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AI-03 Intelligent routing ยท Kanpur

AI Workflow Automation in Kanpur

Some routing decisions at your Kanpur operation cannot be made from a field, because the field does not exist yet. A model reads the item, works out what it is, and the workflow routes on that plus how confident the model was.

  • For queues where somebody has to read before routing
  • Three lanes, not two: automatic, assisted, and fully human
  • A human-lane fallback for the day the provider is down

Reply within 1 working day ยท pilot before build, always

Dhanush Prabha Co-Founder, CTO and CMO

Builds the extraction, tunes it on your real documents, and designs the exception queue.

How an item flows

Read ClassifyExtractDetect language
Score Confidence per categoryCost of a misroute
Route Straight throughAssistedFully human
Record What it sawWhat it concludedWho acted
Correct ReassignmentBack into the evaluation set
  1. InputTriggers and document types
  2. ExtractRead, classify, structure
  3. ActRouted into the system of record
  4. OutputAn exception queue, staffed
ServingKanpur
StartPilot on your items
RoutingThree lanes
FallbackBuilt first

01 Choosing

When does a workflow need a model in it?

Only when the routing rule depends on something nobody has typed yet. In short, if the field exists, use rules; if the answer is inside a paragraph, the paragraph has to be read first.

Where the routing input comes from, and what that implies
The routing ruleWhere the input livesUse
Above โ‚น2 lakh goes to financeA value field on the requestRules. Cheaper, faster, no review needed
Capital spend goes to the boardA category picked from a dropdownRules
Warranty claims go to the service deskBuried in a customer's emailA model, then rules on what it found
Urgent complaints escalate immediatelyTone and content of free textA model, with a conservative threshold
Contracts renewing in 60 days go to legalInside a PDF nobody has readA model to extract, rules to compare dates

The honest default is rules

We will tell you to use plain workflow automation whenever the routing field already exists, because rules cost nothing per item, fail loudly, and never need a review lane.

02 Design

Why three lanes instead of two?

Because the middle one is where the saving is. Most designs offer automatic or manual and lose the large band of items where the model is probably right and a person can confirm in seconds.

  • 01

    Lane 1 ยท Straight through

    High confidence, in a category whose errors are cheap. Routed with no human involvement, logged with the confidence score.

    Automatic
  • 02

    Lane 2 ยท Assisted

    Medium confidence. Reaches a person with the model's suggestion pre-filled, so the work is a confirmation rather than a reading task.

    Confirm, seconds
  • 03

    Lane 3 ยท Fully human

    Low confidence, or a category whose errors are expensive. Arrives with nothing pre-filled, because a wrong suggestion is worse than none.

    Read and decide

The assisted lane was the surprise. Roughly half our tickets now arrive pre-classified with a suggestion, and confirming one takes a second instead of a minute of reading.

Service desk managerEquipment servicing company

03 Delivery

How we build one

Five steps, and the first is allowed to end the project by concluding that rules would do the job better.

  1. Step 1: Check the field is not already there

    If the routing input exists as a field somewhere, rules are cheaper, faster and need no review lane.

    Week 1
  2. Step 2: Pilot the classification

    100 to 300 real items with the correct routing recorded by your team, and accuracy reported per category rather than as one average.

    Weeks 1 to 3
  3. Step 3: Set the lane boundaries

    Per category, from the cost of a misroute. A cheap-error category gets a wide straight-through band; an expensive one may have none.

    Week 4
  4. Step 4: Build the exception desk and fallback

    Ownership, a service level, escalation, and a rule that routes everything to people if the provider is unavailable.

    Weeks 4 to 8
  5. Step 5: Close the correction loop

    Every reassignment recorded and fed back into the evaluation set, so accuracy stays true and problem categories surface.

    Ongoing
  • Every routing decision records what the model was shown, what it concluded, its confidence, the lane it took and who acted, which is what makes a disputed route answerable later.
  • Where the workflow writes into the books, the proviso to Rule 3(1) of the Companies (Accounts) Rules, 2014, administered by the Ministry of Corporate Affairs, has required a non-disableable audit trail since 1 April 2023.
  • Where items carry personal data, the Digital Personal Data Protection Rules, 2025, notified on 13 November 2025 by the Ministry of Electronics and Information Technology, apply, with the main obligations commencing on 13 May 2027.

04 Honesty

The line we hold on decisions

A model can prepare a decision. It should not make one that somebody has to answer for. Three cases where the human stays in the seat rather than in the loop.

  • An approval. Extracting the value, checking it against policy and routing to the right approver is appropriate. Approving it is not, because an accountable person has to own that outcome.
  • A rejection that affects somebody. A declined claim, a rejected application or a refused refund reaches a person before it reaches the customer. The model may recommend and must not conclude.
  • An escalation nobody can appeal. Any lane with no human touch anywhere needs a sampling review, because a category the model quietly gets wrong at scale is worse than one it gets wrong loudly.

Where this goes wrong in practice

A straight-through lane set too wide during a quiet month, then left in place when the document mix changed. It is why the lane mix and the misroute rate are on the dashboard from day one.

05 Reference

Terms used on this page

Confidence-based routing
Sending an item down one of three lanes according to model confidence: automatic, assisted with a suggestion attached, or fully human.
Exception desk
The staffed queue receiving items the model could not route confidently, with ownership, a service level and escalation.
Straight-through rate
Written STP rate: the share of items routed with no human involvement.
Misroute rate
The share of items that reached the wrong owner and had to be reassigned, measured after go-live rather than assumed from the pilot.
Correction loop
Feeding every reassignment back into the evaluation set, so accuracy stays measured and problem categories become visible.
Anchoring
The tendency to accept a confident pre-filled suggestion. The reason the fully human lane deliberately presents nothing.
Data Protection Board of India
The adjudicating body under the Digital Personal Data Protection Act, 2023. The Schedule to that Act sets fixed rupee ceilings rather than a share of turnover: up to ₹250 crore for failing to take reasonable security safeguards against a personal data breach, up to ₹200 crore for failing to notify the Board and the people affected, and up to ₹50 crore for a breach of any other provision. That is why consent, retention and access control are build decisions here rather than paperwork.

06 Questions

AI workflow automation in Kanpur: FAQs

By the number of queues, the daily volume in each, and how many lanes the routing needs, none of which changes with the city. One queue with classification, extraction and confidence routing is the starting unit for a team in Kanpur. Queues added after the first cost less each. Every quote states IncorpX professional charges; model usage is billed by the provider at actuals, and any government fees are billed separately at actuals.

Next step

Name the queue somebody reads all day just to sort it.

We will pilot the classification on your own items and come back with the accuracy per category, the share that could route straight through, and a fixed quote.

Read by Dhanush Prabha, our CTO, not a form queue. Reply usually within one working day, in your time zone.

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