Generative AI
Drafting, summarising and knowledge assistance grounded in material the organisation chooses to provide.
Our Businesses
Turning Intelligence Into Business Impact.
AI used where it removes repetitive work or improves a decision, with a person still accountable for the outcome.
Introduction
AI & Intelligent Solutions helps organisations use artificial intelligence on a real task: reading documents, answering routine questions, drafting a first version, or spotting patterns in data they already hold.
Current services are the engagements Shreeva is prepared to scope and deliver with human review. Exploratory areas are directions under study. They are not packaged products and should not be read as live offerings.
Business challenges
- 01
Work that is repetitive but still needs judgment
Teams lose hours on sorting, summarising and retyping, then still need a person to decide.
- 02
Interest in AI without a defined task
A model demonstration is not a workflow. The useful question is which step should change, and how errors will be caught.
- 03
Data that is incomplete or sensitive
Automation built on poor records, or on information that should not leave the organisation, creates risk rather than leverage.
Services
AI consulting
A practical assessment of where AI can help, what it should not do, and what data or process work must come first.
AI workflow automation
Assistance inside an existing process, with a person reviewing the result before it is acted on.
Document intelligence
Extraction and classification from forms, letters and reports, followed by human confirmation where the decision matters.
Conversational AI
Guided question-and-answer tools for staff or customers, limited to an approved body of information.
Data analytics
Clearer reporting from operational data, so leaders can see what is happening before they automate it.
Custom AI applications
A focused application around one use case, rather than a general chatbot with no boundary.
Technical capabilities
- Use-case selection and risk review
- Workflow design with human approval
- Document extraction and summarisation
- Knowledge-base question answering
- Reporting and operational analytics
- Integration of AI steps into business software
Industries and contexts
- Service businesses with heavy documentation
- Education and internal knowledge teams
- Operations teams seeking fewer manual handoffs
- Organisations preparing their data before automation
How an engagement is run
01
Name the decision
Write down the task, the user and what a wrong answer would cost.
02
Bound the information
Use approved sources. Do not point a system at every file the organisation owns.
03
Keep a reviewer
Design the screen so a person can accept, edit or reject the output.
04
Measure one workflow
Judge the work by time saved and errors caught, then decide whether to extend it.
Exploratory work, not a current product
The items below are areas of study. They are listed so the boundary is visible. They are not offered as finished services.
AI agents
Multi-step assistants that take actions across tools are being explored. They are not offered as an unsupervised service.
Machine learning models
Custom predictive models need enough reliable history. Where that history does not exist, Shreeva will say so rather than force a model.
Predictive analytics
Forecasting demand, risk or maintenance is an exploratory capability and depends on data the organisation can stand behind.
Notes on what this page does not claim
- Shreeva does not claim proprietary foundation models, published benchmarks or a record of deployed AI products.
- Customer data is not used to train public models unless that arrangement is agreed in writing.
Questions
Generative assistance, consulting, supervised workflow automation, document intelligence, conversational tools on approved knowledge, analytics and custom applications around a single use case. Agents, custom machine-learning models and predictive analytics are exploratory and are not sold as ready-made products.
The services on this page are designed with a human reviewer. Shreeva does not offer unsupervised systems that take material business actions on their own.
Not unless that is agreed in writing. The default position is that customer material is used only for the agreed engagement.
Then the honest recommendation may be to improve the records before any model work. Shreeva will say when the data is not ready.
Next step
Talk to us about this practice.
Describe the work, the people involved and what a useful first conversation would cover.
Related reading is available in Insights.