AI consulting agency · Powered by Anthropic Claude
We train. We build. We govern.
Sourcing
Trend detection
Drafting brief
Compliance check
Our services
Every pillar is delivered on the Anthropic Claude stack — Claude.ai web, Claude Desktop, Claude Code CLI, the Claude API with MCP servers — and to Anthropic's published methodologies for prompting, agent design, governance, and safety. Other vendors are introduced only when your stack policy demands it.
Pillar 01
We assess your AI readiness, identify the highest-leverage applications inside your business, build a phased roadmap, and tie every initiative to a measurable business outcome.
Pillar 02
We train executives and operating teams to use AI well — not to take courses, but to ship. Executive immersion plus a Champions cohort that builds and signs off the first production agents alongside your team.
Pillar 03
We build and ship AI assistants, knowledge systems, document-processing agents, and process-automation pilots — engineered for production from day one, not science-project demos.
Pillar 04
Adoption is where AI programmes succeed or stall. We lead the organisational change that AI brings: new ways of working, the capabilities to sustain them, and the structures that make adoption stick across teams.
Our leadership
Ex-EY Partner, ex-Accenture, ex-Deloitte, ex-SAP. 24+ years of experience, 10+ based in MENA. Deep expertise in digital transformation — steering enterprise programmes for Tier-1 clients in Oil & Gas, Utilities, and Financial Services across the US, Europe, MENA, and CIS.
Ex-EY, ex-Deloitte, ex-Accenture, ex-PepsiCo, ex-Abbott. 14+ years of experience, 5+ based in MENA. Deep expertise in HR Transformation, Change Management, and Organisational Development — leading large-scale change programmes for Tier-1 clients in Oil & Gas, Energy, Banking, Auto Retail, FMCG, Pharma, Telecom, and the public sector across MENA, Europe, and CIS.
Ex-EY Associate Partner with 15+ years in professional services, delivering across MENA, Europe, Africa, and CIS. SAP-certified expert in large-scale, technology-enabled business transformation — leading multimillion ERP, BI, and CRM programmes and digital initiatives spanning Big Data, Machine Learning, and Robotics for clients in Oil & Gas, Utilities, Telecom, and Consumer Products.
Our thoughts
Field notes on Anthropic Claude, agent design, and what it really takes to put AI into production — published as we learn it.
Agentic systems are only as good as the context they can reach, and most enterprise knowledge sits locked in silos and unstructured files. This piece is a practical guide for non-technical leaders: how to inventory your internal data, clean and standardize it without a data-science team, and build a shared knowledge base that multi-agent systems can actually use, plus the governance questions that come with centralizing it.
Read the essayVelocity claims are cheap; Fable 5's are measured in shipped work. Stripe migrated a 50-million-line Ruby codebase in a single day, and on scientific frontiers the Mythos 5 line accelerates drug design roughly tenfold — matching or beating skilled human experts. The enterprise takeaway isn't the headline benchmark but the economics: at $10 per million input tokens, the bottleneck shifts from model capability to whether your workflows are built to absorb that much throughput.
Read on LinkedInA raw language model is an engine on a factory floor — it fires, but it can't carry cargo. The harness is the chassis: the loops, tool access, memory, and guardrails that turn a statistical text predictor into a reliable digital worker. For enterprise buyers across MENA, the lesson is that the model is rarely the differentiator. This piece traces a request through a production harness and hands you a buyer's checklist for evaluating AI partners.
Read the essayThe industry remains obsessed with velocity, but the evaluation data for Anthropic's Claude Opus 4.8 suggests we're benchmarking the wrong metrics. Its real value lies in its friction: it flags uncertainties, pushes back on unsound plans, and achieves a 4× reduction in letting flawed code pass unremarked. For high-stakes enterprise workflows, blind compliance is a liability — the breakthrough is a model with the maturity to tell you when you're wrong.
Read on LinkedInContacts
