Services
AI Engineering & Automation
We build AI features into your product and automate the work behind your operations with fine-tuned language models, decision engines and agent workflows tied to measurable business results.
How we approach ai engineering
Most AI projects stall between a promising demo and a dependable feature. The gap is engineering: clean data pipelines, evaluation, guardrails, cost control, latency, and a way for people to correct the system when it is wrong. That gap is where we work.
We start from a business problem, such as a support queue that is too long, a manual review step that slows sales, or a product that would be more useful if it understood natural language. Then we design the smallest AI capability that measurably improves it. Sometimes that is a carefully prompted foundation model with retrieval over your documents; sometimes a fine-tuned model; sometimes a classic machine-learning model or a rules engine is the better answer. We judge the work by whether your team keeps using it.
For organisations in Saudi Arabia, we design for Arabic from the start, including Arabic and mixed Arabic/English input and evaluation that accounts for dialects. Data protection requirements such as the Personal Data Protection Law (PDPL) also shape where data is stored and which models can see it.
What we offer
In-product AI features
Assistants, smart search, summarisation, content generation and interpretation built into web and mobile apps, like the dream interpretation engine we built for Manami.
- Anthropic
- Google Gemini
- Hugging Face
- React
LLM integration & fine-tuning
Retrieval-augmented generation over your own data, prompt and model evaluation, and fine-tuning when an off-the-shelf model is not enough.
- Hugging Face
- PyTorch
- LangChain
- Python
AI agents & workflow automation
Agents that read, decide and act across your tools, such as CRM, email, ticketing and ERP, with human approval where it matters.
- LangChain
- n8n
- Zapier
- HubSpot
Decision engines
Scoring, routing and recommendation systems that make or support operational decisions automatically.
- Python
- TensorFlow
- FastAPI
- PostgreSQL
How we work
Opportunity mapping
We identify the processes and product moments where AI can measurably save time or create revenue, and rank them by value and feasibility.
Proof of value
A focused prototype on your real data, with an evaluation set that defines what “good enough” means before we scale.
Production engineering
APIs, data pipelines, guardrails, monitoring, cost controls and fallbacks so the feature is dependable at real traffic.
Adoption & iteration
Training, feedback loops and regular evaluation runs as models, prompts and your data change.
Technology we use
Frequently asked questions
Do we need our own model?
Usually not at first. Most products get the best return from a strong foundation model plus retrieval over your own data. We fine-tune or train custom models only when evaluation shows it is worth the cost.
Can AI features work in Arabic?
Yes. We test with Arabic and mixed-language input and choose models and evaluation sets that handle it well.
How do you keep our data safe?
We design data flows around your security and compliance requirements, including where data is processed and stored, which providers can access it, and how long it is retained.
How do we know it is working?
Every AI feature ships with an evaluation set and production monitoring, so quality, cost and latency are measured continuously rather than judged by impression.
Let’s talk about your ai engineering project
Book a free consultation and leave with clarity, direction and expert advice you can act on straight away.