Scout
ResearchMaps your data, systems and constraints before any code is written.
Senior AI engineering · Fixed price
Seven AI agentsdo the work.The eighthsigns it off.
We are a senior AI engineering team. We build production AI systems for companies: RAG assistants, agents, evaluation and MLOps. AI tools speed up the routine work, and our engineers make every decision and sign off every deliverable.
Delivered in English and Arabic
08 · signed off
Our team
Seven stages. One senior team behind all of them.
Every project moves through the same eight segments as our mark. Our engineers use specialised AI tools at each stage to move faster, and the eighth segment is the team itself: the people who decide, review and sign off.
Scribe
ProposalsTurns the discovery call into a scoped, fixed-price proposal within 48 hours.
Architect
DesignDrafts the system design: retrieval, models, infrastructure and cost.
Builder
EngineeringWrites the pipelines, APIs and integrations to the approved design.
Tester
EvaluationBuilds eval sets and measures accuracy, latency and failure cases in both languages.
Shipper
DeploymentDeploys to your cloud and region, with CI/CD, logging and rollback.
Watcher
MonitoringTracks quality, drift and spend after launch, and reports every week.
The Eighth Agent
Our senior engineering teamRuns every client call, makes the architecture decisions, reviews all work and signs it off. Nothing ships without it.
Fixed
Fixed price
You know the cost before we start. Scope changes are quoted, never billed by surprise.
100%
Senior review, always
Every deliverable is reviewed and signed off by a senior engineer on our team.
3–4 wk
Weeks, not quarters
AI tools take care of the repetitive work, so an MVP typically lands in 3–4 weeks.
EN + AR
English and Arabic
Arabic-first retrieval, evaluation and interfaces, built in-house in both languages.
Services
Five fixed-price offers
Every engagement has a fixed price, agreed before we start. Start with the one-week Readiness Sprint, or book a call and get a written quote within 48 hours.
AI Readiness Sprint
Use-case shortlist, data audit, architecture sketch and a costed roadmap. The best place to start.
- Use-case shortlist
- Data audit
- Architecture sketch
- Costed roadmap
$2,500Book the sprint
RAG / Agent MVP
A working assistant or agent on your data, with evals, deployed in your cloud.
Fixed pricequoted in 48h
Production Hardening
Take an existing prototype to production: evals, guardrails, latency, cost, monitoring.
Fixed pricequoted in 48h
Fractional Head of AI
Senior AI leadership for teams without an in-house lead: strategy, hiring, vendor and architecture calls.
Fixed monthlyquoted in 48h
Managed AI Ops
We monitor your AI systems and send a weekly quality and cost report, reviewed by our team.
Fixed monthlyquoted in 48h
50% upfront, 50% on sign-off. Every quote follows a free 30-minute discovery call.
Use cases
Problems our team has already solved
Real projects from our engineers' work across the GCC, the US and Europe. Client names stay private. Each one ends with a tip you can use today, whether or not you work with us.
Government · GCC01
Arabic document assistant for a public-sector body
The problem
Staff spent hours searching thousands of Arabic and English regulations, circulars and reports.
What we built
A retrieval assistant that answers in Arabic or English, cites the exact paragraph, and runs inside the client's own cloud region.
- RAG
- Hybrid search
- Arabic NLP
- Azure
Pro tip
Normalise Arabic text before you index it: unify alef and hamza forms, and remove tatweel and diacritics. Then combine keyword and vector search. Vector search alone misses exact legal terms and names.
Healthcare02
Computer vision on clinical video
The problem
Hours of procedure video with no easy way to find key moments or measure what happened.
What we built
Models that detect and tag key events in the video, so clinical teams can jump straight to the moments that matter and review them.
- Computer vision
- PyTorch
- MLOps
- AWS
Pro tip
Before tuning any model, have two experts label the same sample and compare. If they disagree, your model can't beat them. Fix the labelling guide first.
Energy & retail · GCC03
Analytics across a national network of fuel stations
The problem
Hundreds of sites, each with its own cameras and sales data, and no single view of operations.
What we built
Vision models that turn camera feeds into events (queues, vehicle counts, safety issues), joined with sales data in one dashboard.
- Computer vision
- Edge inference
- Spark
- Dashboards
Pro tip
Run the model on-site and send events, not video. Bandwidth drops sharply, and footage never leaves the site, which makes privacy approvals far easier.
Retail · US & Europe04
Demand forecasting for global fashion brands
The problem
Forecasts built in spreadsheets, disconnected from inventory and too slow to update each season.
What we built
A forecasting pipeline on a cloud data platform that refreshes every week and feeds planning and replenishment.
- Databricks
- Spark
- Forecasting
- MLflow
Pro tip
Always compare your model against a naive baseline, such as last year's sales for the same week. If it can't clearly beat that, it isn't ready for planners.
HR tech05
Fair matching between candidates and jobs
The problem
Recruiters screening large volumes of CVs by hand, with a real risk of bias.
What we built
An LLM-based matching service that explains why each candidate fits a role, with a recruiter always making the final call.
- LLMs
- Embeddings
- Bias testing
- Python
Pro tip
Remove proxy features such as names, photos, addresses and graduation years. Then compare match rates across groups before launch, not after complaints.
Operations06
AI agents for back-office workflows
The problem
Repetitive multi-step work across email, documents and internal systems that tied up skilled staff.
What we built
Agents that read requests, pull data from internal systems, draft the output and hand it to a person for approval.
- LangGraph
- LLMs
- Tool calling
- Evals
Pro tip
Give each agent a small set of tools, a step limit and a human approval step for anything that can't be undone. Log every tool call, so you can replay and fix failures.
How an engagement runs
From first call to signed-off system
- 01Our team
Discovery call
30 minutes on your goals, data and constraints. Free.
- 02Our team
Fixed-price proposal
Scope, timeline and price in writing within 48 hours.
- 03Engineers + AI tools
Build
We design, build and test. You get a short status report every week.
- 04Senior review
Review and sign-off
Every line and every eval result is checked before handover.
- 05Our team
Handover or AI Ops
Full documentation, or we keep watching it for you.
GCC
AI that understands Arabic first.
GCC organisations that need Arabic-first AI
- Assistants and search over Arabic and English documents
- Deployment in-region to meet data residency rules
- Evaluation sets built for Arabic, dialects included
US · EU
No AI lead? Now you have one.
Mid-market teams without an AI lead
- A first production AI feature, shipped in weeks
- A prototype that works in demos but not in production
- Senior AI judgment without a full-time hire
How we handle your data
Open about our tools. Careful with your data.
- ◇We tell you where AI is usedYou always know where AI tools were used and which engineer reviewed the work.
- §Data processing agreementsWe sign a DPA before touching any client data.
- ◎Your region, your cloudWe deploy where your data must stay, including GCC regions.
- ✓No data in tools without consentYour data goes into an AI tool only when you have agreed to it in writing.
FAQ
Questions clients ask
Who is responsible for quality?
Our senior engineers. Nothing is delivered until it has been reviewed and signed off by a senior engineer, and that sign-off is part of every contract.
Why are you cheaper than a traditional consultancy?
AI tools handle much of the research, drafting, scaffolding and first-pass testing that usually takes junior hours. We pass part of that saving on as lower fixed prices.
Can you work with our existing team?
Yes. We often work alongside in-house engineers, or as a white-label partner for agencies that need senior AI delivery.
What time zones do you cover?
Calls during GCC business hours, and in the evening for US and European clients.
Start here
Book a 30-minute
discovery call.
Tell us what you want to build. You get a fixed-price proposal within 48 hours, whether or not you go ahead.
Pick a time in the calendarCalls in English or Arabic
NewSocial media agentification
Agentify your social media.
We build an automated pipeline that turns one brief into a week of on-brand posts and AI-generated videos, in English and Arabic. Agents do the research, writing, editing and scheduling. Your team approves every post before it goes out.
3 mistakes that slow down your AI rollout
Mistake #1: starting with the model, not the problem.
What runs every week
Agents scan your industry, competitors and past posts to suggest what to talk about this week.
Hooks, scripts and captions in your brand voice, in English and Arabic.
Short videos with AI visuals, voice-over and your brand kit: logo, colours and fonts.
Cut for Reels, TikTok, Shorts and LinkedIn, with burned-in subtitles.
You review the week's batch in one place, then edit or approve with one click.
Posts go out at the best times, and results feed into next week's plan.
The agentification process
How we turn your content workflow into an agent pipeline, step by step.
Map
We map how content is made today: who writes, who designs, what needs approval, and where the time goes.
Decide
We choose which steps agents take over and which stay with your team. Brand, approvals and anything sensitive stay human.
Build
We connect the pipeline to your brand kit, video generation tools and scheduler, and tune it on your best past posts.
Run
Agents produce a weekly batch and you approve it. Every week the pipeline learns from what performed.
Typical setup: 2–3 weeks · Fixed price, quoted in 48h
Your accounts stay yours. Nothing is published without your approval.