Custom models for the problems off-the-shelf AI can't solve
Predictive models, recommendation engines, and computer vision trained on your data and deployed with the MLOps to keep them accurate in production.
Not every AI problem is a language problem. Forecasting demand, spotting fraud, recommending products, detecting deepfakes, or segmenting satellite imagery all need models trained on your own data, for your own objective.
We handle the full lifecycle: data preparation and labelling, model selection and training, rigorous validation, and deployment behind an API with monitoring for drift. Models are delivered with the pipelines to retrain them, so accuracy holds up as the world changes.
What's included
Everything Machine Learning & Computer Vision covers — from idea to a system running in production.
Computer vision
Image and video classification, object detection, segmentation, and media authenticity analysis.
Predictive analytics
Forecasting, churn and risk scoring, and anomaly detection on your business data.
Recommendation engines
Personalized product and content recommendations driven by user behaviour.
NLP models
Classification, entity extraction, and sentiment analysis tuned to your domain.
Data preparation & labelling
Cleaning, feature engineering, and labelled datasets that make models trainable.
MLOps
Training pipelines, model registries, deployment, and drift monitoring in production.
Where it pays off
Common places teams put Machine Learning & Computer Vision to work.
Deepfake & manipulation detection
Analyze audio and video to assess authenticity with confidence scores and explanations.
Satellite & land-cover analysis
Segment imagery to measure green cover and model its relationship with air quality.
Personalization & recommendations
Recommend the right products and content to each shopper based on behaviour.
Vehicle search & evaluation
ML-assisted search and valuation across large, fast-moving auction inventories.
How an engagement runs
A typical timeline — scoped to your workflow in the first conversation.
- 1Week 1–2
Data & baseline
Audit available data, define the target metric, and build a simple baseline.
- 2Week 2–5
Model development
Feature engineering, model training, and validation against held-out data.
- 3Week 5–8
Deploy
Serve the model behind an API and integrate it into your product or dashboards.
- 4Ongoing
Monitor & retrain
Track drift and accuracy, and retrain on fresh data on a schedule.
How we de-risk it
AI projects fail for predictable reasons. We design those failure modes out from the start.
Baseline first
Every model must beat a simple baseline to earn its complexity.
Explainable outputs
Confidence scores and explanations so users understand why a prediction was made.
Built to retrain
Pipelines delivered with the model so accuracy doesn't decay over time.
Built by AwaitSol
Real Machine Learning & Computer Vision work from our portfolio.
FakeXpose
An AI-powered deepfake detection platform — analyzes audio and video to tell real media from fake, with real-time verification, explainability, and confidence scoring.
AirQualify
A computer-vision platform that segments urban blue-green areas, correlates them with the Air Quality Index, and recommends afforestation zones to improve city air.
FloraNet
A computer-vision model that classifies plant imagery into shrubs and trees, trained on a diverse labeled dataset spanning multiple environments.
OneAuctionView
Centralized auction-management software with ML-assisted search and evaluation of vehicles across all wholesale auctions — an industry-agnostic solution.
Hi-Tec
AI-driven enhancements for the outdoor and sports gear retailer's e-commerce platform — product recommendations, customer behavior analytics, and A/B-tested UX optimization.
Bumpa
Omnichannel business management with AI-powered sales analytics — unifying in-store sales with WhatsApp, Facebook, Twitter, and Instagram channels in one dashboard.
Technologies we use
Ways to work with us
Start with a pilot, bring in a full AI team, or add AI engineers to your own.
AI Pilot
A fixed-scope pilot around one workflow and one success metric — the fastest way to prove AI works for you.
- Fixed scope and price
- One workflow, one metric
- Production-ready, not a throwaway demo
Best for: Teams proving the case for AI
Talk to us about thisDedicated AI Team
A cross-functional squad of AI engineers, full-stack developers, and a delivery lead working as an extension of your team.
- AI + full-stack skills in one team
- Weekly releases and demos
- Scales up or down with your roadmap
Best for: Companies building AI products
Talk to us about thisAI-Enabled Engineers
Senior AI and LLM engineers embedded in your existing team to add capability fast — with knowledge transfer built in.
- Engineers who have shipped AI
- Works in your tools and process
- Monthly, flexible engagement
Best for: Teams with a roadmap but no AI engineers
Talk to us about thisMachine Learning & Computer Vision FAQs
Ready to find your first AI win?
A 30-minute call with an AI engineer — a straight answer on what to build, no sales pitch.
Other AI services
AI Consulting & Strategy
Find the AI use cases worth building — and a roadmap to ship them.
AI Agent Development
Custom AI agents that research, decide, and act across your tools.
Generative AI & LLM Apps
Production LLM features grounded in your own data.
Chatbots & Voice AI
Conversational AI for support, sales, and bookings — on every channel.
Intelligent Process Automation
Automate documents, approvals, data entry, and follow-ups.