Where the operation was getting stuck.
Important knowledge was difficult to locate and repetitive information work slowed down everyday decisions.
tokensoftStart a project Anonymised case study 03
Project SignalConfidential knowledge-intensive organisation
Enterprise · Workflow intelligence
Responsible AI that helps teams find, interpret and act on business knowledge.
An intelligence layer that helps teams find, interpret and act on information already present across documents and operational systems.
03 / Product systemAnonymised delivery context Product evidence retained
The product context
Faster decisions, with the context intact.
The organisation already had the information required for better decisions, but it was distributed across documents, systems and individual knowledge. Finding and preparing the context consumed valuable specialist time.
The experience
A focused intelligence layer retrieves relevant approved information, shows the evidence behind an answer and prepares useful next actions inside the existing workflow. People remain responsible for consequential decisions.
Confidentiality note
The client identity has been withheld. The information challenge, control model and Tokensoft delivery responsibilities have been retained.
Published product evidence
What we can responsibly disclose.
Applied AI discovery, workflow design and product engineering
- 01Permission-aware knowledge retrieval
- 02Evidence-linked outputs inside operational workflows
- 03Human review retained for consequential decisions
How Tokensoft shaped the product.
Tokensoft connected secure retrieval, structured workflows and human review into an AI-assisted operating experience.
Inside the product
Connected capability around one clear journey.
Permission-aware retrieval
Users receive answers from the sources they are authorised to access, preserving organisational boundaries.
Evidence with every answer
Source context helps users understand, verify and refine the information returned by the system.
Workflow assistance
Structured outputs and draft actions reduce repetitive preparation without hiding human accountability.
Evaluation and feedback
Quality signals, feedback and edge cases guide controlled product improvement over time.
How we approached it
Understand the system around the software.
Find the information bottleneck
Identify frequent, expensive knowledge work with sufficient value and usable source material.
Prototype on real context
Test retrieval, output structure and review controls against representative work before scaling.
Engineer the operating system
Connect permissions, evaluation, observability and workflow integration around the AI capability.
Product outcomes
A clearer operation and a stronger foundation for what comes next.
- 01Faster access to approved business knowledge
- 02Less repetitive information preparation
- 03Visible sources and human review
- 04Measurable pathway for responsible improvement
Technology foundation
Selected for the product and its operating reality.
The implementation combined modern product engineering with secure integration, maintainable architecture and visible quality controls.
Platform & methods
Retrieval-augmented generationVector searchSecure APIsDocument processingEvaluation pipelinesRole-based accessWorkflow orchestrationObservability
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