Data isn't AI-ready
Pipelines exist for BI, not for agents. Embeddings, lineage, and access control are afterthoughts.
Agentic AI · data engineering · enterprise & government
Clean data.
Captured knowledge.
AI that does the work.
We build agentic systems that run real work inside banks, ministries, and national institutions — grounded in your data, governed by your knowledge, and delivered to production across the GCC and North America.
In production across banking · government · accounting · payments · healthcare
Why enterprise AI stalls
The model isn't the bottleneck.
Most enterprise AI projects don't fail at the model. They fail at the four things around it.
Knowledge stays in heads
SOPs live in wikis. Tacit expertise lives in people. Agents have nothing real to reason against.
Agents run unguarded
No policy gates, no citations, no audit trail. Risk and compliance teams say no — and they're right.
The stack changes weekly
New models, new protocols. Anything shrink-wrapped is obsolete by the next quarter.
Our operating model
Three layers. One operating model.
A self-evolving harness that turns enterprise data and knowledge into AI that runs real work. Each layer is earned by the one below it.
Data foundation
Clean, governed, AI-ready data. Pipelines and ingestion, semantic enrichment, lineage, vector and relational stores.
Agentic harness
Business knowledge as guardrails and instructions. Policies, eligibility gates, citations, observability, feedback loops.
AI use cases
Agents that perform measurable work. Production-grade, citation-backed, audit-logged, continuously improving.
Ingest knowledge → compose agents → learn from outcomes
Explore the framework →Bilingual by default
Ask in either language. The answer follows.
Our agents detect the language of the question — Arabic answers come back in Arabic, right-to-left, with the same visible reasoning and the same sourced trace.
How many Muscat residents own both an apartment and a vehicle?
plan identify registries → people · real_estate · vehicles query 3-source join on national_id rows 1 row · 38 ms · read-only
1,284 residents of Muscat currently own both an apartment and at least one registered vehicle. Full query trace attached.
كم عدد سكان مسقط الذين يمتلكون شقة ومركبة معاً؟
plan identify registries → people · real_estate · vehicles query 3-source join on national_id rows 1 row · 38 ms · read-only
١٬٢٨٤ من السكان في مسقط يمتلكون حالياً شقة ومركبة مسجلة واحدة على الأقل. سجل الاستعلام الكامل مرفق.
What we do
From strategy to systems that stay in production.
AI & data consulting
- Strategy and roadmap design
- Technology stack assessment
- AI readiness and governance
- Use-case identification
Solution delivery
- End-to-end pipeline development
- LLM integration and fine-tuning
- Agentic AI orchestration
- Intelligent analytics and modeling
Ongoing support
- Model monitoring and fine-tuning
- Continuous improvement cycles
- System performance tracking
- Long-term AI partnership
Selected work
Agents in production, measured in outcomes.
Clients are referenced by sector and country. The numbers are theirs — and they are exact.
Banking · Qatar
Agentic loan officer
A multi-agent workflow reads salary certificates, cross-checks pay slips, and computes eligibility — on LLMs that never leave the bank. Verification fell from 14 minutes to 4, approvals run 48 hours faster, and tampered documents are flagged with 97% precision.
Read the case → Government · QatarAgentic recruitment flow
Seventeen agents take an applicant from first conversation to shortlist on a national employment platform. Applications dropped from 30 minutes to under 8, and completed applications rose 25%.
Read the case → Accounting · Saudi ArabiaProfessional-standards assistant
Members ask in Arabic or English and get cited answers from the full professional library — grounded in the source paragraph, never invented. It drafts memos and triggers portal workflows too.
Read the case →The shape of the work
2 B+ card transactions analyzed for behavioral targeting
100 M+ Arabic social posts indexed for research
17 agents orchestrated in one production workflow
97 % precision flagging tampered loan documents
Figures from delivered engagements · clients referenced by sector and country
Built on
Microsoft Azure AWS Google Cloud Databricks
Applied-research collaboration with MILA — the Montreal Institute for Learning Algorithms.
How engagements start
Discovery to production, without the leap of faith.
Discovery call
A 30-minute conversation to map your highest-value use cases and the data behind them.
Pilot
A scoped proof on your real data — agreed metrics, a fixed timeline, your environment.
Scale
Production rollout with SSO, audit, and monitoring — and a knowledge-transfer plan, not a dependency.
Let's put your knowledge to work.
Start with the data and the knowledge. The models follow.