How long does a first deployment take?
A scoped pilot on a single workflow typically runs two to six weeks depending on data readiness. Productized modules that need no custom retrieval can be live in days.
How it works
One workflow, audited, automated, and handed back running
Siththi.ai engagements start with a workflow audit, move to a fixed-scope pilot on your real data, then integrate into the systems you already run. Most first deployments are live within two to six weeks.
Engagement
Four phases
- 01
Workflow audit
We map one process end to end — who touches it, what it costs in hours, where it breaks — and quantify the recoverable time before writing any code.
- 02
Scoped pilot
A single workflow is automated against your real data with a fixed scope and a fixed fee. You see output quality before committing to a retainer.
- 03
Integration
Output lands in the tools you already use — spreadsheets, email, messaging, your existing system of record. No new dashboard to adopt.
- 04
Managed operation
Ongoing monitoring, prompt and retrieval tuning, and expansion into adjacent workflows as accuracy on the first one stabilises.
Architecture
The vocabulary behind the builds
Four concepts explain almost everything about how a Siththi.ai system behaves in production.
- Specialist agents
- Ingestion, classification, compliance, and output are separate agents with narrow responsibilities, so an error is traceable to one stage rather than one long prompt.
- Stateful orchestration
- A coordinating layer holds the state of a job across steps, allowing retries, escalation, and resumption instead of one-shot generation.
- Grounded retrieval
- Answers are generated over your indexed documents with citations back to the source, which is what makes output auditable.
- Clearance node
- A mandatory human approval gate placed in front of any action with regulatory, financial, or reputational consequences.
Questions
Frequently asked questions
Do we need to replace our existing software?
No. Siththi.ai modules sit alongside your current systems and write into them. The deployment model deliberately avoids platform migration, which is what makes short timelines possible.
What happens when an agent is uncertain?
Low-confidence outputs are routed to a human clearance queue instead of being sent onward. Nothing that carries regulatory or financial risk leaves the system without a person approving it.
Who owns the data and the models?
You own your data. Retrieval runs over a knowledge base you control, and we use zero-data-retention endpoints so your documents are never used as training material.