Workflow and integration plan
A map of triggers, source data, decisions and destinations. We identify where an API connection or a fixed rule is enough, and where an LLM-assisted step may be useful.
AI & WORKFLOW AUTOMATION / GURUGRAM, INDIA
SliceCode provides AI automation services from Gurugram for teams with repeated tasks and disconnected systems. We start with the workflow: what arrives, what needs to happen, who approves the result and where it should go. AI is one option within that process, not a requirement for every step.
For a team repeatedly sorting enquiries, preparing documents or moving records between systems. A useful starting point is one well-understood task with sample inputs, a process owner and a measurable definition of a correct result.
The proposal confirms the deliverables for your brief. Here is how we approach the work.
A map of triggers, source data, decisions and destinations. We identify where an API connection or a fixed rule is enough, and where an LLM-assisted step may be useful.
One agreed workflow built around representative examples. Possible starting points include enquiry classification, draft document summaries or synchronization between supported business systems.
Validation checks, human approval points and a path for incomplete or uncertain results. Important actions should have the appropriate review before they reach customers or change records.
Agreed test cases, an account of external usage costs and instructions for running the workflow. We compare the pilot with the existing process before deciding whether to expand it.
Planning timeline: Flexible sprints
Get a scoped proposalAI automation is quoted by workflow or retainer. Cost depends on the systems involved, permissions, data preparation, review interface and exception handling. Model API usage, hosting and other provider subscriptions are separate considerations. We agree the pilot timeline after mapping the task.
We work from Gurugram with local and remote teams. Share your current setup, the result you need and any deadline so we can agree a realistic first step.
A predictable calculation or data transfer is often better served by conventional software. AI can help with language-based inputs, but its outputs need evaluation against examples from your real process.
Agree the permitted data, access, providers and retention requirements before implementation. The solution depends on what your systems support and what the team is authorized to share.
Bring sample inputs, the tools involved, time spent on the task and examples of exceptions.
Agree one useful outcome, permissions, review points, test cases and the budget for external services.
Connect the agreed systems and test normal, incomplete and unexpected inputs before enabling the workflow.
Compare correctness, review effort, handling time and running cost. Improve or expand only when the pilot meets the agreed acceptance checks.
The Explore area includes an architecture explorer and a sample signal replay. These browser demonstrations use fictional sample data to explain system flows. They are not evidence of a deployed client AI system or a promise of measured savings.
Try the architecture demonstrationStart with a repeated, bounded task such as categorizing incoming enquiries, preparing a draft summary for review, or transferring approved records between tools. The first task should have accessible inputs and a clear way to check the output.
We quote after reviewing the workflow and integrations. The proposal separates implementation scope from recurring model, hosting or provider usage. There is no single fixed price that fits every automation.
This can be part of the scope when the application and providers offer suitable access. We review where the AI output appears, what data it receives, how users check it and what happens when a request fails.
The level of autonomy is agreed for each step. We can keep people responsible for approvals and uncertain outputs, with conventional rules for predictable tasks. A pilot should establish the review requirements before production use.
Agree a baseline before building: task volume, handling time, error rate and review effort. Evaluate the pilot using representative examples and operating costs. Any claim about time saved should follow those measurements.
Engineering · 3 MIN READ
Map a repeatable task, choose rules or AI deliberately, define human review and measure an automation pilot against the existing process.
Tell us what needs to work.
We’ll help define the first step.