What LLM services does STEPS offer?

We provide end-to-end LLM solutions including fine-tuning open-source models (such as Llama, Mistral, and others) on your proprietary data, building RAG (Retrieval-Augmented Generation) pipelines, developing custom AI-powered chatbots, and creating internal knowledge assistants that respond to queries specific to your organization's information.

Can you fine-tune an LLM on our company's internal data?

Absolutely. We specialize in fine-tuning large language models on custom datasets so the model understands your business context. For example, we fine-tuned Llama 3 for Afreximbank to respond to inquiries specific to the bank's internal information, combining conversational queries with database-level retrieval.

What industries can benefit from your LLM solutions?

Our LLM services apply across multiple sectors including banking and finance, insurance, healthcare, retail, e-commerce, energy, manufacturing, transportation, and communications. Whether you need automated document processing, intelligent customer support, or internal knowledge management, we tailor the solution to your industry.

Do we need to share our sensitive data externally?

Data security is a priority. We can deploy LLM solutions on-premise or within your private cloud environment, ensuring your proprietary data never leaves your infrastructure. We also offer advisory services to help you define the right data governance strategy before any project begins.

What is RAG, and why would we need it?

RAG (Retrieval-Augmented Generation) combines the power of LLMs with your existing knowledge base. Instead of relying solely on what the model was trained on, RAG retrieves relevant documents from your data in real time and uses them to generate accurate, context-aware responses. This is ideal for organizations that need up-to-date, factual answers drawn from internal repositories.

How long does it take to implement an LLM solution?

Timelines vary depending on scope. A proof of concept can typically be delivered within 4 to 6 weeks. A full production-ready solution, including data preparation, fine-tuning, testing, and deployment, generally takes 2 to 4 months. We follow an Agile process with regular sprint reviews so you see progress throughout.

LLM Application Development | RAG, AI Chatbots, GPT Integration

Artificial intelligence that runs in production. Not the kind that impresses in demos.

85% of AI projects launched worldwide never make it to production. The remaining 15% are the ones that matter. We design, build and operate AI systems that make real decisions, on real data, with measurable outcomes, across Africa, the Mediterranean and beyond.

AI AT STEPS IN NUMBERS
AI expertise isn’t proven in slides. It’s proven in production.

Since 2014, we’ve integrated AI at the core of our deliverables. Here’s what that represents concretely today.

Modeles AI en production

+80 %

Projets livrés

30 +

Industries couvertes

8 +

OUR APPROACHThree principles that separate AI that holds from AI that shines in meetings

We delivered our first ML production in 2014. Since then, we’ve watched dozens of AI projects get excited in POC phase and die at the production handover. We’ve drawn three principles that now guide every engagement.

Data first, not model first

60% of failed AI projects fail because of data quality, not model choice. We audit your data sources before any ML architecture discussion. If quality isn’t there, we build it first, otherwise the most sophisticated model will always produce mediocre results.

Sovereignty is not optional

For banking, insurance, public sector and healthcare, your data cannot leave your environment. We deploy latest-generation open-source models (Mistral, Llama, Qwen) on your private cloud or on-premise. Same performance as GPT-4 on most use cases, full sovereignty.

MLOps before
ML

A model trained in a Jupyter notebook is worthless. We industrialize from POC: reproducible data pipelines, model versioning, production drift monitoring, scheduled automated retraining. This discipline is the difference between a model that lasts and one that collapses after 3 months.

OUR DOMAINS Five AI expertise domains we cover in depth

Predictive ML

Real-time fraud detection, credit scoring, churn prediction, customer segmentation, demand forecasting, predictive maintenance.

Generative AI

Enterprise chatbots, RAG on document bases, business copilots, multi-step autonomous agents, regulatory document generation.

Computer Vision

Industrial quality control, document recognition, automated reading of checks and forms, intelligent monitoring.

Document Intelligence

Information extraction from PDFs, contracts, invoices. Automatic classification. Regulatory summaries. Semantic search on archives.

Autonomous Agents

Automation of complete business processes, multi-system orchestration, conditional decision-making, automated customer interactions.

OUR METHODFrom business hypothesis to production model, in 4 disciplined stages

We rebuilt our AI methodology in 2023, after observing that the classic ML-plus-delivery approach wasn’t enough for projects expected to hold up beyond 6 months in production. The four stages below are what replaced it, each designed to protect a different failure mode we’ve seen kill AI initiatives in the wild.

PHASE 01

DATA AUDIT
2-3 weeks

Audit of available data sources, quality assessment, gap identification, definition of business success metrics. Deliverable: a written go/no-go report that recommends proceeding, correcting data first, or stopping.

PHASE 02

POC
3-5 weeks

Build of a baseline model on your real data (not a toy dataset). Benchmark of 3 to 5 technical approaches. Results measured against stage-1 success metric. Explicit go/no-go decision at end.

PHASE 03

Industrialisation
4-10 weeks

Production deployment with data pipeline, API, monitoring, logs, integration to business systems. Load testing. Full documentation. Training of your teams so they can operate without us.

PHASE 04

MLOps

Production model drift monitoring, scheduled retraining, adjustments based on user feedback, infrastructure cost optimization, evolution to new use cases. The stage that makes a model last 5 years, not 5 months.

You have an AI idea but don’t know where to start?

Most of our clients start here. A 60-minute call to explore your context, identify the highest-ROI use cases, and receive a POC plan within 5 business days. No obligation, no commitment.

CASE STUDIESOUR WORK SPEAKS FOR ITSELF

Explore our portfolio of bold and impactful projects, designed to inspire and deliver excellence.

Edtech

LMS - Learning Management System

Travel & hospitality

Travel management platform

AI & Data

Moost RFM

FAQFREQUENTLY
ASKED QUESTIONS

Do we have enough data to do AI

Probably yes. For LLM applications (chatbots, RAG, copilots), 500 to 1000 internal documents suffice. For serious predictive ML, 10,000 observations minimum. For computer vision, 500 annotated images per class. The first-stage data audit answers precisely.

How much does an AI project cost?

Very variable depending on ambition. A targeted POC takes 3 to 5 weeks. Full industrialization 3 to 6 months. Exact budget depends on scope, data quality, sovereignty requirements. Precise quote within 5 business days after the scoping session.

Will our data end up at OpenAI?

Only if you accept it. We can deploy everything on-premise or on your private cloud with open-source models (Mistral, Llama) that reach 90% of GPT-4 performance on most use cases. Enterprise versions of OpenAI and Anthropic also have zero retention — your data doesn’t train their models.

How do you avoid the abandoned POC trap?

Four rules. Business success metrics defined before any line of code. POC on real production data, not toy datasets. Production architecture considered from POC stage. An identified business sponsor who will actually use the system — not just an IT sponsor.

What is AI sovereignty and why does it matter?

It’s the ability to operate an AI system without depending on external parties (OpenAI, Google, etc.) for model access or data processing. Critical for banking, insurance, healthcare, public sector, and mandatory in some countries (Tunisian decree 2025). We systematically deploy open-source models on client infrastructure in these cases.

How do you measure AI project ROI?

Three metrics per project. Business metric (fraud detection rate, average processing time, conversion rate). Technical metric (precision, recall, latency). Financial metric (ROI, infra cost, cost per inference). All defined before POC to guarantee alignment.

What open-source models do you work with?

We work with a range of open-source LLMs including Llama 3, Mistral, Gemma, DeepSeek, and others. We evaluate which model best fits your use case based on performance, cost, language support, and deployment constraints, and we help you choose the most suitable tech stack for each project.

Do you offer ongoing support after deployment?

Yes. We provide post-deployment maintenance, model retraining as your data evolves, performance monitoring, and continuous optimization. Our CTO-as-a-Service and dedicated team models ensure you always have access to expert support.

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