Data & artificial intelligence / AI assistants
A copilot that
knows your company.
AI assistants and copilots that answer, search and draft over your internal knowledge: your documents, your wiki, your tickets. With citations to verify it, per-user permissions and a person always in charge. Not a generic chatbot: a copilot that knows your world.
What it is
A model that speaks
your company's language.
An AI assistant or copilot is a language model (LLM) connected toyour knowledge sources. Instead of answering with what it learned from the internet, it answers with what your documents, your wiki, your tickets or your intranet actually say, and cites where it found it. The technique that makes this possible is calledRAG (retrieval-augmented generation): before answering, the system searches your data and writes the response from it.
This sets it apart from a generic chatbot, which only knows what the model ships with and tends to make things up when it doesn't know the answer. A well-built copilot leans on your information, admits when it doesn't know and leaves a verifiable trail.
And it should not be confused with an AI agent. An assistant answers, searches and drafts — and a person reviews and decides; an agent executes actions on its own, more autonomously (create a record, launch a process). Agents that act are covered in our process automation practice. Here we mean copilots: leverage for your team, with control always in a person's hands. It is part of ourdata and artificial intelligence practice.
What it can do
Six ways to take work
off your hands.
Internal knowledge search
Your team asks in plain language and the copilot answers with what your manuals, wiki or procedures actually say, citing where it found it. No more digging through folders.
Support copilot
It suggests answers to your agents from your knowledge base and past tickets. The person reviews and sends; the customer gets a correct answer, fast.
Drafting
It prepares first versions of emails, proposals, replies or summaries in your tone and with your data. You edit and approve; the blank page disappears.
Document summary and analysis
It summarises long reports, contracts or endless threads, pulls out the key points and flags what matters, with a link to the original paragraph so you can verify it.
Employee answers (HR and IT)
It handles the same repetitive questions — leave, payroll, VPN, access — with the current policy. Fewer internal tickets and consistent answers.
Sales assistant
It finds the right product sheet, price or case study in seconds and prepares material for a meeting. Your rep walks in ready, not improvising.
How we build it with control
Powerful, yes. But
always under control.
A useful copilot is also a safe one. This is how we design it from day one:
- We connect your sources: documents, wiki, intranet, tickets, emails and databases.
- Retrieval-augmented generation (RAG): every answer is grounded in your data, not in what the model “believes”.
- Citations and traceability: every answer links back to its source so it can be verified.
- Per-user permissions: each person only sees what they are allowed to see.
- No data leakage: your information is not used to train third-party models.
- Evaluation on real cases: we measure whether it answers well before opening it to everyone.
- Human-in-the-loop: on anything sensitive, a person always decides.
- EU hosting or on-premise when the data demands it.
By department
A copilot for
every team.
| Department | What you ask the copilot | Result |
|---|---|---|
| Customer support | “How do I resolve this case according to our policy?” | A correct answer with the source cited, in seconds and with the same judgment across the whole team. |
| Sales | “Draft a proposal for this client.” | A draft ready to review, with correct product data and pricing. |
| HR | “How many leave days do I have left and how do I request them?” | An instant answer on the current policy, without opening a ticket. |
| IT / internal support | “How do I set up the VPN on a new laptop?” | Guided self-service, step by step; fewer repetitive tickets for the team. |
| Legal / operations | “Summarise the risk clauses in this contract.” | Key points flagged with a link to the original paragraph to review them. |
The core idea
A good copilot doesn't replace you: it takes the boring work off your hands.
Benefits
What you can
expect.
Instant answers on what you know
Knowledge scattered across dozens of documents becomes searchable in a single question.
Less manual searching
Nobody wastes half an hour looking for “that PDF” or asking around who knows about a topic.
Faster onboarding
New joiners ask the copilot instead of interrupting a colleague. They get up to speed in days, not weeks.
Consistency
The whole team answers with the same information and the same judgment, not according to who picks up the phone.
Available 24/7
It answers at any hour and in several languages, with no queues or office hours.
Knowledge that doesn't walk out the door
When someone changes role, their knowledge stays accessible instead of leaving with them.
Technical approach
How it's
built.
We choose each piece for robustness and control over the data, not for the hype. If the case calls for it, everything can run on European infrastructure or in your own environment.
CPPA methodology
From your documents
to a reliable copilot.
Sources and use case
We identify which questions the copilot must answer and from which sources: documents, wiki, tickets, intranet. We also define who can see what.
RAG and guardrails
We build the retrieval that grounds every answer in your data, with citations, and we set the limits: what tone it uses, what it won't answer and when it should say “I don't know”.
Integration where you already work
We take it to where your team already is: Microsoft Teams, Slack, the intranet or a chat on your website. No extra tool to learn.
Evaluation and improvement
We measure quality on real cases (evals), fix what fails and keep the sources up to date. The copilot improves with use.
Examples
What it looks like
in practice.
Internal knowledge assistant
- Employee asks in plain language
- Search across your documents and wiki
- Answer written with citations
- Link to the original document
- Rating to keep improving
Support copilot
- A customer query comes in
- Search across knowledge base and past tickets
- Suggested draft reply
- The agent reviews and adjusts
- Sent to the customer
HR assistant
- Question about an internal policy
- Retrieval of the current rules
- Answer with the applicable section
- Escalated to a person if the case is sensitive
Contract and document analysis
- The document is uploaded
- Summary and extraction of key points
- Risk clauses flagged
- Citation of the source paragraph
- Human review and decision
Risks and mitigation
What we watch
to make it work.
Generative AI has real risks. We name them plainly and explain how we handle them:
Hallucinations
A model can invent a convincing but false answer. We prevent it by grounding everything in your sources (RAG), requiring citations and measuring with evals before opening it up.
Data leakage
Your knowledge is sensitive. We apply per-user permissions, keep your data out of third-party training and host in the EU or on-premise when needed.
Outdated answers
An answer that was correct a year ago can be wrong today. We keep sources updated and re-indexed so the copilot always cites the current version.
Over-reliance
A copilot assists, it does not decide for you. On sensitive matters we keep human control: the person reviews, approves and signs. Responsibility is not delegated.
Poorly defined permissions
If anyone can see everything, the assistant becomes a risk. We design with least privilege and test that each user only reaches what is theirs.
Low adoption
The best tool is useless if nobody uses it. That is why we integrate it where your team already works and support the rollout.
Frequently asked questions
How is this different from ChatGPT?
What is the difference between an assistant and an AI agent?
Does it use my data to train the model?
Is it secure and GDPR-compliant?
What does it integrate with?
How long does it take to be ready?
Related services
Keep exploring.
Artificial intelligence
An assistant is one form of applied AI. Explore the rest: vision, language and automation with judgment.
View →
Machine learning
When you need models trained on your data to classify, recommend or predict, beyond language.
View →
Business intelligence & dashboards
To see your data in clear dashboards. Often the copilot also summarises what they show.
View →What does your company know that nobody can find?
Request a proposal →Want to see how we think about a connected end-to-end system? Read ourCPPA X-RAY on 100 Montaditos.