Generative AI & LLM Solutions
Generative AI that's actually production-ready.
From Riyadh, we build LLM-powered assistants, copilots, and automations grounded in your data, with the guardrails and evaluation that keep them accurate.
Beyond the demo: grounded, evaluated, safe
We connect models to your knowledge with RAG, add guardrails and human review, and measure quality continuously, so your AI is trustworthy in front of real users. Because every change is checked against an evaluation set, you can ship improvements with confidence instead of crossing your fingers.
Core outcomes
What you can expect.
Grounded answers
Retrieval keeps responses tied to your real, current data, not hallucinations.
Hours back per week
Automate research, drafting, and support tasks that drain your team.
Safe by design
Guardrails, redaction, and human-in-the-loop where the stakes are high.
Measured quality
Evaluation pipelines catch regressions before your users do.
Cited and auditable
Every response can show its sources, so users and reviewers can trust and verify it.
Cost under control
Caching, model routing, and right-sizing keep token spend predictable as usage grows.
Use cases
Where it pays off.
Representative examples of the work this practice delivers.
Support copilot
A RAG assistant that drafts accurate replies from a company's help center and tickets.
Knowledge search
Natural-language search across thousands of internal documents with cited sources.
Document automation
Auto-extraction and summarization of contracts for a services firm.
Sales enablement
An assistant that drafts proposals and answers product questions from approved content.
Onboarding assistant
A guided in-app copilot that answers setup questions straight from the docs.
Meeting to action
Summaries and action items pushed automatically from call transcripts into the CRM.
How we deliver
A clear path to outcomes.
A repeatable, transparent workflow, so you always know what's happening and what's next.
Frame
We pick a high-value use case and define what 'good' looks like.
Ground
We connect the model to your data with retrieval and guardrails.
Pilot
We put it in front of a small group, gather real prompts, and tighten the rough edges.
Evaluate & ship
We measure quality against real examples, then launch with monitoring.
Improve
We refine prompts, retrieval, and guardrails from production signals.
What we deliver
Capabilities & tooling.
In this practice
- LLM Apps & Copilots
- Retrieval-Augmented Generation (RAG)
- AI Agents & Automation
- Prompt & Evaluation Pipelines
- Private & On-Prem Models
Tech & tools we use
The 'impressive demo, risky launch' problem
LLM prototypes dazzle, then fail on edge cases and hallucinations. We engineer the retrieval, evaluation, and guardrails that make them dependable.
FAQ
Common questions.
Can we use our own private data securely?
Yes. We can deploy with private or on-prem models and keep your data inside your own environment.
How do you prevent hallucinations?
Retrieval grounding, strict prompts, guardrails, and evaluation sets that flag low-confidence answers.
Which models do you use?
Whatever fits the task and budget, from hosted APIs like Anthropic and OpenAI to open models you self-host.
How long until we see something working?
Usually a working pilot in 3 to 6 weeks, grounded in a slice of your real data.
Will it integrate with our existing tools?
Yes. We connect to your knowledge bases, CRMs, and apps over their APIs, with authentication handled.
What does it cost to run?
We design for predictable token spend with caching and model routing, and give you usage dashboards from day one.
Ready to put this to work?
Tell us where you are today. We'll map the fastest safe path to where you want to be.
Get in Touch