Artificial Intelligence
AI that works inside real operations
We design and ship AI features that are measurable, governed, and connected to your data, from retrieval systems to task-specific agents.
Overview
An AI system needs evaluation, data access, cost controls, and clear oversight alongside its model. Planning for these parts can help teams assess whether a use case is suitable.
Models are one component in a larger system that can also include data pipelines, evaluation, guardrails, and interfaces.
Capabilities
What AI Systems covers
AI Application Development
Production applications with AI at the core, not bolted on afterwards.
Generative AI Integration
Language models connected to your products, documents, and internal tools.
AI Agents
Task focused agents that plan, call tools, and hand off to people when needed.
Machine Learning Solutions
Forecasting, classification, and recommendation models trained on your data.
Natural Language Processing
Extraction, summarisation, and search across contracts, tickets, and records.
Retrieval-Augmented Generation (RAG) and Enterprise Search
Search internal knowledge with answers linked to the source passages they use.
Document Intelligence
Extract fields, classify documents, and route uncertain cases for review.
Conversational and Voice AI
Voice and text interfaces connected to bounded workflows, with a clear handoff to people.
Computer Vision
Inspection, document capture, and image classification for operational workflows.
AI Automation
Review queues and repetitive decisions handled with humans kept in the loop.
AI Strategy and Governance
Use case selection, data handling, risk controls, access policies, and operating guidance.
Model Evaluation and Red Teaming
Test representative cases for accuracy, safety, latency, and cost before and after release.
AI Training and Enablement
Role-based workshops and guidance to help teams assess and use AI tools.
Intelligent Process Automation
Automate structured, rules-based tasks across existing applications, with review paths for exceptions.
Enterprise AI Integration
Connect model gateways and access controls to existing systems and governance.
Data-Driven Applications
Products that improve as they learn from usage and outcomes.
Technical challenges
Examples this service may address
Project requirements vary. These examples can help frame an initial discussion.
Pilots that never reach production
Promising demos that stall on security review, cost, or accuracy.
Knowledge trapped in documents
Answers spread across PDFs, wikis, and inboxes that nobody can search.
Manual review queues
Skilled people spending hours on triage that follows predictable patterns.
Governance concerns
Leadership uncertain about data leakage, hallucination, and model risk.
How we deliver
How the work runs
Sequence and architecture are set by your goals and constraints.
- 1
Frame
Pick use cases with measurable value, available data, and an acceptable risk profile.
- 2
Prototype
Build against real data within weeks and test with the people who will use it.
- 3
Evaluate
Define accuracy, latency, and cost benchmarks. Test against them on every change.
- 4
Deploy
Ship with guardrails, monitoring, feedback loops, and clear human oversight.
- Assistant UI
- Workflow triggers
- Public API
- Agent runtime
- Prompt registry
- Guardrails
- Embeddings
- Vector store
- Search index
- LLM gateway
- Model hosting
- Evaluation
Technology stack
Examples of technologies that may fit this service area.
- Python
- PyTorch
- scikit-learn
- OpenAI compatible APIs
- LangGraph
- pgvector
- Pinecone
- Hugging Face
- FastAPI
- MLflow
- Databricks
FAQ
Common questions
Data handling depends on the selected model provider, deployment, and agreement. Confirm retention and training terms before connecting sensitive data.
We are model agnostic. We benchmark commercial and open weight models for each use case and route requests through a gateway so you can switch providers later.
Through retrieval grounded in your sources, citation requirements, structured outputs, automated evaluation suites, and human review for high impact decisions.
Timing depends on data access, integrations, approvals, evaluation criteria, and the intended scope.
Prefer to talk it through?
A private consultation, 30 minutes with the founder, in your time zone.
Planning a ai systems initiative? Let us talk.
Tell us what you are working on. We start by understanding the problem, the people, and the systems involved.