Troubleshooting for engineering & IT support teams
Know what broke, and why.
DeepTech Cloud reads your logs, error messages and documentation, suggests what most likely went wrong, and drafts the incident report. Your engineers check the work and decide what to do.
We're early and building this with the people who handle incidents every day. If that's you, we'd like to hear how you do it today.
Logs
09:36:52 INFO release v2.18.0 09:41:07 WARN pool wait 412ms 09:41:08 ERROR acquire connection timed out (5000ms) 09:41:08 ERROR POST /v1/orders 503 09:41:09 WARN retry 2/5 ord_1042
Likely cause
Connection pool exhausted after v2.18.0
Check next
- · Pool size, v2.17 vs v2.18
- · Idle-in-transaction queries
Why we're building this
Most of an incident is spent figuring out what you're looking at.
The clues are scattered.
Logs in one tool, runbooks in another, the last incident in someone's notes. Every investigation starts by collecting context.
We solve the same thing twice.
A new engineer hits an error the team fixed last quarter, and nobody can find how.
The write-up comes last, and late.
When the fire is out, people are tired. The report gets rushed, or never written.
Platform
From a wall of errors to a clear next step.
What we're building, feature by feature. Screens on this page show illustrative sample data.
01
Log analysis that shows what matters
Paste logs or error messages and see the related errors grouped, the first failure marked, and the lines worth reading highlighted.
3 related errors · first seen 09:41:08
02
Find the right doc without the tab hunt
Ask in plain language. Get the section of your runbooks or product docs that applies, with the source shown so you can read it yourself.
connection pool timeout after deploy
runbooks/database.md
Resizing the connection pool
postmortems/2025-11.md
Pool exhaustion after a config change
03
A root-cause draft you can edit
Symptoms, evidence, possible causes and what to check next, in one structured document. Observed facts and suggestions are kept apart.
- Symptoms
- 503s on POST /v1/orders from 09:41.
- Evidence
- Pool at 20/20; timeouts follow the v2.18.0 rollout.
- Possible cause
- Connections held longer after the release (unverified).
- Next steps
- Diff pool config; inspect idle transactions.
04
Updates people can actually read
Turn your notes into a clear summary for your team, or a plain-language explanation for a customer. Edit it, then send it your way.
Subject: Order creation errors this morning
Between 09:41 and 10:05 some orders failed to submit. The cause was a database connection limit reached after our latest release. We've adjusted it and are monitoring closely.
How it works
Four steps. You stay in charge.
- 1
Add what you have
Logs, error messages, incident notes.
- 2
Pull in context
Relevant documentation and past incidents.
- 3
Get a first read
Likely causes and checks, with the evidence.
- 4
Review and share
Edit the findings, then export the report.
Engineers make the final call. The assistant suggests; people verify before anything is changed or shared.
Product preview
Try a sample investigation.
Press Analyze to walk through the workflow with a sample incident. Everything here stays in your browser.
Sample data. Please don't paste real logs or secrets. 592/4000
Results appear here. Press Analyze to see a sample.
Solutions
For the people who get the page.
SaaS engineering
Triage production errors faster and keep investigation notes consistent across the team.
IT support
Make sense of error messages and recurring tickets with less manual searching.
DevOps & SRE
Line up deploys, config changes and log patterns while an incident is still open.
Technical support
Turn engineering findings into explanations a customer can follow.
Incident management
Draft timelines, summaries and post-incident reports from evidence you already have.
Principles
How we think about building this.
We're an early-stage company, and we don't claim certifications or compliance standards we haven't earned.
- 01
Evidence first
Every finding should point back to the log line or document it came from.
- 02
People decide
Suggestions are for engineers to evaluate. Nothing is applied automatically.
- 03
Careful with data
Technical data is sensitive. We aim to handle as little as we can and to be clear about it.
- 04
Facts and guesses, kept apart
What was observed and what the AI suggests are always shown separately.
Roadmap
Where we're headed.
Our current plan. It will change as we learn, and it isn't a promise of dates.
Now
Define the workflow
Work out which troubleshooting steps matter most and check them with engineers.
Next
Build the first version
An MVP for log analysis and root-cause report drafts.
Then
Connect your knowledge
Documentation search and answers that cite their sources.
After
Work with real teams
Pilot with early users, measure results, improve reliability.
Company
A small team working on a practical problem.
DeepTech Cloud is building an AI assistant for technical troubleshooting. We think investigating a problem should feel organised and explainable, not like searching for a needle in a stack of logs.
We're at the start: defining the workflow, building the first version, and learning from the engineers and support teams who handle incidents every day.
Deep Saha
Founder
Tell us how your team handles incidents.
We're shaping DeepTech Cloud with input from engineers and support teams. Tell us about your tools, your on-call reality, and what slows you down. We read every message and reply personally.
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