AI automation that turns a repeated business process into one monitored workflow.
Thalenor is an AI automation company in India that combines software integrations, workflow logic and, where genuinely useful, AI models to handle repetitive operational work with less manual intervention.
Most automation problems are not AI problems. They are hand-off problems.
A quotation request arrives by email, someone reads it, checks stock in a spreadsheet, replies on WhatsApp and updates the CRM at the end of the day. Nothing there needs intelligence. It needs connection, sequence and a place where exceptions surface.
Six hand-offs become one workflow.
The same quotation request, handled two ways. The second version is not more intelligent. It is more connected, and it shows its status at every step.
Six hand-offs. Each one a place for delay or error.
Same outcome. Visible status at every step. People only where judgement is needed.
A quotation enquiry, from inbox to pipeline.
One enquiry followed through a real orchestration design. Rules do the predictable work, AI reads the language, and a person approves the one step that carries financial risk.
- 09:14:02Email arrives at sales@ with a PDF attachment.
- 09:14:03Required fields validated; sender domain checked against CRM.
- 09:14:05Intent and summary extracted: quotation for 400 units, delivery by 30 Sept.
- 09:14:06Draft quotation generated from price list; discount rule triggers approval because value exceeds ₹2,00,000.
- 09:31:40Sales lead approves the draft in the workspace and edits the delivery clause.
- 09:31:41Quotation sent, CRM stage updated, 48-hour follow-up scheduled, pipeline dashboard refreshed.
The CRM API times out while updating the stage.
Three attempts with backoff. The step is idempotent, so a repeat never creates a duplicate record.
If retries fail, the run pauses and a named owner is alerted with the full context.
The quotation is already sent; the pending CRM write is queued and visible in the run log.
What is AI automation?
AI automation combines software integrations, workflow rules and, where useful, AI models to handle repetitive work with less manual intervention. Rules move records, calculate and notify. AI classifies, reads documents and drafts language. People approve the decisions that carry risk. The result is one workflow with a visible status instead of six hand-offs between tools.
Three kinds of steps. Each handled by the right thing.
This distinction is where automation projects succeed or fail. We make it explicit in every workflow we design.
Rules
Predictable, testable, cheap.
- Update the CRM
- Calculate totals and dates
- Route to the right person
- Notify by email or WhatsApp
- Move and copy records
AI
Where language and meaning are involved.
- Classify an enquiry
- Read an invoice or contract
- Understand a free-text message
- Draft a reply for review
- Extract fields from documents
Human
Where the decision carries risk.
- Sensitive customer decisions
- Financial approvals
- Exceptions the rules did not foresee
- High-risk or irreversible actions
- Anything the business must own
Operational work that repeats every day.
Sales and lead handling
Enquiry capture, qualification, CRM updates, quotation drafts and follow-up sequences.
Customer support
Classification, suggested replies, order lookups and escalation to a person when confidence is low.
Document workflows
Reading invoices, forms and contracts; extracting fields; routing for approval.
Operations and reporting
Status changes, notifications, data synchronisation between systems and scheduled reports.
LLM automation with guardrails
Language-model steps with schemas, logging, fallbacks and approval gates.
From one messy process to a workflow you can watch.
Map
Walk through the process as it happens today, including the workarounds.
Decide
Which steps are rules, which need AI, which stay with a person.
Connect
Integrate CRM, email, documents and databases through APIs.
Run
Launch with logging, alerts and a clear owner for exceptions.
Refine
Tune classification, expand coverage, remove the next manual step.
What an AI automation is actually made of.
AI is one layer of six. The others make it safe to run every day.
Each action carries a key so a retried step cannot create a second record or send a second email.
Every run has a visible state: received, validated, classified, awaiting approval, done, failed.
Runs, latencies, confidence distributions and failures are visible to your team, not just to us.
Straight answers to the questions that decide a project.
- Introduce a language model where a validation rule is more reliable.
- Let an automation send money, delete data or contact a customer without an approval gate on first deployment.
- Build a workflow nobody on your team can see the state of.
Will this integrate with the tools we already use?
Almost always. CRMs, email, accounting, storage and databases expose APIs or webhooks. Where a tool does not, we use exports, a small connector or a human step until it can be replaced.
What happens when the AI is wrong?
AI outputs are structured and checked. Low confidence routes to a person. Decisions with consequences wait for approval. Every output is logged so mistakes are found and the step improved.
Can staff override the workflow?
Yes. Override points are designed in, and every override is recorded. That record often shows where the rules need to change.
Can it start small?
The best projects do. One process, end to end, with logging. Then the next manual step.
Who owns the workflow?
You do. Definitions, code, credentials and logs live in your accounts. We document how it runs and how to change it.
Questions worth asking first.
We map the process step by step. If a step can be expressed as a rule, it becomes a rule. AI is reserved for steps that involve language, unstructured documents or interpretation. That keeps the workflow cheaper, faster and easier to trust.
Show us the workflow.
Describe the process as it happens today, including the spreadsheet and the WhatsApp messages. We will tell you honestly which parts should be rules, which parts benefit from AI, and which should stay with your team.