Data & AI
Integrating artificial intelligence into your business, realistically and securely
Generative artificial intelligence now makes it possible to process language at scale: answer from your documentation, extract information from invoices or contracts, classify requests, summarize exchanges, assist your teams. We integrate these capabilities into your existing tools, with hosted models (OpenAI, Anthropic, Mistral, Google) or models deployed in your own infrastructure.
This service is for businesses with a concrete use case and data to work with, not for those looking for a demo. We start by checking that the problem is suited to AI, that the data is accessible and that the result can be measured, then we build a first version evaluated on real cases.
We are direct about the limits: a language model can make up answers, be manipulated by instructions hidden in a document, cost a lot in tokens if the context is not controlled, and expose personal data if it is poorly integrated. Every solution we deliver includes an evaluation set, guardrails and a clear data policy.
When do we step in?
Answering from your internal documentation
Your teams waste time searching through procedures, contracts or past tickets. A RAG-based assistant (retrieval-augmented generation) answers while citing sources, without retraining any model.
Extracting data from documents
Invoices, purchase orders, resumes, reports: the useful information sits in unstructured PDFs or emails. An extraction pipeline feeds your ERP or CRM directly, with human review on uncertain cases.
Classifying and routing requests
Incoming requests (support, complaints, applications) are sorted by hand. A classifier, based on an LLM or a specialized model, categorizes, prioritizes and assigns them, with a confidence threshold below which a human decides.
Assisting your teams inside their tools
Drafting replies, summarizing customer history, preparing quotes: the assistant lives inside your CRM, ticketing tool or messaging rather than in a separate interface.
A prototype that never reaches production
A demo impressed everyone, but it is neither reliable, nor secure, nor measured. We take over the project with an evaluation, a clean architecture and cost control.
How we work
- 1
Scoping and feasibility
We qualify the use case: which task, which data, which acceptable error rate, which expected gain. We check data access and confidentiality constraints before writing a single line of code.
- 2
Evaluation set
Together with you, we build a set of real cases with expected answers. It is the only way to know whether the solution works and to measure progress at each iteration.
- 3
Architecture and model choice
Hosted or self-deployed model, model size, document chunking and indexing, vector database (pgvector, Qdrant, OpenSearch), context and caching strategy to control token costs.
- 4
Iterative development
We build the full chain (ingestion, retrieval, prompts, tool calls, interface), measure on the evaluation set, adjust, then widen the scope.
- 5
Security and compliance
Protection against prompt injection, personal data filtering, logging of exchanges, access control on sources, hosting choice compatible with GDPR and your industry obligations.
- 6
Production and follow-up
Deployment with monitoring of costs, latency and quality, built-in user feedback, and regular review of failure cases to improve the system.
Technologies we use
- OpenAI, Anthropic, Mistral, Google Gemini
- Open models (Llama, Mistral) via vLLM or Ollama
- LangChain and LlamaIndex
- pgvector
- Qdrant and OpenSearch
- Embeddings and reranking
- Python (FastAPI)
- Node.js (TypeScript)
- Amazon Bedrock
- Langfuse (evaluation and observability)
Related expertise
Why choose Agencei?
An evaluation, not a demo
We do not deliver an AI system without an evaluation set. You know precisely what rate of correct answers you get and on which cases it fails.
Integration into your existing systems
We are a development agency: AI is integrated into your CRM, ERP, APIs and database, with the same rigor as conventional software.
Security and data taken seriously
Prompt injection, data leakage through context, model hosting, log retention: these topics are handled at design time, not after an incident.
Controlled costs
The right model for each task, caching, context limits and token consumption monitoring: we design so the bill stays predictable.
In brief
- What is this service?
- Artificial intelligence integration service for businesses: solutions based on large language models (LLM), RAG over your documents, information extraction, classification and assistants integrated into your tools.
- Who is it for?
- SMEs, mid-sized companies and software vendors with a concrete use case, data to work with and the need for a reliable, secure and measured solution.
- What problem does it solve?
- Time lost searching for information, manual document entry, manual request sorting, unreliable or insecure AI prototypes.
- How long does it usually take?
- Scoping and a first evaluated version often take a few weeks. A production rollout integrated with your systems is usually measured in weeks to a few months depending on scope.
- What factors influence the price?
- Price depends on the use case, the number of data sources and integrations, the choice between hosted and self-deployed models, security and compliance requirements, and the desired follow-up.
- How does the engagement run?
- Scoping and feasibility, building an evaluation set, architecture and model choice, measured iterative development, security and compliance, monitored production rollout.
- What are the risks?
- Made-up answers (hallucinations), prompt injection, personal data leakage, uncontrolled token costs, vendor dependency, low adoption if the tool is not integrated into real workflows.
- What alternatives exist?
- AI features built into your existing software, conventional automation without AI when rules are explicit, or improved document search without generation.
Frequently asked questions
Do language models make up answers?
Yes, it happens, and no technique eliminates it completely. RAG greatly reduces the problem by grounding the answer in your documents and citing sources, and an evaluation set lets you measure the real error rate. For sensitive decisions, human validation remains necessary.
Is my data sent to OpenAI or other providers?
It depends on the chosen architecture. With a hosted model accessed via API, requests go through the provider, under contractual terms that generally exclude training on your data. If that is not acceptable, we deploy open models in your infrastructure or on a European cloud. We help you choose according to your obligations.
Do we need to train a model on our data?
Rarely. For most business use cases, RAG and good prompt engineering are enough and far simpler to maintain. Fine-tuning is justified for very specific formats or large volumes, after the other options have been exhausted.
How much does using an LLM in production cost?
The cost depends on the model, the request volume and above all the size of the context sent with each call. We estimate consumption from your real volumes, set up caching and limits, and use smaller models for simple tasks. Cost monitoring is part of the delivered solution.
What is prompt injection and how do you protect against it?
It is the insertion of malicious instructions into content the model reads (a document, an email, a web page) to hijack its behavior. Protection comes from strictly separating instructions from data, limiting the actions the model can trigger, validating outputs and testing these scenarios in the evaluation set.
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