For software agencies
Keep supporting clients.
Keep building what’s next.
Clients need answers and fixes long after delivery. Maito investigates support requests and prepares fixes for review, so your developers spend less time on recurring support work. Handle requests together in Slack or through the AI assistant you already use.
Use your own AI provider. $149/month per workspace.
Illustrative workflow. Findings and fixes depend on your project and connected tools.
The project ships. The support keeps coming.
A broken export, a question about billing, a small fix in the issue tracker. Each request needs someone to understand the project, find the cause, and work out the next step. Maito takes on that support work using the code and connected tools, and brings back answers, evidence, or a fix for your team to review.
@Maito Acme asks why a wholesale buyer sees a different price from the catalog. Can you check?
This buyer has an account-specific price list. The storefront uses their agreed prices from the ERP instead of the public catalog price.
Answer questions about the delivered system
Help your support team explain pricing, permissions, and business rules using the project’s code and documentation.
@Maito ServiceNow case CS001842: Acme’s new orders aren’t reaching the ERP. Can you investigate?
The sync rejects orders containing the new SKU format. I reproduced the failure and traced it to the order mapping code.
Investigate client incidents
Work from a support case, trace the failure through the code and available logs, and give your team evidence for the next step.
@Maito Jira ACME-184 describes the SKU mapping fix. Implement it and check both SKU formats.
Updated the mapping and added checks for both formats. The order sync tests pass. The pull request is ready for review.
Resolve small support tickets
Turn a well-described ticket into a proposed code change, run the relevant checks, and bring back a pull request for review.
@Maito Check Acme’s open support cases and the linked Jira tickets each morning.
Two cases have fixes waiting for review. One still needs logs from the client. I drafted an update for each case.
Stay on top of open support work
Check unresolved cases, follow the progress of fixes, and prepare updates so your team knows which requests still need attention.
Get the investigation moving while the client waits.
Your project manager has the client’s report, but needs a developer to check the logs before they can give an answer. With Maito, they can start that investigation in Slack. An analyst can add what was agreed with the client, and a developer can review the findings and proposed fix. The request, evidence, and discussion stay together.
@Maito Acme says a customer received two order confirmations. Were they charged twice?
There’s one charge, but two orders. The payment provider retried its webhook, and the order handler processed it twice. This matches an unresolved ticket from last month.
Check which other orders are affected. Prepare a fix and a reply we can send to Acme.
I found three affected orders, with no duplicate charges. The fix prevents repeated webhooks from creating another order, and the regression checks pass.
Draft customer reply
We confirmed that your customer was charged once. A repeated payment notification caused the duplicate order confirmation. We’ve identified the cause and are reviewing a fix to prevent it happening again.
Support needs the context, even when the original team has moved on.
Six months into a support contract, the person who knows the project may be working elsewhere. A new request still needs the original requirements, previous decisions, and history of what was fixed. Maito draws on the project’s documentation and recorded support work, so the next person can pick up the issue with that context available.
Connected sources
Maito uses your project’s code, documentation, logs, and tickets to find the context behind each issue.
Previous investigations
Findings and evidence from earlier issues stay available, so the next investigation can build on work already done.
Team decisions
Your team’s decisions carry into future work, with the original conversation available for context.
Completed fixes
The resolution, checks, and pull request stay with the issue, so Maito can refer to what changed and why.
Keep open issues and recurring errors from piling up.
Client support includes checking whether an error has returned, chasing an unresolved issue, and updating guidance after a fix. Set up Loops to do those checks on a schedule or when something happens. Maito brings findings, proposed fixes, and documentation updates back for your team to review.
Investigate client support cases
Pick up a client’s report, check the project’s code and available logs, and bring back the cause with evidence for your team.
Check production errors
Check new errors in client applications, trace them to the relevant code, and prepare a fix for review.
Debug failed integrations
Find out why orders, products, or customer records stopped syncing. Check the logs and mapping code, and explain where the failure started.
Investigate access problems
Check account settings, permissions, and the project’s access rules to explain why a client’s user cannot sign in or use a feature.
Investigate failed releases
Review failed pipeline jobs and recent code changes. Find what blocked the release and prepare the next step for your team.
Find recurring issues
Connect repeated client reports with known bugs and production errors. Identify the underlying problem and suggest a lasting fix.
Follow up on open issues
Check which client requests are waiting on a fix, a review, or more information. Flag stalled work and draft updates for your team to send.
Keep support docs current
Compare resolved cases and code changes with the project’s support guidance. Draft missing answers and update outdated instructions for review.
How your client support work runs.
Your agent keeps project context and tool connections on a dedicated computer. Coding tasks run in separate sandboxes, with credentials supplied through Vault.
Your agent runs on its own cloud computer, with your project context, connections, and work history. It keeps working when you close Slack or your AI assistant.
Keep client support moving.
Help clients get answers and fixes while your team keeps building.