What type of companies work with SAUCO?

We work with companies of any type that need custom technology solutions. Our clients are usually in sectors where operational efficiency is critical: logistics, finance, manufacturing, B2B retail, and professional services.

What type of solutions does SAUCO implement?

We develop solutions focused on transforming real operations: process automation and intelligent workflows, critical integrations with APIs and systems, AI applications for daily operations, real-time dashboards and data analytics, and B2B products with cloud-native architecture and enterprise security.

How long does it take to develop a solution?

It depends on complexity, but our FDE (Forward Deployed Engineering) model allows us to deliver value from the first weeks. A functional MVP is usually ready in 4-8 weeks, and complete solutions in 3-6 months. We prioritize incremental deliveries so you see results quickly.

What is the FDE working process like?

We integrate directly into your team. We start with a deep immersion to understand your real operation, not what reports say. Then we build functional prototypes that we validate with you, iterate quickly, and scale what works. It's frontline engineering, not remote development.

What makes SAUCO different from a traditional software factory?

Software factories execute specifications. We question the problem before building. We speak P&L, not just code. Our incentive is aligned with your outcome: we seek the shortest Time-to-Value, not maximizing billable hours.

Do you offer maintenance after development?

Yes, but our goal is that you don't need us. We design maintainable systems, document every decision, and train your team. We offer ongoing support for those who prefer it, but also complete knowledge transfer for teams that want autonomy.

Do you work with startups or only established companies?

We work with both, but with criteria. For startups, we need to see initial traction and a clear problem to solve. We don't do speculative MVPs. For established companies, we look for internal sponsors with decision-making power and a real operational problem that justifies the investment.

RAG for Business: How to Make AI Understand YOUR Business Data (Without Training Models)

Training your own large language model is, for 99% of companies, a disastrous financial and technical decision. It is slow, prohibitively expensive, and worst of all: the model is outdated the moment it's finished. By the time training ends, your operational data has already changed.

The solution being adopted by companies that actually put AI into production isn't fine-tuning, but RAG (Retrieval-Augmented Generation).

At SAUCO we call it "giving the AI books rather than trying to make it learn them by heart."

The Problem: Memory vs. Retrieval

When you interact with a generic LLM (like GPT-4), you're talking to someone who has read almost the entire internet but has no clue who your best customer is, what your current discount policy is, or exactly what that contract you signed yesterday says.

Attempting to solve this through training (Fine-tuning) presents three insurmountable walls:

Opacity: You cannot verify where the model gets its information.
Hallucinations: If it doesn't know something, the model tends to invent it with astonishing confidence.
Privacy: Sending your master data to train external models is a compliance risk that most CFOs are not willing to take.

What is RAG? The Real-World Architecture

RAG changes the paradigm. Instead of relying on the model's "memory," we provide it with a real-time data retrieval system.

The simplified process works like this:

Your question: "What is the return procedure for Platinum customers?"
Search: The system searches your private documents (PDFs, ERP, Database) for the exact fragments that discuss this.
Context: The system hands the LLM the found fragment and instructs: "Based only on this text, answer the question".
Answer: The AI responds with real data, citing the source and without inventing anything.

RAG is, essentially, an open-book exam. And in the business world, we prefer someone with an open book over someone trying to remember a sales figure from six months ago.

Three Strategic Business Advantages

1. Verifiable Truth

Every answer generated by a RAG system can include a direct reference to the original document. If the AI says the margin is 12%, you can click and see the report where that figure came from. Trust is the king metric in operations.

2. Instant Updates

If you change a procedure in your operations manual at 10:00, your AI system already knows about the change at 10:01. No re-training. Data flows from your infrastructure to the AI's response without friction.

3. Privacy by Design

Your data never "trains" the global model. It stays within your infrastructure (or your private cloud). The LLM only sees the fragments necessary to answer a specific query and then "forgets" them.

The SAUCO Angle: Context Engineering

Implementing a RAG is not just about connecting a vector database to an OpenAI API. That's a demo, not an operational tool.

At SAUCO, we approach RAG as a Context Engineering problem:

Data Cleansing: If your documents are a mess, your AI will be a mess. We curate the source.
Granular Security: We ensure the AI only retrieves information the user has permission to see. An intern should not be able to ask about executive committee salaries, even if the AI "has access" to the documents.
Proprietary Orchestration: We design the workflows that decide when to use AI and when to query the ERP directly for exact figures.

AI should not be an experimental toy. It should be the system that allows your team to stop searching for information and start making decisions with it.

Does your company have a mountain of data that no one consults? Let's talk about how to make your AI understand it.