AI agents that take action, not just chat.
We design, build and deploy custom AI agents that read context, make a decision and take a real action inside your stack: your CRM, inbox, ticketing system or database. Built for production, tested continuously and owned end to end.
AI agent development is building software that uses a large language model to read context, decide what to do and take an action on its own, such as looking up data, calling an API or completing a multi-step task, rather than just replying with text.
ibute is a custom software and AI development company, founded in 2022 with teams in Austin, TX and Lahore, Pakistan, that builds and runs production AI agents. Roughly 60% of our work is AI-led. We start with a free 30-minute call and a cost breakdown within 48 hours. For a deeper start, a fixed-price 2-week discovery sprint ($6,000 through Austin, TX or $3,000 through Lahore) gives you the plan and a fixed-price quote for the build.
At a glance
- What it is
- Custom-built, action-taking AI agents
- Best for
- Support, sales, ops & internal workflows
- Discovery sprint
- 2 weeks, fixed price
- Core stack
- OpenAI, Anthropic, LangChain, RAG
- Deploys to
- Your CRM, inbox, tickets, database
- Teams
- Austin, TX + Lahore, Pakistan
What is an AI agent and how is it different from a chatbot?
The short version: a chatbot answers and an agent acts. Many projects people call 'AI' are really agent projects.
An AI agent is a system built around a large language model (LLM) that can do more than write a reply. It can read the context of a request and decide which steps to take. It can call tools and APIs to get information or make changes, then complete the task end to end and check its own work. A chatbot ends at the answer; an agent ends at the outcome.
The practical difference is where the work lands. A chatbot lives in a website widget and hands the conversation back to a human. An agent lives inside your systems. It drafts and sends the email, updates the CRM record, triages the ticket or runs the multi-step research task. That's the line we use when scoping: if the goal ends in an action inside your stack, you want an agent.
Agents range from simple (one tool, one decision) to complex multi-step or multi-agent systems that plan, retrieve and verify. We start with the smallest version that solves the real problem, prove it works against a baseline and add capability only where the numbers justify it.
What teams hire us to build.
A sample of what we build. If your workflow ends in an action a person now does by hand, it's probably a fit.
Customer support agents
Reads the ticket and your knowledge base, drafts or sends a grounded reply, escalates the hard ones to a human with context attached.
Outbound & research agents
Researches a contact, drafts a tailored sequence in your voice, sends from your inbox and auto-replies to easy threads, as ReachStack does.
Document & workflow agents
Invoice triage, contract review, document classification and inbox routing: the manual queue that quietly eats a team's week.
Internal copilots
An agent over your wiki, database and tools that answers staff questions and performs the lookups and updates they'd otherwise do by hand.
Research & analysis agents
Multi-step research across internal and public sources, with citations. It turns half a day of manual digging into minutes.
Voice agents
Speech-to-text → LLM → text-to-speech for inbound/outbound calls. We've shipped production voice agents in the past year.
The full agent stack, from model to monitoring.
Most agent projects fail at the integration and evaluation layers, not the model. We own all of it.
Agent architecture
Single-agent, multi-agent or tool-using, designed around your real workflow, not a framework demo.
Reasoning & planning
Task decomposition, tool selection and self-checking. The agent picks the right step, then checks it worked.
RAG & retrieval
Grounded answers from your knowledge base via pgvector, Pinecone or Qdrant, so the agent cites instead of inventing.
Tool & API integration
Connect the agent to your CRM, inbox, tickets, database and internal APIs so it can actually do the work.
Guardrails & safety
Input and output validation, approvals for high-stakes steps and scoped permissions. The agent can only act within the permissions you give it.
Eval & monitoring
Test sets, accuracy tracking and live monitoring. The unglamorous half that decides whether an agent stays in production.
Comparing our delivery model vs. alternatives
| Rule-based chatbot | AI agent | Traditional RPA | |
|---|---|---|---|
| What it does | Answers from a script or FAQ | Reasons, decides and takes actions | Replays fixed UI clicks |
| Handles new situations | No: breaks off-script | Yes: reasons over context | No: brittle to UI changes |
| Takes real actions | Rarely | Yes: calls tools and APIs | Yes, but rigidly |
| Setup effort | Low | Medium: timeline set in a fixed-price quote | High and fragile |
| Best for | Simple, repetitive Q&A | Judgment + action workflows | Stable, legacy-UI tasks |
Rule of thumb: if the task needs judgment and ends in an action inside your systems, build an agent. If it's pure scripted Q&A, a chatbot is cheaper. If it's clicking through a legacy app with no API, RPA may still fit.
Discover, build, support.
Every engagement starts with a free call and a cost breakdown within 48 hours. For a deeper start, a fixed-price discovery sprint gives you the plan and a fixed quote before you commit to a build.
Discover
A fixed-price 2-week sprint. We map your use case, check your data, design the architecture and give you a fixed-price quote for the build. The plan is yours to take to any vendor.
Build
We build the agent on your real data and tools and test it against your current process. We launch once it meets the accuracy, cost and speed targets we agreed on.
Support
Monitoring, evals, guardrail updates and improvements in production. Support after launch costs 15% to 20% of the build cost a year, because models change and so does your workflow.
What an AI agent costs with ibute.
We publish our prices. Contract through Austin, TX if you want your team close to you or need developers in the US. Contract through Lahore for an offshore team at a lower price. The skills are the same either way.
| Through Austin, TX | Through Lahore | |
|---|---|---|
| 2-week discovery sprint (fixed price) | $6,000 | $3,000 |
| Single-task agent | $35,000 to $70,000 | $20,000 to $40,000 |
| Multi-step agent | $60,000 to $130,000 | $35,000 to $75,000 |
| Multi-agent or enterprise system | From $130,000 | From $75,000 |
| Support and improvements after launch | 15% to 20% of the build cost a year | 15% to 20% of the build cost a year |
A single-task agent does one job, such as resolving support tickets or qualifying leads. A multi-step agent runs a longer workflow with several decisions and systems. The first call and a cost breakdown within 48 hours are free. Compare these prices with the market in our AI agent development cost guide.
Model-agnostic, framework-pragmatic.
We benchmark on your real data before recommending. The right model usually picks itself once you fix two of latency, accuracy and cost.
Where this service has shipped.
Two recent engagements that leaned heavily on this practice. Read the full case studies, or browse all work.
An AI agent that researches, writes and sends in the rep's voice.
Multi-step research → tailored draft → multi-account send → inbox auto-reply. RAG over public and internal knowledge bases. Evals running continuously.
Custom ML model: 88% accuracy detecting how someone is traveling.
Sensor-fusion deep learning across gyroscope, accelerometer, GPS, magnetometer and barometer. Trained on multi-continent data. Predictions served from a cloud API.
The questions we get most.
Anything else? Email hello@ibute.tech and we'll reply within 24h.
What exactly is an AI agent?
What's the difference between an AI agent and a chatbot?
How long does it take to build an AI agent?
How much does AI agent development cost?
Why do prices differ between Austin and Lahore?
Will the agent hallucinate or take wrong actions?
Which LLM do you build agents on?
Can the agent run on our own infrastructure?
Do you keep working with us after launch?
AI Solutions
The pillar: agents, custom ML models and automation, all in one place.
AI Agent Development
Autonomous agents that take actions inside your stack.
AI Integration
Add GPT, Claude or Gemini to your product with RAG.
AI Consulting
Find where AI pays off, then de-risk the build.
AI Automation Agency
Replace manual, repetitive workflows with AI.
Get in touch
Have an AI agent in mind?
Free 30-minute review. We'll tell you whether this is the right fit, what the shape of the engagement would look like and roughly what it costs. No deck. No follow-up unless you ask.
Austin · Lahore · Reply within 24 hours.