AI Transformation · 04 of 04
Agent Orchestration.
Specialist agents with tools, memory and guardrails that carry out the work and draft the decisions a human approves.
A chatbot answers questions about the work. An orchestrated agent system does the work. It reads the request, routes it to the specialist with the right tools, drafts the quote or the follow-up or the data entry, and hands it to a named person to approve. Every step is logged and every tenant is kept apart.
guard layer · permissions · spend limits · PII · per-tenant queues
audit log
01intent: pricing request · 5 plants · from call #4821
02orchestrator → quote agent (tools: price-book, crm)
03draft: ₹ 2,40,000 · 3 line items · margin ok
04awaiting approval — Bava
05approved · sent · logged · model v3.2
live demo · synthetic data · runs in your browser · nothing is uploaded
what it buys you
The middle of the day, handed to software.
Work done, not answered
Quotes drafted, follow-ups sent, records updated, exceptions routed. The operational middle that eats your team's day, carried by software.
Drafts a human approves
Anything consequential is drafted, never done. A person sees it, edits it and approves it in seconds instead of the hour it took to write.
Every action explainable
Which agent, which tool, which data, which model version. A log your auditor can read and your team lead can trust.
how it works
Orchestrator, specialists, guard, approver.
The four parts of the runtime and the credentials each one holds.
what leaves your site
Agents run in an isolated tenant with scoped credentials. Your data is never used to train shared models, and every tool call is logged.
- 1
An orchestrator routes intent
One front door reads the request and routes it to the specialist that owns that job: pricing, CRM, scheduling, documents.
- 2
Specialists with their own tools and scope
Each agent holds only the tools and data its job needs. A pricing agent cannot touch HR records. A scheduling agent cannot issue refunds.
- 3
A guard layer in front of all of it
Permissions, spend limits, PII handling and rate limits sit between the agents and your systems. It is the same per-tenant isolation our own platform runs on.
- 4
Draft, approve, log
Outputs arrive as drafts to a named approver. Approvals and edits are logged with the model version. If anything needs re-running, backfill and replay are one click.
where it fits
Workflows with obvious hours in them.
Sales follow-up
Post-call summaries, quotes and reminders drafted and queued for the rep.
Operations exceptions
A late truck, a short shipment, a failed reading: triaged and routed with the context attached.
Finance approvals
Invoices matched, anomalies flagged, approvals drafted with the evidence.
Support triage
Tickets classified, answers drafted from your own knowledge base, escalations routed.
People requests
Leave, letters and onboarding steps handled end to end, with a human on the sign-off.
measured before trusted
One workflow, one month, one number.
One workflow, one month, measured against how long it takes your team today, with every agent action logged so you can audit what it did.
One workflow, one month
Sales follow-up, invoice matching or ticket triage. One process, run for real, for thirty days.
Against your team's hours today
We time how long the work takes now and how long it takes with the agents drafting. You get the difference.
A named approver on every draft
Nothing consequential is sent, paid or filed until a person you name approves it.
Every tool call logged
Which agent, which tool, which data, which model version. Replay and backfill are one click.
what is real today
It runs our own operations.
The agent runtime on this site's homepage is not an illustration. It is the same system carrying routine work inside TealOrca every day.
the client systems behind the AI chapter →- 1
Orchestrator, specialists, guard layer
One front door routes intent to specialist agents that each hold only their own tools and scope, behind a layer enforcing permissions, limits and PII rules.
- 2
Draft, approve, log
Consequential outputs arrive as drafts to a named person. Approvals, edits, tool calls and model versions are logged. Replay and backfill are one click.
- 3
Per-tenant by design
Isolation at the row level and queue fairness between tenants. These are the patterns our multi-tenant products run on, not a demo architecture.
asked before signing
Control, models, systems, first step.
What technical leads ask once the demo has finished.
different question? WhatsApp us →Three things. Each agent only has the tools its job needs. A guard layer enforces permissions, limits and PII rules. And anything consequential is a draft a person approves. Autonomy is earned per workflow.
Whichever wins for the task. We benchmark and route, and we can run on your cloud account. Every output carries the model version, so a change in model is visible.
That is the point. Agents work through connectors to your CRM, ERP, telephony and documents rather than in a separate tool your team has to learn.
With one workflow where the hours are obvious: sales follow-up, invoice matching, ticket triage. One month, measured against today, logged end to end. Then the next one.
your move
Name one workflow. We'll run it for a month and show you the hours.
One message. An engineer replies within 24 hours with how we would approach it.
after you press send
within 24hAn engineer replies
Sasi, Bava or Bharath. No sales script.
day 1A 30-minute call
Your problem, mapped. No long presentations.
day 2–4A clickable mock-up
You react to screens instead of documents.
week 2+First version goes live
You see it working early. Then we grow it.
no retainers to start · no slideware · your first invoice comes with working software