AI is most useful when its role in a workflow is clear. Aranya helps assess where intelligent assistance or conventional automation may reduce repetitive steps, improve access to information or help people review material. We begin with a bounded task and the consequences of getting it wrong, rather than assuming every process needs a language model.
Choose an appropriate starting point
Discovery can map the current workflow, identify manual handoffs and compare rules-based automation with AI-assisted approaches. Potential scopes include document triage, knowledge retrieval, drafting assistance and internal support tools. Data permissions, confidentiality, expected volumes and exception handling shape the design from the outset.
Test before extending access
A pilot can include a prototype, a representative evaluation set, documented acceptance criteria and a review interface. We consider inaccurate outputs, missing evidence, sensitive information, prompt injection and unsuitable requests. Where a system uses business documents, responses should make their supporting sources available when feasible.
Automation that can take actions needs explicit limits, approval steps and an audit trail. Deliverables may include integration code, evaluation reports, operating instructions and a monitored rollout plan. A named business owner should decide which decisions remain with people and how errors are escalated.
Understand the limitations
Models can produce plausible but incorrect answers, and their behaviour can change with new data or provider updates. Accuracy, response time and operating cost depend on the task and implementation. We do not promise autonomous reliability or guaranteed savings. Start small, retain a manual fallback and expand only when evaluation and operational readiness support the next step.