Connect your business data and knowledge to the work your team needs to do.
Raystrat develops AI applications, connects them to existing systems and works with your users to evaluate and deploy them.
A useful answer depends on the right information, the context to interpret it and a clear definition of what good looks like.
A useful business application needs more: access controls, integrations, review steps and a dependable way to handle missing information or incorrect output.
We build those parts together, around the workflow the system must support.
Bring information from files and business systems into reports that people can check and use.
The system can assemble a draft, identify missing information and inconsistencies, and make supporting sources available during review.
Define before building: source formats, report requirements, validation rules and who approves the output.
Help teams answer questions using relevant internal information.
Connect documents and systems, retrieve supporting material and present answers with references. Respect existing access permissions and flag when the available information does not support an answer.
Define before building: authoritative sources, user permissions, update frequency and evaluation criteria.
Turn incoming documents into information the business can act on.
Extract relevant fields, check required information and pass the result into the next workflow or system. Route ambiguous or incomplete cases for review.
Define before building: document types, required fields, exception rules and downstream actions.
We scope the application around your operating requirements. These are starting points, not a fixed product catalogue.
A field name does not explain a business rule. A report does not capture every assumption behind a decision.
We work with your team to identify the definitions, relationships and exceptions the application needs to handle. Relevant context becomes part of the system through maintained reference material, explicit rules and tested workflows.
As requirements change, that context needs an owner and a process for keeping it current.
Scope, deliverables, infrastructure, ownership terms and ongoing support are agreed before implementation.
Identify the users, available information, existing systems and outcome the business needs. Agree what is included and how the result will be evaluated.
Develop the application using representative inputs. Review working outputs with the people who understand the task.
Integrate the application into the agreed environment. Resolve access, reliability and user-readiness requirements.
Review performance against the agreed measures. Address limitations and extend into additional workflows where the results justify it.
Not necessarily. We first assess the tools and interfaces already available. The solution may integrate with existing software rather than replace it.
No. We assess the relevant sources and identify the preparation required. Missing, inconsistent or inaccessible information may affect the scope and sequence of delivery.
We define evaluation criteria, test representative cases and design review and exception paths around the consequences of an error. AI output should not be treated as inherently reliable.
Ownership, access, third-party dependencies and maintenance responsibilities are agreed in the engagement terms. The operating model should be clear before deployment.
A specific workflow, access to the people who understand it and representative examples that can be shared through an agreed secure process. Please do not send confidential files through the public enquiry form.
Tell us how the work happens today, where time or quality is being lost and what a useful outcome would look like.