What an AI agent development service actually builds
Aiqwip builds custom AI agents for small and mid-sized businesses in the US, organized by business function (front desk, sales, support, recruitment, procure-to-pay) rather than by industry.
Some work can't be handled with simple rules: replying to customer emails, qualifying leads, reviewing documents, chasing invoices. AI agents can take it on.
Three kinds of software often get lumped together, and it helps to separate them:
- Automation follows fixed rules. When a form comes in, it creates a CRM record. It's fast and predictable, but it can't handle a case nobody planned for.
- A chatbot answers questions in a conversation. It's useful for support and FAQs, and it usually stops at the answer.
- An AI agent works toward a goal. It reads the situation, decides what to do next and uses tools to act. It might look up a record, update your CRM, draft a reply or book a meeting.
What we deliver is a working agent connected to your systems. It has clear limits on what it may do, and a person approves the decisions that matter.
Many businesses use all three. AI automation services handle predictable steps, AI chatbot development covers conversations, and agents take the work in between.
Why this matters
Your best people spend hours on work that needs judgment but not their expertise. An agent that handles the routine cases frees them for the work that grows the business, and it keeps working around the clock without adding headcount.
Where AI agents fit in your business
Agents pay off where work needs judgment but has a clear goal. We have ready-made agents for five common business functions, and we adapt them to your tools and process. That's a faster start than building from scratch.
- Front desk. Answers inbound enquiries, qualifies leads, routes conversations and books meetings, and hands anything unusual to your team. See the front desk AI agent.
- Inside sales. Contacts prospects at the right moment, follows up on stalled deals and works alongside your sales team. See the inside sales AI agent.
- Customer support. Resolves routine product, account and order questions, and hands over to a person when needed. See the customer support AI agent.
- Recruitment. Screens applications, schedules interviews and keeps candidates updated. See the recruitment AI agent.
- Procure-to-pay. Handles purchase requests, invoice matching and approval routing. See the procure-to-pay AI agent.
If your work doesn't fit one of these, we build the agent around your process from the start.
Our AI agent development process
Here's how a project runs, from first call to live agent.
- Discovery and scoping. We map the work you want to hand over and find where an agent fits. We define what it can do, which tools it can use, when it must ask a person, and how success is measured.
- Architecture and tool selection. We decide which models to use and how the agent reaches your systems and data: tool calling, retrieval from your documents (RAG), or MCP where several agents share tools.
- Build and integration. We build the agent and connect it to your systems.
- Testing. We test the agent against real cases from your business before it goes live, including odd inputs.
- Deployment. We roll it out step by step with your team, starting with a person reviewing its decisions.
- Ongoing support. We review what the agent does, fix issues and keep improving it based on real use.
Tools and platforms we build with
We're model-agnostic and use widely used tools, so you can hire developers for them later.
- Models: OpenAI, Anthropic's Claude or open-source models, chosen per task for accuracy, speed and cost. Azure OpenAI suits businesses that run on Microsoft's cloud.
- Agent frameworks: LangGraph and LangChain for multi-step agents that plan and call tools.
- Tool calling: the agent works through functions you approve, such as looking up an order or creating a ticket, instead of acting freely.
- RAG: retrieval-augmented generation, so the agent answers from your own documents and data and can cite its sources.
- MCP: the Model Context Protocol, an open standard for connecting agents to tools and data. It helps when several agents or apps share the same tools.
- Integrations: your CRM, email, calendar, help desk and ERP, connected through their APIs.
- Hosting: major cloud platforms, most often Microsoft Azure, with logging and monitoring in place from day one.
What it costs to develop an AI agent
Every agent is priced for its scope, so we don't publish a price list. The consultation and written roadmap are free. A short paid scoping phase then fixes the price and timeline before we build. What moves the price:
- Number of integrations: each system the agent reads from or writes to adds build and testing work.
- Data complexity: clean, structured data is quicker to work with than scattered documents or old records.
- Compliance requirements: approval steps, audit trails and rules about where data lives add design and testing.
- Autonomy: an agent that drafts work for a person to approve is simpler to prove out than one that acts on its own.
Running costs are separate from the build. Model usage and hosting grow with how much work the agent does. Read more about how we price custom AI software.
Agent projects we've built
Two projects show core agent skills in practice: working out what a person needs and acting on it, from pulling order data to running interviews.
- A live call co-pilot for an e-commerce company. It listens to calls, works out what the customer wants and pulls order status, account details and support history from CRM and ERP systems. The service rep never leaves the call. Read the contact center AI co-pilot case study.
- Hiring software for an HR tech company. It parses and ranks resumes and runs automated voice interviews that adapt to candidate answers. Human interviewers get a co-pilot that suggests questions and scores responses. Read the recruitment automation case study.
Custom development vs off-the-shelf agent builders
Off-the-shelf agent builders are a good choice for simple, generic tasks. If you want an assistant that answers questions from a few documents or sorts incoming email, try one first. It's usually cheaper, and we'll tell you so.
Custom development is worth it when:
- The agent has to work inside your specific systems, such as an ERP, an older database or a tool with no ready-made connector.
- It depends on your own data and business rules, and wrong answers are costly.
- You need control over permissions, approval steps and where your data lives.
- You want to switch models later without starting over.
Plenty of businesses use both: a builder for simple jobs, and a custom agent for the workflow that matters most.
Why work with Aiqwip
We're a small team that builds custom AI software for growing businesses.
- Across business functions. We don't stick to one industry. Our work covers sales, support, hiring, finance and research, so we've seen the same problems in more than one part of a business.
- Direct access to the build team. You talk directly with the people designing and building your software.
- Custom software, not a platform. We don't resell an agent product or tie you to one.
What you get
- A written agent spec: what it may do and which tools it can use
- A dashboard to review what the agent did and why
- Full source code and documentation, owned by you