify builds agents with retrieval over your data (RAG), guardrails and human approval steps. A pilot typically takes 2–4 weeks and a production agent 6–12 weeks.
What's included
Agents built for real operational use
RAG & knowledge integration
Agents grounded in your docs, tickets, and data — not just the model's training data.
Tool-use & multi-agent systems
Agents that call your APIs, update your systems, and hand off to each other reliably.
Guardrails & human review
Confidence thresholds and approval steps so agents escalate instead of guessing.
Cost-aware architecture
Caching and model selection tuned so agent costs stay predictable at scale.
Our approach
How an agent project runs
Define the task boundary
We scope exactly what the agent should — and shouldn't — decide on its own.
Ground it in your data
Connect knowledge sources and tools so responses are accurate, not generic.
Pilot on real traffic
Run in shadow mode against live cases before it acts autonomously.
Monitor & tune
Track accuracy, cost, and escalation rate, and refine after launch.
Use cases
AI agent use cases
Common projects where this service delivers the fastest return.
Customer support agents
Classify, answer and escalate tickets using your help-center content and order data.
Sales & lead-qualification agents
Enrich inbound leads, score them and draft personalised follow-ups in your CRM.
Document processing
Extract structured data from invoices, contracts and forms with review workflows.
Internal knowledge assistants
Answer employee questions from policies, wikis and tickets with cited sources.
Back-office operations
Reconcile transactions, chase approvals and prepare reports automatically.
Research & reporting agents
Gather, summarise and monitor information on a schedule and deliver briefings.
Cost & timeline
Typical cost and timeline
Typical ranges. Model usage costs scale with volume, so we estimate monthly run cost before launch.
| Project type | Typical timeline | Typical budget |
|---|---|---|
| Pilot / proof of concept | 2–4 weeks | $10k–$30k |
| Production single-purpose agent | 6–12 weeks | $30k–$90k |
| Multi-agent system | 3–5 months | $80k–$200k |
| Monthly run cost (models and infrastructure) | Ongoing | $200–$3,000 typical |
Compare options
AI agent vs. chatbot vs. rules-based automation
An honest comparison to help you pick the right approach.
| AI agent | Chatbot | Rules-based automation (RPA) | |
|---|---|---|---|
| Handles unstructured input | Yes: emails, documents, free text | Limited to conversation | No: needs structured input |
| Takes actions in your tools | Yes, through APIs with permissions | Rarely | Yes, scripted only |
| Adapts to exceptions | Reasons about new cases, escalates when unsure | Falls back to scripts | Breaks on unexpected input |
| Predictability | Managed with guardrails and evaluation | High | Very high |
| Best for | Judgment-heavy, variable tasks | Simple Q&A | Fixed, repetitive steps |
Technologies we use
A pragmatic, proven stack
Common questions
About AI agent projects
What does an agent actually cost to run?
We give you a clear monthly cost estimate before launch, based on expected volume and model choice — not a surprise bill after.
Will the agent make decisions on its own?
Only within boundaries you approve. High-stakes actions route to a human by default until accuracy is proven.
What is the difference between an AI agent and a chatbot?
A chatbot mainly converses. An AI agent pursues a goal: it reads context, decides on steps and takes actions in other systems, such as updating a CRM record or issuing a refund, with permissions and approval rules you define.
Is our data safe when using large language models?
We design for data protection: minimal data sent to models, redaction of sensitive fields, encrypted storage, access controls and, where required, providers or deployments that do not train on your data.
How do you stop an AI agent from making mistakes?
We use guardrails, confidence thresholds, automated evaluation sets and human approval for high-stakes actions. Agents run in shadow mode on real traffic before they act on their own, and we monitor accuracy after launch.
Which AI models do you use?
We select per use case among leading models from Anthropic and OpenAI and open-source options, balancing quality, latency, privacy and cost, and we design so models can be swapped later.
From the blog
Guides for AI agent projects
AI Agents vs. Chatbots: What Is the Difference and Which Do You Need?
A chatbot answers questions. An AI agent completes tasks. Here is how they differ, where each fits, and how to deploy agents safely.
What Is RAG? Retrieval-Augmented Generation Explained for Business Leaders
RAG lets AI answer from your own documents instead of guessing. How it works, when to use it, and where projects go wrong.