AI agent development for tasks that run across your systems
We build AI agents that carry out multi-step work, such as answering support tickets, qualifying inquiries, and updating records, using the tools your business already runs.
Each agent is limited to a defined task, given only the access it needs, and set up to pass anything uncertain to a person.
Based in Lahore, we work with businesses across Pakistan and internationally.
Scope the task before choosing the agent
An agent that performs well in a demonstration and one that can be trusted in daily use differ in the parts that are rarely shown: how it connects to the systems where the work happens, what it is allowed to do without asking, and when it should stop and hand over to a person. We treat those questions as the main part of the project.
Each agent starts with one task it can do reliably and is extended only after it has proved itself on your real cases. You receive the code, the prompts, and the evaluation results, and the accounts it runs in are yours.
Quick answers
What is the difference between an AI agent and a chatbot?
A chatbot answers questions. An agent can also take actions in your systems, such as updating a record, booking a meeting, or routing a request, within the limits set for it.
What can it do without asking a person?
Only what has been agreed in scope. Actions that carry risk, such as refunds or contract changes, are routed to a person for approval.
How long does it take?
It depends on the task and the number of systems involved. A single-task agent is usually the first deliverable, and further tasks are added after it is in use.
What an AI agent build can include
Task and data review
Map the task, the data the agent needs, and the systems it must read from or write to.
Agent design
Choose the model and tools, define the steps the agent follows, and set the points where it hands over to a person.
System integration
Connect the agent to your CRM, helpdesk, email, chat, or databases through their APIs.
Testing and evaluation
Test against real cases and edge cases, and record how the agent performs before it goes into use.
Deployment and monitoring
Put the agent into use, log its decisions, and review its behavior so the scope can be adjusted over time.
Review the task
Discuss the work the agent will do, the data involved, and how success will be measured.
Design the agent
Agree the model, the tools it may use, the steps it follows, and where a person approves.
Build and connect
Build the agent and connect it to the systems it needs through their APIs.
Test and adjust
Run it against real cases, review the results, and adjust the limits and handoffs.
Deploy and review
Put it into use, monitor its decisions, and extend the scope where the results support that.
Connect the agent to the tools you already use
An agent is useful only where it can reach the systems the work lives in. We connect through each tool's own API and limit the connection to the access the task requires.
- CRM and helpdesk: Read and update records in the systems your team already works in
- Communication: Email, chat, and calendar tools where the agent needs to send or schedule
- Data: Databases and internal APIs, with read or write access set per task
- Models: Hosted or open-source models chosen for the task, and changeable later
CRMs
Helpdesks
Comms and data
Types of AI agents we build
Customer support agents
Answer common questions, resolve routine tickets, and pass the rest to a person with the history attached.
Sales and inquiry agents
Qualify inbound inquiries, answer product questions, book meetings, and update the CRM.
Operations agents
Process orders, reconcile records, prepare routine documents, and trigger the next step in a workflow.
Research and data agents
Gather and summarize information from your tools and agreed sources for a person to review.
Voice agents
Handle inbound and outbound calls that can also take actions. See our chatbots and voice agents page.
Internal assistants
Give staff an assistant that knows your processes and documents and can draft work for review.
AI agents by industry
Most industries have a few tasks that follow clear rules and take up hours each week. Those are where an agent is usually applied first.
Healthcare
Triage patient inquiries, route them to the right person, and handle appointment scheduling and reminders.
E-commerce
Answer order-status and returns questions, and handle product questions in conversation.
Real estate
Qualify buyer and tenant inquiries and book viewings into agents' calendars.
SaaS
Answer onboarding and how-to questions from your documentation and route the rest to support.
FinTech
Guide users through account questions and flag cases that need a compliance review.
Professional services
Take in new client inquiries, collect the details needed, and schedule consultations.
Signs that a task suits an agent
An agent is a practical fit where the work is high in volume, repetitive, and follows rules a person could write down.
Where the task is one step in a larger process, workflow automation or business process automation may be the better starting point.
- A high volume of similar questions arriving by email or chat each day
- First replies that wait until someone is free
- Inquiries that arrive outside office hours and wait until the next day
- Staff copying data between systems by hand
- Support or administration costs that rise with every new customer
- Routine work that follows the same steps but still needs a person each time
Designed to stop and ask
A useful agent is one that does its defined job reliably and hands over cleanly when it reaches the edge of that job. We set that boundary at the start. The agent handles the cases it has been tested on, passes the rest to a person with the history attached, and logs what it did so the reason for each action can be checked.
As the agent runs, we review where it hesitates, where it hands over too often, and where it could take on more. Changes to its scope are made from that record, and only with your agreement.
AI agent development pricing
AI agent development starts from Rs. 175,000. The final quote depends on the number of systems the agent connects to, the level of autonomy agreed, and the amount of data preparation involved. Model usage and any third-party service charges are separate.
AI Agent Development: your questions
What is the difference between an AI agent and a chatbot?
How long does it take to build an AI agent?
How is our data handled?
Can a small business use an AI agent?
How much does an AI agent cost?
Which AI models do you use?
Do you work with businesses outside Lahore?
Tell us about the task
Describe the task that takes the most time or waits the longest. We will tell you whether an agent is a practical fit and what it would involve.