Custom ML&AI solutions
Build, Deploy & Scale Enterprise AI
We design, operationalize, and scale next‑generation AI solutions,from predictive models to generative and vision systems,on secure, governed data platforms built for the enterprise.
Why Enterprise AI Fails (and How We Fix It)
Most AI initiatives stall because the use case isn’t tied to value, the data isn’t production‑ready, or the model never makes it past the lab. Add growing regulatory pressure (PDPL, GDPR) and new model risks (hallucination, bias, drift) and it’s clear: AI needs engineering, governance, and ongoing care.
We help you deliver AI that works in production and drives measurable outcomes.
Common issues we solve:
01
Pilots that never scale.
02
Models degrading silently in production.
03
Lack of lineage from data → feature → model → decision.
04
Unclear ownership across business, data, and data science teams.
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Use the full lifecycle or engage us where you need help most.
Our AI Delivery Framework
Align ambition to value. Confirm data fitness and regulatory constraints before building.
- High‑ROI use case discovery workshops.
- Data availability / quality spot‑check (foundation review).
- Feasibility & impact scoring matrix.
- Success metrics & business sponsorship alignment.
Rapid, iterative build cycles focused on value and explainability.
- Forecasting & optimization (demand, churn, capacity, risk).
- Recommendation engines (product, content, offer).
- Multilingual NLP & domain LLMs (Arabic + English).
- Computer vision (defect detection, safety, asset tracking).
- Retrieval‑Augmented Generation (RAG) over enterprise knowledge.
Production‑grade operations so models keep delivering.
- CI/CD pipelines (build → test → deploy).
- Feature stores & data versioning.
- Model registry, lineage & governance metadata.
- Automated retraining & drift detection.
- Multi‑environment deployment (batch, API, streaming).
Reduce risk and meet compliance expectations in GCC & global markets.
- Bias & fairness testing.
- Model explainability reporting (regulator / exec friendly).
- Data minimization & consent alignment (PDPL, GDPR).
- Usage policy enforcement & audit logging.
Bring intelligence closer to events and assets.
- Low‑latency inference at edge (manufacturing, logistics, energy).
- Digital twin simulation for operations & maintenance.
- Streaming analytics + model scoring (Kafka / Flink / Kinesis).
Already built something? We’ll evaluate and improve it.
- Performance diagnostics & drift analysis.
- Cost/performance tuning (GPU/CPU mix, quantization, distillation).
- Technical debt review & refactor plan.
- SLA‑backed support roadmap.
Expertise Domains
01
MLOps & AI Ops
02
Generative AI & LLM Integration
03
Computer Vision & Video Analytics
04
Edge AI & Embedded Inference
05
Digital Twins for manufacturing / logistics / infrastructure
06
AI for Climate & Sustainability (energy use, emissions, asset efficiency)
07
Responsible / Regulated AI governance frameworks
08
Quantum ML (R&D / advisory)
Delivery Flow (Fast to Value)
02
Prototype
Prove value with a minimal model or RAG demo.
01
Readiness & Prioritization
Value scoring + data checkpoints.
05
Scale Across Use Cases
Shared components, reusable features, platformization.
04
Drift, feedback loops, retraining, optimization.
Monitor & Improve
03
Production Build
Harden data flows, secure access, deploy with MLOps.
Technology Snapshot
Category
Open Source
Enterprise / Cloud
ML / DL Frameworks
PyTorch, TensorFlow, XGBoost, LightGBM
Databricks ML, SageMaker, Vertex AI
GenAI / LLM
Hugging Face Transformers, Llama, vLLM, LangChain
Azure OpenAI, Amazon Bedrock, Google Vertex AI Models
MLOps
MLflow, Kubeflow, BentoML, Feast (feature store)
Azure ML, SageMaker Pipelines, Tecton
Pipelines & Orchestration
Airflow, Prefect, Kedro
Informatica, Databricks Workflows
Vision
OpenCV, Detectron2, YOLOv8
AWS Rekognition, Azure CV, Google Vision
NLP Multilingual
spaCy, fastText, Hugging Face Arabic models
OpenAI, Anthropic Claude (region routing), AWS Comprehend
Faster path
from PoC to production (weeks vs. quarters).
20–50%
operational cost savings via automation & optimized inference.
Increased revenue
through personalization & forecasting accuracy.
Reduced compliance risk
via governed, explainable AI pipelines.
Business Outcomes
Enterprises typically achieve:
Impact Highlights
See All Case Studies
Retail
Demand + promo forecasting model improved in‑stock planning and drove +6% gross margin in seasonal categories.
+6%
gross margin
Banking
AML alert triage ML reduced false positives by 35% while meeting governance audit controls.
35%
reduced false positives
Energy
Digital twin production optimization reduced energy intensity by 8% across pilot assets.
8%
reduced energy intensity
Logistics
Edge vision model cut damage claim cycle time 40%; retraining automated via MLOps.
40%
cut damage claim cycle time
Engagement Models
02
AI Accelerator
Deliver a production‑ready predictive, GenAI, or CV model with MLOps handoff.
01
AI Readiness Workshop
Prioritize use cases, assess constraints, map value.
04
Managed AI Ops
Monitoring, drift mgmt, retraining, SLA support.
03
Rapid RAG / LLM pilot over internal knowledge bases.
GenAI Lab Sprint
Ready to build AI that scales, performs, and earns trust?
Request AI Readiness Review
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info@s-o-d-a.com
+971 55 130 4590
IFZA, Dubai Silicon Oasis, Dubai, UAE
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