Artificial intelligence
Generative AI for business: LLM and RAG
We connect language models to your company knowledge. With RAG, answers rely on current documents and not only on what the model learned earlier.
What we can do for you
Knowledge base with semantic search
We index documents, wikis, PDFs and tickets to find meaning, not only keywords.
RAG with source citations
Each answer links to documents, so it is easy to verify.
Content and summary generation
Drafts of proposals, product descriptions, reports and summaries that the team only approves.
Model selection and evaluation
We compare models on quality, speed, cost and data requirements.
About this service
AI adoption in a company usually starts with a single process: handling customer inquiries, processing documents, searching company knowledge or forecasting. Depending on the task we build chatbots and assistants, AI agents, process automation, OpenAI API (ChatGPT) integration, generative solutions with RAG, or predictive models.
As a software house we handle the engineering side: connecting the model to your systems, data security (with GDPR in mind), control of model usage costs, and the ability to switch providers without rebuilding the application.
Why Cormo
Current knowledge
After re-indexing, a change in a document reaches answers without retraining a model.
Content permissions
Retrieval respects permissions, so everyone sees only the documents they have access to.
Vendor independence
The architecture lets you swap the model without rebuilding the whole solution.
Technologies
- Python
- pgvector
- Embeddings
- OpenAI API
- Claude API
- Elasticsearch
- FastAPI
Experience you can rely on
For years we have built business systems in PHP, Python and JavaScript. We treat language models and machine learning as one more component of such a system: first we establish which process should benefit and what data that requires.
We work in line with ISO/IEC 27001
Data security is part of our process, not an add-on at the end of a project.
How we work
- 01
Meeting
We learn the business goal, the users and the project constraints.
- 02
Estimate
We settle the scope, a first estimate and a schedule.
- 03
Delivery
We design, build and test in short iterations.
- 04
Support
We deploy, monitor and evolve the solution after launch.
Estimate calculator
Estimate your project in 2 minutes
Answer a few questions and we will prepare a first estimate and send it to your email. No strings attached.
See also
Chatbots and AI assistants
An assistant that answers customer and employee questions from your documents, price lists and procedures, and hands the case to a person when needed.
AI agents
An AI agent does not only answer, it acts: it fetches data, calls APIs, prepares documents and asks for approval where a person should decide.
Process automation
We take over repetitive work with documents, emails and spreadsheets. Your team handles exceptions and decisions instead of retyping data.
AI consulting
Before you invest in artificial intelligence, we check where it will bring a measurable result, which data is needed and what it will cost.
Predictive AI
The data your company already holds can suggest decisions. We build models that forecast demand, flag deviations and assess risk.
Data science and analytics
We organize data from many sources and present it in a form you can use daily: reports, dashboards and analyses for management and teams.
OpenAI (ChatGPT) integration
We connect OpenAI models to the apps, CRM and processes in your company. We design it so you can change the model provider without rebuilding the whole system.
AI document processing
Instead of retyping invoices, contracts and orders, the system reads them, pulls out the needed data and hands it to accounting software or an ERP system. A person checks only what is uncertain.
Computer vision
A camera or a phone photo can be a source of data. We build systems that recognize objects, read labels and detect deviations, and pass the results to your software.
AI for e-commerce
A store holds data that can turn into sales: order history, search queries and product descriptions. We add AI features that help customers and save your team time.
AI-powered applications
We help you build a new AI-based product or add AI features to an existing app or SaaS platform, so they are fast, secure and affordable to run.
Frequently asked questions
What is RAG and how does generative AI use my company’s knowledge?
RAG (retrieval-augmented generation) is an approach where a language model first searches your current documents for relevant passages and then writes an answer based on them. It therefore does not rely only on what it learned earlier. We deliver the whole pipeline: preparing and indexing documents, retrieval, model integration and an interface such as a knowledge search or an assistant.
How does an LLM and RAG project run step by step, and how long does it take?
First we collect and organise knowledge sources, then choose how to split documents and which model to use, then build a prototype and assess answer quality on a set of real questions. Further iterations improve retrieval and how answers are phrased. Time depends mainly on the state of your documents and how many sources there are. The first prototype shows quickly whether the approach works on your data.
What drives the cost of an LLM and RAG solution?
Key factors are the volume, format and tidiness of documents, access-permission requirements (who may see which content), the number of integrations, the choice of model and its cost per query. We estimate after reviewing your knowledge sources. Choosing between a cloud model and one hosted on your side also changes the budget, and we explain the trade-offs openly.
Will confidential documents go to an external model provider, and can the model hallucinate?
Those are the key questions. Before starting we decide which data may leave your infrastructure, and where that is unacceptable we consider hosting the model on your own infrastructure. We reduce hallucinations by grounding answers in documents, citing sources and testing on real questions, but they cannot be ruled out entirely. Document permissions are respected in retrieval. We follow an ISO/IEC 27001 approach.
How do you keep knowledge current in a RAG system after launch?
Documents change, so we design a process for re-indexing them, either manually or automatically when the source changes. It also helps to measure answer quality on a fixed set of questions, to notice any decline after a model or data change. We can handle maintenance, and the code and documentation belong to you, so you may also continue development on your own.
Get in touch
Tell us about your idea
Describe what you need. We will reply with a proposal for the next steps.