AI assistants and knowledge search
Help people find, understand, and use trusted information across documents, policies, operational data, and internal knowledge.
AI consultancy servicesPractical AI solutions built around useful business outcomes.
Solvanto helps organisations find sensible uses for AI, prove value safely, and build solutions that fit their existing systems and ways of working. From internal knowledge assistants to document processing and AI-enabled automation, delivery stays focused on security, accuracy, ownership, and long-term supportability.
A proportionate route to AI
Start with the work, not the model.
AI is most useful when it improves a real task and works within clear boundaries. We help you choose a credible use case, test it against representative work, and understand the trade-offs before scaling it.
A defined problem and success criteria
Clear data, security, and review boundaries
Evidence from a focused proof of value
Monitoring, documentation, and ownership
Where AI can help
Focused AI capabilities connected to real work
The strongest opportunities are usually specific: finding an answer across approved information, extracting data from incoming documents, helping a team handle requests consistently, or reducing time spent reviewing and summarising routine material. The solution should fit the process around it, including the exceptions and decisions that still need a person.
AI-enabled process automation
Add classification, extraction, summarisation, and decision support to workflows while keeping important controls and human review in place.
Document and data processing
Turn emails, forms, PDFs, and other unstructured content into useful, validated information that can flow into business systems.
AI integration
Connect AI capabilities to Microsoft 365, Power Platform, Azure, APIs, and existing operational systems instead of creating another isolated tool.
Good candidate use cases
Where a focused first project can make sense
A useful first use case is bounded, repeated often enough to matter, and supported by information you can legitimately use. It should also have an understandable way to check whether the output is good enough.
Some processes do not need AI at all. If a reliable rule, integration, form, or workflow can solve the problem more simply, that is often the better investment.
Finding answers across approved internal documents
Classifying and routing requests or correspondence
Extracting structured data from documents and emails
Drafting summaries, responses, and operational updates
Supporting service teams with relevant context
Identifying patterns and exceptions in operational data
Adding natural-language access to existing information
Reducing repetitive review and data-entry work
Delivery approach
From a worthwhile use case to a supportable solution
AI delivery needs more than a successful demo. The route to production should establish what the solution can do, where it can fail, who owns it, and how its quality and cost will stay visible.
01
Choose the right problem
Start with a process, decision, or information bottleneck where AI could create useful and measurable improvement. We also identify where normal automation or a simpler system change would be the better answer.
02
Check data, risk, and feasibility
Review the information available, expected users, security boundaries, quality requirements, failure modes, and the level of human oversight the use case needs.
03
Prove value in a focused pilot
Build a bounded proof of value using representative scenarios and clear acceptance criteria, so the organisation can judge usefulness before committing to a wider rollout.
04
Build for real operation
Integrate the solution, test it, add monitoring and safeguards, document ownership, and plan how performance and cost will be reviewed after launch.
Responsible, practical delivery
Governance designed into the solution
Useful before impressive
A good AI project solves a recognisable operational problem. It does not begin with a technology demo in search of a purpose.
Control where it matters
Permissions, data boundaries, human review, traceability, and safe failure behaviour are designed around the risk of the task.
Measured in operation
Quality, usage, cost, exceptions, and feedback need to remain visible so the solution can be supported and improved over time.
Related pages
AI works best as part of a joined-up system
Explore the related capabilities that help turn a promising AI use case into a reliable operational solution.
FAQ
Common questions about AI consultancy
Practical answers for organisations considering their first AI project or trying to move an existing idea beyond a prototype.
What kind of AI consultancy does Solvanto provide?
Solvanto helps organisations identify worthwhile AI use cases, assess feasibility and risk, build focused prototypes, and deliver supportable AI-enabled workflows, assistants, document-processing solutions, and integrations using Microsoft technologies where they are a good fit.
Do we need to know exactly which AI tool we want?
No. The better starting point is the operational problem, the information involved, and the outcome you need. Solvanto can then assess whether AI, conventional automation, an integration, or a simpler process change is the most proportionate option.
Can AI work with our existing Microsoft systems?
Yes. AI capabilities can be connected to Microsoft 365, Power Platform, Azure, SharePoint, databases, APIs, and line-of-business systems. The right design depends on permissions, data location, reliability requirements, and how people will use the result.
How do you handle sensitive business data?
Data access and security boundaries are considered from the start. The design should use appropriate permissions, minimise unnecessary data exposure, define retention and logging needs, and keep human approval around decisions where the risk requires it.
Can you build an internal AI assistant?
Yes. An internal assistant can help people search approved knowledge, summarise information, or complete a defined task. Its usefulness depends on the quality and permissions of the source material, so discovery and evaluation are important parts of the work.
How do we know whether an AI solution is accurate enough?
Accuracy needs to be assessed against representative real-world examples and clear acceptance criteria. Solvanto designs evaluation, fallback behaviour, source references, monitoring, and human review in proportion to the consequences of an incorrect result.
Start with one useful problem
Have an AI idea, or a process that might benefit from one?
We can help you assess the opportunity, identify the simplest credible route, and decide what a focused proof of value should demonstrate before you invest further.