Artificial Intelligence
Enterprise AI that survives contact with production
We build grounded, evaluated AI systems on Vertex AI — retrieval architectures, predictive models and the MLOps around them — with the governance an enterprise needs to deploy them.
Business challenges
What usually brings teams to us
These are the recurring conditions we are asked to resolve. If several look familiar, an assessment is the efficient starting point.
Pilots that never ship
A convincing demo exists, but nothing was built for evaluation, cost control, ownership or failure modes.
Ungrounded answers
Generative features answer confidently from model priors because no retrieval layer connects them to your data.
No definition of good
Quality is judged by anecdote, so regressions ship unnoticed and nobody can approve a release.
Unclear data rights
Teams are unsure which data may be sent to which model, so legitimate use cases stall in review.
Models without operations
Training happens in notebooks, deployment is manual, and drift is discovered by users rather than monitoring.
Unbounded cost
Token and inference spend grows without routing, caching or a view of cost per interaction.
Our approach
Grounded systems, evaluated continuously
We treat an AI feature as a system rather than a model: retrieval and grounding, prompt and context management, evaluation, deployment, monitoring, and explicit governance over what data is used where.
- Use-case selection against feasibility and value before any build begins.
- Retrieval architecture grounded in your governed data, with access control preserved end to end.
- An evaluation harness with representative cases, so quality changes are measured rather than debated.
- MLOps on Vertex AI: versioned artifacts, reproducible pipelines, staged deployment and rollback.
- Monitoring for drift, latency, failure rate and cost per interaction after launch.
- AI governance: documented data flows, retention decisions, human-review points and audit trails.
Core capabilities
What we deliver
- Generative AI
- Assistants, summarization and document workflows grounded in enterprise content.
- Vertex AI
- Platform architecture across training, tuning, deployment and endpoint management.
- Machine learning
- Supervised and time-series modelling against curated warehouse data.
- Predictive analytics
- Forecasting and propensity models wired into operational decisions.
- Natural language processing
- Classification, extraction and entity resolution over unstructured text.
- Retrieval-augmented generation
- Chunking, embedding, vector search and re-ranking with permission-aware retrieval.
- AI application architecture
- Service boundaries, orchestration, caching and graceful degradation.
- MLOps
- Reproducible pipelines, model registry, CI/CD and staged rollout.
- Model evaluation
- Golden datasets, automated scoring and regression gates before release.
- AI governance
- Data-use policy, human oversight, logging and auditability.
Technology stack
Google Cloud services we work with
Only services that are genuinely relevant to the capabilities described above.
Vertex AI
Model training, tuning, deployment, endpoints and pipelines.
Gemini
Foundation models for generative and multimodal workloads.
BigQuery ML
Modelling directly against warehouse data without extraction.
BigQuery
Curated features, training data and evaluation results.
Cloud Storage
Document corpora, artifacts and model assets.
Dataflow
Embedding, enrichment and feature pipelines at scale.
Pub/Sub
Event-driven inference and asynchronous processing.
Architecture
Reference data flow
01
Enterprise content
- Documents
- Curated tables
- Operational events
02
Preparation
- Chunking
- Embedding
- Metadata and permissions
03
Retrieval
- Vector search
- Filtering
- Re-ranking
04
Model layer
- Vertex AI
- Gemini
- Prompt and context management
05
Evaluation
- Golden sets
- Automated scoring
- Regression gates
06
Applications
- Assistants
- Workflow automation
- Decision support
Business outcomes
What changes as a result
Pilots reach production
Deployment, ownership and failure handling are designed in from the first sprint.
Answers you can defend
Responses are grounded in retrievable sources with citations back to the origin.
Measurable quality
An evaluation harness makes model and prompt changes comparable over time.
Governed AI usage
Documented data flows, retention decisions and human-review points.
Controlled cost
Routing, caching and monitoring keep inference spend proportional to value.
Operable systems
Versioned pipelines and monitoring that an internal team can run.
FAQ
Common questions
Do you build your own foundation models?
No. We build systems on established platform models such as those available through Vertex AI, and focus our engineering on retrieval, evaluation, integration and operations.
Our data governance is not ready. Should we wait?
Not necessarily, but scope matters. We typically start with a use case whose data boundary is clear while the broader governance work proceeds in parallel.
How do you measure whether an AI feature is good enough?
With a representative evaluation set agreed with the business, automated scoring against it, and explicit thresholds that a release must clear.
Can AI work run on our existing data platform?
Usually yes. Where curated, well-governed tables already exist, grounding work is considerably faster; where they do not, we sequence the data work first.
Start a conversation
Put an AI initiative on a credible architecture.
An AI readiness workshop establishes which use cases are feasible, what your data supports today, and what the implementation path looks like.