How do you build an AI agent for your business?
Building an AI agent takes five steps: pick a specific, measurable use case (customer support, document search, invoice processing), gather the reference documents and real examples, connect the model to your data and tools with the right access rights, run a pilot that your teams test in real conditions, then measure the gains before scaling. JNS-IA designs and deploys this kind of agent in a few weeks, with sources cited, human approval on sensitive actions, and teams trained to supervise it.
What is a custom AI agent?
A custom AI agent is an assistant connected to a company's own data and tools — documentation, CRM, ERP, email — that understands a request in plain language, finds the right information, then acts: answers a customer, prepares a file, updates a system or triggers a process. What sets it apart from a generic chatbot is that it gets the job done end to end, instead of just holding a conversation.
At JNS-IA, every agent ships with guardrails: sources always cited, mandatory human approval on sensitive actions, access rights that mirror your organization, and data kept under the company's control, with models configured not to train on your content.
The build follows five steps — use case, reference material, tool connections, tested pilot, measured gains — for a deployment in a few weeks.
| Generic chatbot | Copilot | Custom AI agent | |
|---|---|---|---|
| Access to your data | None | Whatever documents the user opens | Your knowledge bases, CRM, ERP and email |
| What it produces | A conversational answer | A suggestion to approve in the tool | A task completed end to end |
| Ability to act | None | Within the host tool only | Across your third-party tools, under supervision |
| Traceability | Sources rarely cited | Depends on the vendor | Sources cited with every answer |
| Setup | Instant | Licenses to roll out | Pilot in a few weeks |
The AI agents we build most often
- Document assistant (RAG): answers internal or customer questions from YOUR documents, with sources
- Support agent: tier-1 triage and answers, smart escalation to your teams
- Back-office agent: reads and processes invoices, contracts and inbound email
- Pre-sales agent: meeting prep, prospect briefs, first drafts of proposals
- Multi-step agents: several tools orchestrated to run complete processes
How an AI agent gets built, step by step
A well-run AI agent project doesn't start with technology. It starts with a precise definition of what the agent has to get right. At JNS-IA, every build follows a proven sequence, from scoping to production, with sign-off at each stage.
- Scoping: pick the use case, set measurable success criteria (rate of useful answers, time saved, escalation rate), inventory the data and tools to connect.
- Gathering the material: reference documents, real requests and the answers your team considers good — this material shapes the agent's quality far more than the choice of model.
- Building the pilot: data connection (vector database, RAG), approved tools plugged in, guardrails in place — cited sources, human approval, logging.
- Real-world testing: the teams involved put the agent through cases they know well; its mistakes are analyzed and fixed, and its knowledge base is tuned.
- Measure, then scale: the criteria set during scoping are measured on the pilot; the scope only grows once the value is proven.
- Handover: your teams learn to supervise the agent, correct its answers and enrich its documentation — autonomy is part of the deliverable.
The traps that sink AI agent projects
Most disappointing AI agent projects fail for the same reasons — and none of them is technical. Knowing them before you start saves months of detours.
- Scope that's too broad: an agent meant to “do everything” does nothing well. An agent that handles one type of request flawlessly creates more value and trust than a rough setup spread across ten topics.
- Missing or outdated documentation: the agent answers from your content. If procedures are three years old or only live in people's heads, step one is writing them down — not plugging in a model.
- No success criteria: without a metric agreed in advance, nobody can say whether the pilot worked. The debate turns subjective and the project stalls.
- Teams meeting the agent on launch day: users who never tested the pilot don't trust it. Bringing them in during testing is the best guarantee of adoption.
- No guardrails: an agent that acts on sensitive cases without human approval, without citing sources and without logging gets switched off after the first incident.
- Mistaking a demo for production: a demo impresses in twenty minutes; an agent in production has to hold up on edge cases, unexpected formats and ambiguous requests. That's the gap between a prototype and a working tool.
Our approach: a pilot that proves the value
We always start with a specific, measurable use case: one document scope, one channel, success criteria set in advance. The pilot is built in a few weeks, your teams test it in real conditions, and we scale once the value is proven.
Have a use case in mind — or want to find the one with the best return? Book a free 30-minute call with Nathan Amar.
Budget
What does it cost? Quoted per project.
Every engagement is priced after the first conversation, because a two-hour training for six people and an AI agent wired into your ERP have nothing in common. The quote arrives within 24 h, and it is firm.
What moves the number
- Format and durationA two-hour awareness session, a one-day fundamentals course, a role-specific workshop, a programme over several weeks.
- Number of peopleA team of eight and three hundred employees trained in waves are not sized the same way.
- Level of integrationAn agent that answers from your documents costs less than one that writes into your CRM and ERP.
- Security requirementsHosting, access partitioning, logging, human approval on sensitive actions.
- What comes afterHandover to your teams, or ongoing maintenance by us — two different budgets.
Training: delivered by Nathan, through certified training partners.
Request a quote — reply within 24 hFrequently asked questions
Does our data stay confidential?
Yes. We design architectures where your data stays under your control, with models configured not to train on your content, and access rights that mirror your organization.
How is this different from ChatGPT for business?
A custom agent is connected to your data and tools, follows your business rules and can carry out actions end to end — whereas a generic assistant stops at conversation.
What is RAG, and why does it matter?
RAG (Retrieval-Augmented Generation) means the agent searches your documents before generating an answer. That's what lets it answer from your real content, cite its sources and sharply reduce the risk of making things up.
What do we need to prepare before the project starts?
Three things: a specific use case with success criteria, the reference material (procedures, real requests, answers considered good) and a business owner available a few hours a week during the pilot. No technical skills are required on your side.
What happens once the agent is in production?
The agent evolves with your documentation: your teams, trained during the project, correct its answers and grow its knowledge base as new cases come up. Tracking the metrics set during scoping confirms that quality holds and points to the most profitable scope extensions.