Models trained on your data, running in production.
Forecasting, scoring, ranking and anomaly detection — built from your historical data and deployed with the pipelines needed to keep them accurate.
- Custom models
- MLOps
- Monitoring
Custom models for prediction, scoring and recommendation, deployed and monitored.
From notebook to dependable service.
Most machine learning value is lost between the experiment and the deployment. We treat training pipelines, feature stores and monitoring as first-class deliverables.
Why it matters
Custom models make sense when your data is the advantage. The hard part is not training — it is data quality, evaluation and keeping the model useful in production.
What we build
Forecasting, recommendation, scoring and classification models, computer-vision pipelines, feature stores and full MLOps delivery.
Who it is for
Businesses with meaningful historical data, marketplaces, and operations teams needing prediction rather than generic language output.
What we handle from strategy to delivery.
Six areas we take responsibility for on ai/ml solutions engagements — no handoff gaps between them.
- 01
Problem & data assessment
Whether the data supports the prediction you need.
- 02
Data pipeline engineering
Ingestion, cleaning, labelling and feature storage.
- 03
Model development
Baselines first, then iteration against a held-out set.
- 04
Evaluation & validation
Metrics tied to business impact, not just accuracy.
- 05
Deployment & serving
Batch or realtime inference with versioned models.
- 06
Monitoring & retraining
Drift detection, scheduled retraining and rollback.
Standards we hold every ai/ml solutions project to.
- Measurable accuracy
- Scored against business metrics
- Reproducible
- Versioned data and models
- Production ready
- Monitored inference
- Retrainable
- Pipelines, not one-off notebooks
From first conversation to a product that is ready to grow.
01 — Discover
We map the workflow, the data available and the measurable outcome AI is supposed to improve.
We review requirements, existing analytics, competitors and user needs to understand where ai/ml solutions will create the most value. Nothing is proposed before the problem is clear.
Deliverables
- Use-case assessment
- Data inventory
- Success metrics
Typical activities
- Workflow interviews
- Data review
- Feasibility check
Success criteria
A clear, shared understanding of the problem, scope and expected outcome.
02 — Define
We decide what should be deterministic software and what genuinely benefits from a model.
We turn research into a clear product direction, priorities and information architecture. Scope, sequencing and technical direction are agreed in writing before work starts.
Deliverables
- Solution design
- Evaluation criteria
- Cost model
Typical activities
- Architecture design
- Provider comparison
- Risk assessment
Success criteria
Everyone understands what is being built, in what order, and why.
03 — Design
We design the product surface so people can understand, correct and trust the output.
We translate the agreed structure into a polished, responsive interface — every state, breakpoint and edge case included, reviewed together as we go.
Deliverables
- Interaction design
- Review and override flows
- Prompt or model spec
Typical activities
- Flow design
- Guardrail definition
- Stakeholder review
Success criteria
The experience is validated and ready for implementation.
04 — Build
We implement the pipeline, integrations and interfaces with cost and latency treated as requirements.
We turn approved designs into production-ready software using maintainable components and a scalable architecture. You see working software throughout, not just at the end.
Deliverables
- Working pipeline
- System integrations
- Guardrails
Typical activities
- Development
- Prompt or model iteration
- Integration testing
Success criteria
The product works reliably across the required devices and scenarios.
05 — Validate
We score the system against a real evaluation set instead of relying on impressions.
We test the product against the real requirements, profile performance and surface issues before launch rather than after it.
Deliverables
- Evaluation results
- Regression test set
- Accuracy baseline
Typical activities
- Test-set construction
- Scoring runs
- Failure analysis
Success criteria
Critical issues are resolved and the product is ready for launch.
06 — Launch
We release with monitoring for quality, spend and drift, then iterate on real usage.
We deploy, review the built product in production and refine the details that only appear in the real thing. Monitoring and handover happen at the same time.
Deliverables
- Production deployment
- Monitoring dashboards
- Feedback capture
Typical activities
- Rollout
- Cost tuning
- Continuous evaluation
Success criteria
The product is live, verified, documented and ready for users.
Everything handed over, nothing locked away.
Concrete output at the end of a ai/ml solutions engagement — code, assets and documentation you own.
The stack we reach for first.
Chosen per project constraints — this is the starting point, not a rule.
Backend
- Python
Data
- MLflow
- Airflow
AI
- scikit-learn
- PyTorch
- AWS SageMaker
AI products that do real work.
Selected projects built with the same approach, team and standards.
AI Background Remover SaaS
SmartBG Remover
A subscription SaaS that removes image backgrounds in seconds, with batch processing and an API for developers.
- Next.js
- Python
- AWS
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Pocket MD
A clinic management system covering appointments, patient records, prescriptions and billing in one workspace.
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E-Learning Platform
A course platform with video lessons, progress tracking, assessments and instructor analytics.
View projectOutcomes, not just output.
Clients stay because the work reduces risk and cost after launch, not only because it looks good at handover.
- 01
Less rework
A feasibility phase prevents building on unusable data.
- 02
Faster decisions
A baseline model within weeks sets a realistic ceiling.
- 03
Better performance
Latency and cost of inference designed in.
- 04
Clear handoff
Documented pipelines, experiments and metrics.
- 05
Long-term thinking
Retraining and drift monitoring from the first release.
Questions we get asked.
Still unsure about something on ai/ml solutions? Ask us directly — we answer honestly, even when the answer is no.
It depends on the problem. We run a feasibility check on your existing data before committing to a build.
We will say so. Often a well-tuned rule or better reporting solves the problem at a fraction of the cost.
Either your team using our pipelines and documentation, or us under a support agreement.
Often yes — where an API solves it well, we say so rather than training something custom.
Drift monitoring, scheduled evaluation and a retraining pipeline with rollback.
Your cloud account, in a container or managed inference service you own.
Often delivered together.
AI Integration
Large language models embedded into your product with guardrails and evaluation.
View serviceAI Automation
Document, support and back-office workflows automated with human review built in.
View serviceAPI Development
Versioned, documented APIs and webhooks that other teams can build against.
View service
Ready to build something better?
Tell us what you are trying to build. We'll help you figure out the right next step — scope, sequence and what it realistically takes.
Have a project in mind?
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