Services
AI & ML Solutions
ML and AI systems scoped for production constraints: data quality, inference cost, monitoring, and maintainability, not notebook prototypes.
Our public case studies focus on operational software; AI/ML engagements are scoped individually after a data and feasibility assessment.
Who this is for
Many AI projects stall not because the model is wrong, but because production constraints were never defined. A prototype in a notebook is not a service. Turning a model into something operable requires data pipelines, inference infrastructure, monitoring, fallback logic, and cost controls.
- You have an AI prototype that works in demos but fails under real load or real data
- You have no monitoring for model drift, degradation, or silent failures
- Inference is too slow or too expensive for your actual usage volume
- Your data pipeline is a manual process that breaks when anyone is on holiday
- You're relying on a vendor LLM API and have no fallback if it changes pricing, behavior, or availability
- You need to fine-tune or train a model on your own data but don't have the infrastructure to do it
What we deliver
AI features scoped for production use: monitoring, fallbacks, and operational constraints defined with you, not one-off demos in a controlled environment.
ML model development
Custom model development for classification, regression, ranking, anomaly detection, NLP, and computer vision tasks, selected based on your data and constraints, not the current trend.
- Model selection and baseline evaluation
- Feature engineering and data preprocessing
- Training, validation, and hyperparameter tuning
- Model versioning and experiment tracking
LLM integration and RAG
Practical integration of large language models into your product, with retrieval-augmented generation (RAG), prompt engineering, guardrails, and cost management built in from the start.
- RAG pipeline design and implementation
- Prompt engineering and optimization
- Output validation and guardrails
- Vendor fallback and cost controls
Training and inference pipelines
Automated, reproducible pipelines for data preparation, model training, and deployment, so retraining is a scheduled operation, not a manual scramble.
- Data ingestion and preprocessing pipelines
- Distributed training (where required)
- Model registry and artifact management
- CI/CD for model retraining
Production inference and monitoring
Serving infrastructure that keeps inference fast, cost-effective, and observable. We define monitoring for model behaviour in production so degradation is visible in agreed metrics and alerts.
- REST and batch inference APIs
- Model performance monitoring
- Drift detection and alerting
- Latency and cost optimization
How it works
AI projects benefit from a data and infrastructure assessment before build commitments. We start there rather than assuming a model is the right first step.
- Data and feasibility assessment (1–3 weeks). We review your data: volume, quality, labeling, and freshness. We give you an honest assessment of what's achievable and what's not before committing to a build. Some projects are not ready for ML yet, and we'll say so.
- Model selection and baseline (1–2 weeks). We establish a baseline with simple approaches first. If a heuristic or rule-based system is good enough, we'll tell you: it's cheaper and more maintainable than a trained model.
- Pipeline and infrastructure design (1–2 weeks). We design the training pipeline, inference architecture, and monitoring strategy. This is documented and reviewed before build starts.
- Build and training (4–16 weeks). Pipeline implementation, model training, evaluation, and iteration. Delivered in milestones with evaluation reports at each stage.
- Production deployment and monitoring setup. Inference API deployed to your infrastructure. Monitoring dashboards in place. Alerting configured for drift and performance degradation.
- Handoff and documentation. Full system documentation: data schema, model card, pipeline diagram, deployment runbook. Your team can retrain and redeploy without us.
Indicative pricing
AI and ML costs vary more than other software; they depend heavily on data readiness, model complexity, and infrastructure requirements.
Feasibility study and proof of concept
What it covers: Data assessment, baseline model, initial evaluation, and a written recommendation for whether to proceed to production and what that would cost.
Best for: Teams who need to validate whether ML is the right approach before committing a larger budget.
LLM integration or RAG system
What it covers: Integration of a large language model into your product or workflow: RAG pipeline, prompt engineering, guardrails, fallback logic, and monitoring with cost controls aligned to expected usage.
Best for: Products that need document understanding, semantic search, classification, or generation features.
Full production ML system
What it covers: End-to-end: data pipeline, custom model training, inference API, monitoring, and drift detection scoped for the reliability your use case requires.
Best for: Teams with validated use cases who need a system that meets agreed load and reliability targets and can be maintained and retrained over time.
Ranges are indicative. Final fees depend on data readiness, model complexity, and infrastructure scope, all assessed during discovery.