Chatbot and conversational agent development for companies
Ideasforge builds custom chatbots and conversational agents for companies that need more than automatic replies. Systems that can hold a conversation, look up information, use tools and take actions on CRM, ERP, databases, calendars, documentation, APIs and internal applications.
A conversational agent can answer. But it can also act.
What we build
AI chatbots for the channels where your customers or your team already are: WhatsApp on Meta’s official API, the web and internal tools. The agent understands natural language, queries your real systems (calendar, CRM, database) and completes the task without taking the user out of the conversation.
The difference is in what happens after the answer. A traditional chatbot can explain how to book an appointment, but leaves the booking to you. These agents complete the task inside the conversation.
- A query about business information
- A request logged in a CRM
- A booking created in a calendar
- A change inside a process
- A question about documentation
- An operation in an internal application
- A case passed to the right team
What can a conversational agent do?
These are the tasks we’re asked for most. The first four are specific types of work and the last one is part of every agent.
Customer service
Answers based on your data and documentation, with the matching source, at any hour.
Complete transactions
Book, change, cancel or check, with the calendar and the record updated on the spot.
Conversation screening
The ones with sales potential reach your team already qualified, and the rest get handled without taking anyone’s time.
Internal queries
The same engineering pointed inward, with employees asking their data or their documentation.
Handover to a person
When a conversation needs a person, it reaches the right team member with the full history.
The conversation is the interface. The process happens behind it.
These agents no longer depend on a closed tree of options. They can understand a request written in many ways, but they can only take the actions we’ve defined in advance.
The model interprets the request and chooses among the actions we define with you: book, look up, change or escalate. The code runs the chosen action and validates the result before replying.
WhatsApp Business API integration and the systems behind the conversation
Conversational agents connect to the tools your company already uses, for customers (web, app, WhatsApp) or for internal teams. Instead of clicking through several screens, a request in natural language becomes the operations needed inside those systems.
CRM
Look up customers, opportunities and interactions, or record new information.
ERP
Retrieve operational information, check statuses or run allowed operations.
Databases
Search structured information without the user needing to know technical queries.
Calendars and bookings
Check availability and manage appointments or resources.
APIs
Use features from external applications or your own services.
Documentation
Retrieve information from manuals, procedures, regulations or knowledge bases, with RAG and permissions when the project calls for it.
In-house software
Specific tools so the agent can work on the company’s own applications.
Channels
Web, your own app, WhatsApp or internal tools. Logic, sources and rules are shared. The experience of each channel changes.
Conversational agents already working on real processes
The best examples of a conversational system show up when the conversation is connected to a process.
Managing appointments through a WhatsApp conversation
Wazzy is our own product. The agent checks the real calendar, suggests times, books, changes or cancels, and applies the clinic’s rules. The user has a simple conversation. Behind it, the system controls the conditions under which each action can run.
See the Wazzy case →
Querying business information in natural language
The conversation acts as an interface over business data. The user asks. The system works out which information it needs and uses controlled tools to retrieve it, without anyone needing to know SQL or click through dashboards.
See the Savian case →How we build a custom conversational agent
We don’t put it on every conversation from day one. It starts small, on one channel, at certain hours or with a group of customers, with its categories measured from the first conversation.
01 We analyze the use cases
Who will talk to the agent, which requests the team gets today and what the conversation should achieve. Also which cases it shouldn’t resolve automatically.
02 We define the information sources
What the agent needs to look up to answer correctly: documentation, CRM, ERP, databases, APIs or other systems.
03 We design actions and integrations
What the agent can do beyond talking. Each action becomes a controlled tool inside the architecture.
04 We set rules and limits
Which information it can use, which actions are allowed, when it needs validation or approval and when it has to hand over to a person.
05 We evaluate real conversations
Ambiguous messages, incomplete data, changes of intent, errors, unexpected requests and situations where the agent should refuse to act. Not just the perfect conversation.
06 We measure in production
We pin the model version and every change goes through a test suite before it ships. Every conversation leaves a record that can be reconstructed.
What the agent can answer and when the conversation goes to a person
Knowledge answers come from your data and your documentation, with the source beside them. Sensitive texts, such as a returns policy, a legal condition or a price, aren’t written by the model. They’re texts you approved that the system delivers as they are.
And when there’s no data to answer with, the agent says so and offers the way to a person, who receives the whole conversation without making anyone repeat themselves.
The controls we put around the agent
- 01 Controlled knowledge sources
- 02 Information retrieval
- 03 Structured responses
- 04 Validations
- 05 Limited tools
- 06 Business rules
- 07 Permissions
- 08 Test suite before every change
- 09 Conversation log
- 10 Human intervention with context
When is a custom chatbot worth building?
Telling you when it doesn’t pay off is part of the job too. A conversational agent pays off when there’s volume, when the information exists and when a real share of the tasks can be completed inside the conversation.
It usually makes sense when it
- Needs to look up the company’s own information
- Has to work with several sources
- Has to connect to CRM, ERP or other systems
- Has to take actions
- Faces specific business rules
- Needs different permissions
- Works with private information
- Has to hand cases over in a controlled way
- Covers a process an off-the-shelf platform can’t handle end to end
It’s probably not needed when
- It only has to answer a small set of frequent questions
- An off-the-shelf platform already solves the problem well
- The volume of conversations is very low
- There’s no clear process to automate
- Building it would cost more than the expected benefit
Frequently asked questions about chatbots and conversational agents
What’s the difference between a scripted chatbot and a conversational agent?
A scripted chatbot follows a tree of buttons and breaks the moment you step outside it. A conversational agent understands free text and picks from the available actions, so the same question asked twenty different ways lands in the same place.
Which channels does it work on?
WhatsApp on Meta’s official API, the web and internal tools. Wherever your customers or your team already are.
Can it book, change or cancel appointments on its own?
Yes. We run it in production with Wazzy, an appointment assistant that’s our own product, with real-time availability, immediate confirmation and the calendar kept up to date.
What if my customer wants to talk to a person?
They always can. The way to a person is in plain sight and whoever picks it up receives the whole conversation, without making anyone repeat themselves. Outside working hours the case waits with its history and gets handled first thing. A bot that traps people is expensive in customers, so we don’t build one.
Will it make up answers in front of my customers?
Knowledge answers come from your data with their source, and the delicate lines are texts you approved that the system delivers as they are. When there’s no data it says so and offers a person. That brings the risk down a long way. What’s left gets measured per type of task, so a failure shows up in its own category instead of getting lost in an average.
Can we change what it says without calling you?
You edit the approved texts yourself without touching code. No edit reaches production without passing its check. A new document doesn’t change answers either until the test suite approves it. To change what the agent is able to do, we step in, also with the tests in front of us.
Who answers for the GDPR?
Your company is the controller and we are the processor. What we hand over is a processing agreement, technical measures and retention periods in writing. When special category data is involved, such as health data, that also includes field-by-field encryption and deletion on request of anything without a legal retention period over it.
How much does an AI chatbot cost?
Build and operation prices are the same as for the rest of our agents, between €2,500 and €10,000 to build and between €150 and €500 a month to operate. The monthly fee has no minimum term and model usage runs in accounts under your company’s name.
On WhatsApp there are two variable costs to keep in mind. Each conversation uses its own model calls. Meta charges separately for each template it sends, according to its pricing policy. An appointment reminder is a template, so each reminder has its own cost. Both are itemized.
AI agent development cost, broken down → What if WhatsApp changes its rules or its prices?
The official API protects you from Meta cutting off unofficial shortcuts, not from Meta raising its rates. Theirs are passed on itemized and separate from our part, so you always see which is which. And the agent’s logic doesn’t live in the channel, the same conversation can be handled on the web or in an internal tool with the same brain behind it.
Is it for selling or only for support?
The two overlap. An agent that filters and qualifies hands your team qualified leads, like the real estate agency that now only books viewings. What we don’t do is bulk messaging on WhatsApp, with or without AI.
How many conversations does it take to pay off?
We don’t give a magic number, because it depends on what handling those messages by hand costs you today. The pilot measures it with your real conversations, it’s paid and its price counts toward the final project. If the numbers don’t work, we’re the ones who tell you before you expand.
Other solutions
Tell us what a conversation should be able to resolve
You don’t need to decide whether you need a chatbot, a conversational agent or a specific architecture. Tell us who would talk to the system, which requests your team receives today and what should happen after each conversation. We’ll look at what can be automated, which systems would need connecting and in which situations a person should step in.