AI strategy & feasibility
Assessing suitable uses, technical and data readiness, measurable outcomes and a practical route to implementation.
AI that earns its place in your business
AI features and systems with clear uses, measurable quality and practical guardrails.
A measured approach to AI
AI development applies models and automation to work where they can improve how information is found, processed or acted on. We begin with a defined outcome, the available data and a clear measure of what good performance looks like.
Evaluation, safeguards and monitoring are developed alongside the capability, with human review when needed. This creates AI systems that are useful in practice, transparent in operation and sustainable to run.
Areas of expertise
Generative AI & assistants
Building applications that generate and work with content, from conversational interfaces to search across organisational knowledge.
AI agents & automation
Building systems that carry out tasks and coordinate actions across software, with defined permissions, controls and human oversight.
Computer vision & document processing
Interpreting images, video and documents to recognise content, extract information and support automated processing.
Predictive modelling & recommendations
Developing models that forecast outcomes, identify patterns and anomalies, score options and provide relevant recommendations.
AI engineering & operations
Preparing data, developing or adapting models, integrating them into software and monitoring quality, safety, performance and cost.
How we work
Data assessed for the intended use
The available data is reviewed for relevance, quality, coverage and permitted use. Gaps and assumptions are documented and addressed in model development, retrieval and evaluation.
Evaluation based on real tasks
Quality measures reflect the work the system needs to perform and the consequences of errors. Models and AI features are evaluated against representative examples and an agreed baseline.
Controls matched to the impact
Access, data handling, permissions and human oversight reflect what the system can influence. Outputs and actions can be reviewed, corrected or stopped according to their level of risk.
Approaches compared on quality, speed and cost
Custom models, existing models and simpler software approaches are compared against the same criteria. The choice reflects the required quality, response time, implementation complexity and operating cost.
Quality monitored in operation
Changes in input data, output quality, response times and costs are monitored against agreed measures. Results from real use inform improvements, model changes and decisions about retraining or replacement.
Tools and technologies
A selection of tools we use, chosen to fit the work and existing systems.
Models & serving
- Claude
- OpenAI
- Llama
- Mistral
- AWS Bedrock
- Ollama
- MCP
Retrieval
- Elasticsearch
- pgvector
- Qdrant
- LlamaIndex
- Cohere Rerank
ML & voice
- PyTorch
- TensorFlow
- scikit-learn
- Hugging Face
- OpenCV
- ONNX
- Soniox
- ElevenLabs
Evaluation & monitoring
- promptfoo
- Langfuse
- Weights & Biases
- Grafana
Questions, answered
We begin with the task, available data and a measurable result. We compare an AI approach with simpler software or process changes, then test the most promising option against representative examples before recommending wider development.
Both. The choice depends on the required quality, available data, privacy constraints, response time and operating cost. We can integrate an existing model, adapt one or develop a custom model where the need and evidence justify it.
No single hosting arrangement suits every use case. We assess what data the system needs, where it can be processed and which providers or deployment options meet your requirements. The data flow and access controls are defined before implementation.
We define what a useful result looks like for the actual task and evaluate the system against representative examples, including difficult cases. The level of review and control reflects what could happen if the system is wrong.
Yes. Human review, permissions and the ability to correct or stop an action can be built into the workflow. The right level of oversight depends on the system’s purpose and the consequences of an incorrect output or action.
We consider model use, hosting, data processing, monitoring and ongoing evaluation when comparing approaches. Costs and quality are measured during development and monitored after release, so changes in demand or performance can be addressed.
Selected work
A few engagements we’re proud of. Shipped, scaled, and still in production.
Avenir Global
Desktop applications and AI for a communications group
Tell us what you’re building.
The complex, the critical, the bold. We’ll tell you how we’d ship it.