Big-four consultancies
Bill at a premium and deliver strategy presentations, not working software. When they do build, they subcontract it — adding cost and losing quality. The result is an expensive proof-of-concept that never reaches production.
AI Strategy · Engineering · Deployment
Sowing intelligence infrastructure across industries
Consulting, design, building and maintaining
SOW takes your idea from concept to a deployed, production-ready AI product — then hands you the keys. Discovery, design, data, development, deployment, handover. One team, one accountable outcome.
The gap we fill
AI has never been more powerful. Most companies still can't turn it into a working product.
The reason is consistent across industries: the firms that understand your business don't build, and the firms that build don't understand your business. SOW sits at the intersection — deep technical AI capability with full-product thinking.
Outsourcing & staff augmentation
Deliver fragmented code without business context, or supply individual engineers with no ownership of the outcome. You get contractors and tickets — not a product and not a system that holds together.
Internal hiring
Slow, expensive, and it usually produces isolated engineers without the breadth to architect a full system. Twelve months later you have a team, and still no product in front of users.
Technical capability
The full AI stack, without coordinating five vendors.
SOW selects the right approach for each problem — traditional ML, deep learning, agentic AI, or a hybrid — based on what the problem actually needs, not what is trending.
01
Traditional ML & Data Science
Predictive analytics, classification, clustering, regression, NLP, recommendation engines, time-series forecasting.
Churn predictionFraud detectionDemand forecasting
02
Deep Learning & Computer Vision
CNNs, RNNs, transformers, image classification, object detection, semantic segmentation, medical imaging, document processing.
Quality inspectionMedical screeningDocument OCR
03
Agentic AI & LLM Solutions
AI agents with reasoning and tool use, RAG systems, multi-agent orchestration, LLM-powered copilots, workflow automation.
Service agentsKnowledge basesProcess automation
04
IoT + AI Integration
Edge computing, sensor fusion, embedded ML, real-time monitoring, hardware-software integration. Few competitors combine both.
Smart agricultureIndustrial monitoringHealth devices
05
MLOps & Infrastructure
Model deployment, monitoring, retraining pipelines, cost optimisation, cloud architecture, CI/CD.
Drift detectionScalable inferenceA/B testing
06
Data Engineering
Pipeline design, ETL/ELT, data quality frameworks, warehouse architecture, real-time streaming.
AI readinessData migrationCompliance
The SOW product development lifecycle
Six phases. One team. You keep the product.
A client comes to SOW with a problem — or even just a vague idea — and leaves with a fully deployed, production-ready AI product. Then we hand over the keys.
01
Discovery
Understand the business problem, assess AI feasibility, define scope, identify data requirements, estimate ROI.
Feasibility reportScope documentROI model
1–2 weeks
02
Design
System architecture, model selection strategy, UX/UI design, data pipeline design, API specifications.
Architecture diagramWireframesProject plan
1–3 weeks
03
Data
Data collection, cleaning, labeling, augmentation, pipeline setup, quality assurance.
Clean datasetsData pipelineQuality metrics
2–4 weeks
04
Development
Model training, backend and frontend engineering, API development, integration with your existing systems.
Trained modelsApplication codeIntegration layer
4–12 weeks
05
Deployment
Cloud or edge deployment, CI/CD pipelines, monitoring, load testing, security hardening.
Deployed productMonitoringSecurity audit
1–3 weeks
06
Handover & Support
Full documentation, team training, knowledge transfer, and an optional maintenance retainer.
Technical docsTrainingOptional SLA
1–2 weeks
Selected work
Products in the field, not pilots on a shelf.
AgricultureIoT + AI
Eazy Farms — precision agriculture for smallholder markets
Crop monitoring, yield prediction, and disease detection built on combined sensor and vision data. The engagement is the reason SOW's IoT + AI integration capability exists as a first-class service line rather than an add-on.
IoT+AI
Sensor fusion & edge ML
Africa
Primary deployment market
HealthcareComputer Vision
Idoni — AI scoliosis screening with King Faisal Hospital
A clinical-environment screening product that gives SOW healthcare AI credibility most startups and dev shops cannot claim. Healthcare is our strongest entry vertical precisely because we have shipped into it.
Clinical
Real hospital deployment
Vision
Medical imaging pipeline
Where we go deep
Five industries where we already know the terrain.
Healthcare
Medical imaging AI, patient monitoring, clinical decision support, drug discovery tooling.
Finance & Banking
Fraud detection, credit scoring, customer analytics, compliance automation.
Agriculture
Crop monitoring, yield prediction, disease detection, precision farming, supply chain.
Education
Adaptive learning platforms, automated grading, student analytics, content generation.
Retail & E-Commerce
Recommendation engines, inventory optimisation, dynamic pricing, customer service agents.
Your industry
SOW is horizontal by design. If the problem is real and the data exists, we can scope it.
Tell us about it →Let's build
Bring us a problem. Leave with a product.
Start with a Discovery engagement — one to two weeks, a feasibility report, and an honest answer about whether AI is the right tool for your problem.