Practical AI
AI that works on your data, inside your business.
Turn business documents and data into searchable answers, validated records, and actions your team can review.
Most AI stalls between the demo and the business.
The pilot impressed everyone. Then it met real permissions, real data quality, and real operational load, and it stopped.
We get called at exactly that point. The model was never the hard part. Everything either side of it is.
You do not need a pilot to start a conversation. We also work as thought partners, helping you turn an AI ambition into a practical plan: identify useful opportunities, assess the data and risks, and decide what is worth building first.
What we build
Grounded retrieval
Point a model at your own documents and data, with answers that trace to a source.
Enterprise search
People find the thing they need without knowing where it lives.
Document intelligence
Pull structure out of contracts, invoices, and reports.
Workflow agents
Intake, triage, and routing handled automatically.
Human in the loop
A person stays in the path wherever the decision matters.
Governed access
Routing, policy, and audit around who can ask what.
Don't see what you need?
Tell us about the problem. We can help you work out where AI fits and what to do next.
Talk through your idea →The model sits inside the middle box. It was never the hard part.
Choosing the right tools for your business
We choose tools around your data, security requirements, budget, and the way your team works. Retrieval augmented generation (RAG) and vector search help AI find relevant information in your documents. Model providers supply the AI; DataSafeHouse adds governed access. You do not need to choose a stack before talking to us.
- RAG
- Vector search
- OpenAI
- Anthropic
- AWS Bedrock
- Mistral
- DataSafeHouse
How an AI build usually goes
- Choose a useful taskIdentify a specific decision or repetitive task, the people responsible for it, and how you will judge a useful result.
- Recommend an approachCompare the options against your data, permissions, cost, and accuracy needs so you can choose a practical starting point.
- Prepare the evidenceConnect approved sources, preserve access permissions, and assemble representative examples for evaluation.
- Build and evaluateTest answers or extracted records against those examples, including missing information and ambiguous inputs.
- Connect the workflowAdd validation and human review where needed before the application writes records or takes action in another system.
- Review production resultsTrack failures, usage, and cost, then adjust the application as documents, models, and business rules change.
Where we have done this
The measure-sheet application extracts and validates order details, asking a person about ambiguous lines. The competitive intelligence platform classifies new material as it arrives.
From measure sheet to supplier order, without the typing
An AI system that reads a field measure sheet, checks every line against the supplier catalogue, and places the order directly. No re-keying.
A window and door contractor
Competitive intelligence, watched continuously
A platform that watches a drug market around the clock, classifies everything new as it lands, and keeps a dated picture of every competitor and asset.
A publicly traded pharmaceutical company
Have a task that might benefit from AI?
Describe the task, the information it depends on, and what a useful result would let your team do next.