Custom AI agent development
Ideasforge builds custom AI agents for companies that need more than a chatbot, a basic automation or an off-the-shelf tool. Systems that can look up information, use tools and take actions on databases, ERP, CRM, APIs, internal documentation and in-house applications.
They run on your infrastructure, in a cloud under your company’s name. And they’re measured before every change.
Related guides
What is an AI agent?
What sets an agent apart from a chatbot, how it uses tools and in which situations it adds value inside a company. Read the guideHow much does an AI agent cost?
The factors that drive the cost: integrations, information sources, architecture, models, infrastructure and maintenance. Read the guideWhat we build
Custom AI agents that handle specific tasks: they query your documentation and your databases, guide diagnostics, qualify requests and take actions on the systems you approve. Each agent is built for one specific task and connects only to the tools that task needs.
When a single agent can’t cover the whole process, we build a system that coordinates several specialized agents. One of our production systems works this way and coordinates six.
- Query a database
- Retrieve information from internal documentation
- Check data in an ERP
- Update a CRM
- Check availability in a calendar
- Use an API
- Record a transaction
- Send a message
- Start a workflow
- Ask a person to step in when the case calls for it
What can an AI agent do inside a company?
These are some of the briefs we get most often.
Queries on your data
The question is written in plain language and the system turns it into a validated query against your database. The answer comes from that data, not from the model’s memory. “How much did we invoice in March in the northern region?”, asked exactly like that, the way you’d ask a colleague.
Assistant on your documentation
The agent searches manuals, wikis and internal systems and answers in a single conversation, saying which source each answer came from. The returns policy that today lives spread across three manuals gets answered in one sentence, with its source beside it.
Guided diagnostics
The agent asks questions, rules out possible causes and walks the user step by step to the problem, the way our plant assistant does when a machine stops.
Request qualification
The agent reviews every incoming request, checks it against the criteria you’ve defined and sends it to the right team. Of twenty quote requests arriving through the website, three deserve a call today and the rest can wait until tomorrow.
Actions on your systems
The agent can complete a task using only the actions you’ve approved. Registering the client, generating their contract and leaving it ready for signature, with nobody copying data from one screen to another.
AI interprets and code executes.
The model proposes an action, but the code decides whether it can run. The model chooses from a closed set of actions, so the limits don’t depend only on an instruction written for the model.
If the rules of your process are clear and stable, a custom automation solves it cheaper, faster and without the monitoring a model demands. We build that too.
Enterprise AI agents for different business processes
No single type of agent fits every situation. We design the architecture around the job the system has to do.
Data agents
They look up information stored in databases or business applications using natural language, with controlled tools to retrieve it.
Documentation agents
They work with manuals, procedures, regulations or technical documentation. When needed, with RAG architectures that retrieve only the relevant information.
Operational agents
Beyond looking things up, they take actions on ERP, CRM, APIs or internal applications: recording information, updating statuses, creating transactions.
Conversational agents
They use a conversation as the way in to information or actions, on the web, in internal applications, on WhatsApp or on other channels.
Multi-agent systems
They split responsibilities across specialized agents. Not by default, only when the split brings a real advantage in control, specialization or maintainability.
AI agents already working on real processes
We don’t approach agent development from theory alone. We’ve already built systems that work with real business information, tools and rules.
Querying business data in natural language
The model has no direct access to the database. It interprets the question and hands over a form with fixed fields. From there the code, which does behave the same way every time, builds the query.
See the Savian case →
An AI agent that manages appointments on WhatsApp
Wazzy is our own product. It books, changes or cancels an appointment through a conversation: the agent checks the real calendar and takes the actions following the clinic’s rules. The conversation is flexible and the scheduling rules are not.
See the Wazzy case →How we build a custom AI agent
We don’t start by looking for places to fit AI into your company. We look for the problems already costing you money and assess whether an agent solves them with a gain you can demonstrate.
01 We map the process
With the people who live the process, not only with whoever buys the technology. Step by step, with its people, its systems and its timings.
02 We measure what it costs today
Each candidate gets translated into its expected gain, starting from what it costs today in hours, errors or waiting. Most of that list gets discarded.
03 We design tools, permissions and rules
What it can look up, which tools it can use and which actions it can take. Validations, limits and conditions before sensitive operations.
04 We start with a pilot
One part of the problem, a small group of users and a metric agreed before starting. The pilot is paid and its price counts toward the final project.
05 We widen autonomy in stages
At first the agent proposes and a person reviews every output. When the tests and real use show it works in the expected cases, we widen the actions it can take without review.
A useful agent also has to be controllable
Every agent ships with a test suite, a set of real cases with their correct answer recorded, which runs in full before every change. If quality drops, the change doesn’t ship.
What runs in production gets reviewed every week, because a system with a model inside can get worse without a single error showing up in any log.
The controls we put around the agent
- 01 Tools limited to specific functions
- 02 Parameters validated before actions run
- 03 Structured responses
- 04 Business rules outside the model
- 05 Different permissions by user or context
- 06 Limits on certain operations
- 07 Extra validation before sensitive actions
- 08 A log of the tools used and the actions taken
- 09 Test suite before every change
- 10 A person steps in when the system can’t continue safely
When is custom AI agent development worth it?
Telling you whether it pays off is part of our service. An agent pays off when the process requires interpreting information that changes from one case to the next, such as emails, documents or requests written in different ways.
It usually makes sense when
- It has to work with several company systems
- It needs internal information
- There are specific business rules
- It has to take actions
- Its behavior changes with the context
- It needs its own permissions or limits
- It has to integrate with in-house software
- An off-the-shelf solution doesn’t cover the whole process
It’s probably not needed when
- An off-the-shelf product already solves the problem well
- The process is fully defined by rules
- There isn’t enough information for the system to work well
- Building it costs more than the value it can generate
- The only reason is that other companies use AI
Frequently asked questions about AI agent development
What does “for enterprise” mean in practice?
Your infrastructure or your cloud, your organization’s accounts, your data where you decide, and quality measured continuously. The guarantees your security committee will ask about, answered before the meeting.
Isn’t ChatGPT enough for us?
For helping people with their work, maybe it is, and we don’t charge for telling you so. A chat is a helper tool where whoever asked reviews every output. An agent is a system that produces the outcome on its own, on your data and under your permissions. That demands the engineering this page describes. They’re different purchases for different problems.
Can the agent connect to our internal systems?
Yes, that’s exactly what it’s for. ERP, databases, industrial systems, documentation. The agent only talks to the systems you approve, under each person’s own permissions.
Does the agent act on its own?
It decides inside a perimeter. The possible actions are a closed list you approve, the code validates each one before running it, and the sensitive ones require a person’s confirmation. Autonomy is rationed by cost and reliability, not by faith in the model.
What if our data is a mess?
It’s one of the first things we look at. If the data the case needs is missing or unreliable, we tell you, and the first phase becomes fixing it, because an agent on top of broken data only automates the error faster.
How much does it cost to build an AI agent?
A custom agent runs between €2,500 and €10,000 to build, depending on integrations and validation requirements, plus €150 to €500 a month to operate, which covers monitoring and maintenance of the system. The monthly fee has no minimum term.
The model and the infrastructure run in accounts under your company’s name, so those bills are yours and aren’t part of the fee. Systems with an orchestrator and several agents are quoted per project.
AI agent development cost, broken down → How long does it take to build an AI agent?
It depends on three things: that the data exists, that access has been granted and that the task is well defined. With all three, a pilot in two weeks is realistic. If one is missing, getting it is the first part of the project.
Who owns the agent when the project ends?
You do. The repository is under your name from day one: code, documentation, operations manuals, the model’s instructions and the test suite. So is the infrastructure, set up in a cloud under your company’s name.
Who maintains the agent afterwards?
Your call. We can run the measured maintenance, or train your team and hand over the operations manual so they run it. The documentation is written for that second case even if you choose the first.
What if the model provider raises prices or retires the model?
Swapping models is proven, not promised. We run systems in production on models from different providers, and a switch has to pass the test suite before it ships. Sometimes it also means touching the system, because different models behave differently. We’ve seen it happen: we rejected a cheaper model because the tests showed it lost ten points of accuracy.
What if you conclude our case isn’t worth it?
We tell you before charging for it, and you keep the process map with its numbers, which is worth having even if the agent never gets built. Discarding early is part of the method, not a failure of it.
Other solutions
Tell us what the agent would have to do
You don’t need to decide which model to use, how many agents you need or what the architecture should look like. Tell us which process you want to improve, which tools the system would have to work with and which actions it should be able to take.