Leads wait while someone gets to them
Incoming leads sit in a form inbox until a person reads, qualifies, and enters them. An agent can qualify, validate, and push them into the CRM within seconds.
AI agent development for businesses that need work finished, not only answered.
We design and build AI agents around one clearly defined job. Each agent gets the tools it needs through APIs, validation steps that check its output, fallback logic for when a step fails, and a human handoff for anything it should not decide alone.
AI Agent Development
Custom scope after consultation
Scoped, built, and documented by our founder. You get a fixed scope and price before work starts.
- Agent scoping around one measurable job
- Tool and API integration (CRM, email, forms, databases, webhooks)
- Validation steps that check agent output before it is used
- Fallback and retry logic for failed steps
What we deliver
- A written scope: what the agent does, what it never does, and when it escalates
- A working agent connected to your tools, built in Python or n8n
- Prompt, tool, and decision logic documented so your team can maintain it
- Test runs against real examples from your business before launch
- A handoff walkthrough and post-launch fixes for the agreed support window
Outcomes clients want
- Repetitive multi-step tasks completed without a person driving each step
- Fewer errors, because output is validated before it reaches your systems
- Clear visibility into what the agent did and why
- A system your team can understand and change later
Service strategy
Problems AI agents solve
An AI agent is worth building when a task has several steps, touches more than one system, and follows rules a person can explain. Lead qualification, enquiry handling, data classification, and multi-step back-office work all fit that description.
Axenor AI builds each agent around one job. We define what a correct result looks like, connect the agent to the tools it needs, and add checks so a wrong answer is caught before it reaches your CRM, inbox, or customer.
Staff repeat the same check-classify-send loop
Someone reads incoming data, decides what it is, and forwards it to the right place. That decision loop can be automated with rules and review for edge cases.
Existing automations break on anything unusual
Rigid if-this-then-that flows fail when input is messy. Agents with validation and fallback logic handle variation and escalate what they cannot resolve.
Build process
How an agent build works
Every ai agent development project starts with the business outcome, then the workflow, content, tools, prompts, and handoff rules are designed around that goal.
01
1. Define the job
We write down the task, the inputs, the allowed actions, and the cases that must go to a human.
02
2. Connect the tools
The agent is connected to your CRM, email, forms, databases, or APIs with the minimum access it needs.
03
3. Add validation and fallbacks
Output is checked against rules before it is used, and failed steps retry or escalate instead of failing silently.
04
4. Test on real cases and hand over
We run the agent on real examples, fix what fails, document the workflow, and walk your team through it.
Business use cases
Where AI agents fit
Lead qualification
Read incoming enquiries, ask or infer the qualifying details, validate them, and write a clean record to the CRM.
Customer enquiry handling
Answer from an internal knowledge base, take simple actions, and pass complex cases to staff with context attached.
Data processing
Check, classify, and route incoming data to the right destination, with exceptions flagged for review.
Next reading
Related Axenor AI resources
These pages help connect ai agent development with the wider automation system your business may need next.
Work of this kind we have built
Built by our founder. Client systems stay private, so these are the builds we can show in full.
Ask Haseeb AI
LiveA live retrieval-augmented (RAG) assistant that answers questions about our founder's work from his own documents, and says so when the answer is not in them. New files are picked up from Google Drive, split into passages, embedded, and stored in a vector database; every answer is written from the passages it retrieves.
- Automatic ingestion of PDF, Markdown, and HTML files from Google Drive
- Token-based chunking (about 350 tokens, with overlap)
- OpenAI embeddings in Pinecone, LangChain retrieval, FastAPI backend
- Deployed on a VPS with Gunicorn and Nginx
Built with Python, FastAPI, OpenAI, LangChain, Pinecone
- 1Script
- 2Scene images
- 3Text animation
- 4Sound
- 5Timeline placement
- 6Finished video
Video maker
Internal toolA script goes in and a finished video comes out. The pipeline picks the images, animates the text, adds the sound, and places everything on the timeline without manual editing.
- Turns a written script into scenes automatically
- Handles images, text animation, sound, and placement
- Removes the repetitive editing steps from video production
What clients said about this kind of project
Published word for word, with the platform and date.
“Really solid work from Haseeb. We needed an AI agent that could handle incoming leads, qualify them and push the information into our CRM. He didn't just build the basic workflow, he also added the validation steps we were missing. Took a couple revisions but everything is working properly now.”
“Excellent technical work. We had a complicated workflow involving webhooks, APIs, Python and AI and needed someone who could understand the whole process rather than just one part of it. Haseeb built everything and also helped us clean up some of the logic in our existing automation. Very good communication and delivered what we agreed.”
“Very technical guy and easy to work with. We needed a multi-step agent workflow with API integrations, AI decision making and some fallback logic. Haseeb understood the requirements quickly and built the first version faster than I expected. We made a few changes afterwards and he handled those without any issues.”
AI Agent Development: common questions
Direct answers about how this service works, what it includes, and how it is priced.
Have a different question?
Ask it directly. You get an answer from the engineer who would build your system.
What is an AI agent?
- An AI agent is software that uses a language model to decide which steps to take and then carries them out with tools, such as reading an email, looking up a record, updating a CRM, or sending a reply. A chatbot answers; an agent completes a task.
What can a custom AI agent do for a business?
- Common jobs include qualifying incoming leads and pushing them into a CRM, answering customer enquiries from an internal knowledge base, classifying and routing incoming data, and running multi-step workflows across several APIs.
How do you stop an AI agent from making mistakes?
- We limit each agent to a narrow job, validate its output against rules before anything is written to your systems, add fallback logic for failed steps, and route uncertain or sensitive cases to a human.
Which tools do you use to build AI agents?
- Most builds use Python with FastAPI and the OpenAI API, or n8n when a visual workflow is easier for your team to maintain. Retrieval uses LangChain with a vector database such as Pinecone when the agent needs your own documents.
How is an AI agent project priced?
- Agent projects are scoped after a consultation because cost depends on the number of tools, the decision logic, and how much testing is needed. You receive a fixed scope and price before work starts. API usage, hosting and app subscriptions run on your own accounts, with a one-time $25 launch credit from us.