Article summary
Run bulk TG registration and activity checks with clean country-code handling, separate task queues, traceable Excel results and a clear exception process.
A bulk Telegram account check succeeds when every input row receives an explainable disposition. The main challenge is not uploading thousands of lines; it is keeping registration, activity, invalid formatting, missing results and permission states from collapsing into one vague “account status.”
Before the run: split the list into three queues
Create a ready queue for numbers with reliable country codes and valid normalization, a repair queue for fixable source problems, and a reject queue for records that cannot be resolved without guessing. Only the ready queue becomes the TXT file.
Retain source IDs and authorization data inside the company’s system. The upload should contain one normalized phone number per line.
Registration is the first Telegram question
The TG registration task returns phone number and whether it is registered. Use it when the decision is platform presence. Do not ask for activity or profile fields until the registered cohort and business need are known.
A not-registered task result is not a telecom dead-number diagnosis. Keep those statuses in different columns.
Activity is a second, optional branch
The TG activity service can return UserID, username, offline time, active days, first and last names, VIP state and frozen state in addition to the phone number. Route only the approved records into this branch. Broader fields create more handling and interpretation work.
Use activity for relative cohort analysis, not as a promise that an account will respond. Telegram’s privacy settings can affect last-seen visibility.
Operate the run with four checkpoints
- Input checkpoint: record source count, normalized unique count, repair count and TXT line count.
- Task checkpoint: record which TG service was selected and the expected Excel headers.
- Result checkpoint: preserve the original workbook and verify its task and row count.
- Join checkpoint: match results to source IDs and account for every exception before release.
Use an exception queue instead of manual fixes
Give each exception one reason: missing country, invalid length, duplicate input, result missing, duplicate result, join conflict, unexpected schema or unavailable field. Assign an owner and next action. Manual repair inside the master workbook destroys reproducibility.
If an error affects many rows from the same source, fix the transformation and rerun that subset. Do not patch each row separately.
Scale by partitioning, not by removing controls
When the ready queue becomes large, divide it by country, source system or normalization rule. Each partition receives its own batch ID and can be stopped or rerun independently. This makes a formatting problem visible before it contaminates the entire workload.
Keep the selected task constant inside a partition. If some records need registration while others need activity, create separate child batches and preserve the relationship to the original request.
Review the run as an operating metric
Track preparation rejection rate, processing turnaround, return coverage, join success and exception resolution time across batches. A rising rejection rate may indicate a new source-system problem; weak join success often points to inconsistent normalization rather than a Telegram issue.
Use these measures to improve the pipeline. Do not turn them into claims about the audience until the underlying data question supports that interpretation.
Return statuses to CRM as observations
Append the run date, task, result and batch ID to a history table. Keep the raw phone number, normalized key and returned identifiers separate. A current-state view may select the latest approved observation, but the history should remain available for audit and correction.
Apply opt-outs and other suppression rules in a separate activation layer. A positive TG check never overrides a suppression decision.
How to know the bulk run is finished
- The original source population is accounted for.
- TXT and Excel artifacts are tied to one batch ID.
- The chosen task and returned schema match.
- Joined, unmatched and duplicate counts are reported.
- Unknown values remain unknown.
- CRM updates are idempotent and reversible by batch.
- Transient files have a retention deadline.
AIPUSH supplies the TXT-to-Excel TG checking step. The surrounding queue design turns it into a reliable bulk operation. When the queues and checkpoints are explicit, increasing volume does not require lowering the quality standard.
