Finance · Financial sector and investors
Custom AI agents for companies
At Possition, we design and implement AI agents that go beyond conversation. Our AI agents interpret complex requests, look up information in your systems, and carry out specific tasks, from querying data to generating documents. With our solution, your team is freed from mechanical work, improving efficiency and strategic decision-making.
What this service covers
Chatbot and AI agent: the difference
A chatbot answers questions by following a conversation. An AI agent, in addition to conversing, can take actions: query systems, update records, generate documents, and coordinate steps in a process. The chatbot informs; the agent resolves.
What tasks an agent can perform
- Query information in internal systems and reply with real data.
- Classify requests and route them to the right team or person.
- Update systems: log inquiries, enter data, change statuses.
- Send alerts when it detects something that needs attention.
- Generate documents from templates and system information.
- Coordinate tasks in a flow: request approvals, follow up, flag delays.
Human oversight by design
We define what the agent can do on its own and what requires confirmation from a person. Sensitive actions — payments, commitments, delicate replies — always go through human review. Every action of the agent is logged and auditable.
Security
- Minimum permissions: the agent only accesses what it needs.
- Full log of actions and decisions.
- Company information protected and not used to train third-party models.
- Explicit operating limits defined with your team.
Frequent use cases
- Customer service with access to real order and account information.
- Intelligent intake desk: classify and route emails and requests.
- Internal assistant that answers questions about procedures and documentation.
- Sales follow-up: log interactions and remind next steps.
Chatbot, AI agent, and RPA: how they differ
All three automate work, but they solve different problems. Confusing them is the most common cause of a badly sized project: a chatbot gets bought for a problem that needed an agent, or RPA is forced onto a process that keeps changing.
| Criterion | Chatbot | AI agent | RPA |
|---|---|---|---|
| Converses in natural language | Yes | Yes | No |
| Executes actions in systems | No | Yes | Yes |
| Handles cases it wasn't designed for | No | Yes | No |
| Requires predefined rules | Yes | Partly | Yes |
| What happens when the screen or process changes | Not applicable | Adapts | Breaks and must be rebuilt |
| Typical implementation cost | Low | Medium | Medium-high |
| When it fits | FAQs and handoff | Processes that need judgement | Repetitive, stable tasks |
They are not mutually exclusive: many implementations combine an agent that interprets and decides with RPA-style automation for the mechanical steps.
Real use cases
Professional services · Law firms, accounting firms, and independent professionals
AI assistants over WhatsApp for firms and professionals
65% less administrative time in professional firmsImplementation process
- 1
Diagnostic
We analyze your process and tell you what to automate, how, and what return to expect.
- 2
Implementation
We build the solution connected to your systems, with working deliveries and real tests.
- 3
Operation and improvement
We leave it running, train your team, and measure results.
The full detail, with deliverables per stage, is in how we work.
Guides to decide
Frequently asked questions
Can an AI agent make important decisions on its own?
It should not, and that is how we design it: high-impact decisions require human confirmation. The agent prepares the information and proposes; the person decides.
What does the agent need in order to work?
Controlled access to the relevant sources of information (systems, documents, databases) and a clear definition of its tasks and limits. Both are built in the project.
How is it different from a chatbot?
The chatbot converses; the agent also executes tasks on your systems. Many projects start with a chatbot and evolve into an agent when the case justifies it.
What happens if the agent makes a mistake?
Every action is logged and auditable, and sensitive actions always go through human review before executing. The design aims for an error to be caught and fixed quickly, not for it to never happen.
Can the agent update our systems, or only look up information?
It can do both: query real data and also log, update, or generate documents, depending on what's defined for each case and with the minimum permissions needed.
How long does it take to implement?
An agent with a narrow scope — one task, a few information sources — can be operational in weeks. Ones that coordinate several systems or process steps take more stages.
Does it work for more than one area of the company?
Yes: customer service, an intelligent intake desk, internal assistance, and sales follow-up are common use cases for the same type of agent, adapted to each context.
What tasks would you delegate to an agent?
Tell us what consumes your team's time and we will assess whether an AI agent can resolve it with the right oversight.