You're in a meeting with engineering when someone says:
"We need to decouple the service before we migrate the workload."
Everyone keeps talking.
You nod.
But somewhere between decouple and workload, you lost the thread.
So you have a few options.
Interrupt the meeting and ask someone to explain it. Write it down and look it up later. Open an AI tool and figure out what to ask. Or keep nodding and hope the next five minutes make it clearer.
For product managers, consultants, executives and other people who work alongside technical teams, it's a familiar problem.
AI has gotten remarkably good at helping us capture what happened in a meeting.
But there's another problem worth solving:
What if you need help understanding the conversation while it's happening?
That's the problem behind hmm?.
What is hmm??
hmm? is an AI-powered technical translation platform that helps people understand complex technical conversations in plain English and in the context of their role.
hmm? is built for product managers, project managers, consultants, executives and other professionals who regularly work with technical teams but don't necessarily share the same technical background.
hmm? is a software product, not a large language model (LLM).
Instead of simply telling you what was said, hmm? is designed around a different question:
What does that actually mean?
The goal is simple: reduce the friction between confusion and understanding without requiring someone else to stop what they're doing and translate for you.
The problem isn't always information. It's understanding.
Employees have access to more information than ever.
Meeting transcripts. AI notes. Chat histories. Documentation. Recorded calls. Summaries.
But having information and understanding that information are two different things.
Imagine a product manager receives this message from engineering:
"The API is hitting rate limits because the downstream service isn't handling the current request volume."
A meeting summary or AI assistant can capture that sentence perfectly.
But the product manager may actually need to know:
What's happening? One of the systems can't keep up with the number of requests it's receiving.
Why does it matter? Users could experience failures or delays.
What should I ask next? Is this temporary, or do we need to change the system before usage grows?
Those are different problems.
One is information capture.
The other is comprehension.
Meeting summaries and technical translation solve different problems
AI meeting assistants have made it significantly easier to capture conversations.
That's useful.
If you miss a meeting, need action items or want a record of what was discussed, a summary can save time.
But imagine you're actually in the meeting.
Engineering mentions a dependency.
You don't know what that dependency does.
Two minutes later, the team is discussing migration risk.
Then somebody asks whether the roadmap needs to change.
You may eventually have a perfect transcript and summary.
But you're still missing the context you needed to participate confidently while the conversation was happening.
That's where technical translation is different.
A summary answers:
"What happened?"
Technical translation helps answer:
"What does this mean to me?"
Both are useful.
They're solving different problems.
Technical language means different things to different roles
Specialized language exists for a reason.
Engineers have it.
Finance teams have it.
Legal teams have it.
Marketing teams have it.
The challenge appears when information moves between those groups.
An engineer may hear:
"We need to refactor this before we scale it."
and immediately understand the technical implications.
A product manager may be thinking:
Does this affect the roadmap?
A project manager:
Does this change our timeline?
An executive:
Does this create business risk?
The words haven't changed.
The context each person needs has.
That's why useful technical translation isn't just about replacing complicated terminology with simpler words.
It's about helping people understand what technical information means for them.
Company jargon creates another layer of confusion
Technical terminology isn't the only language people have to learn at work.
Every organization develops a language of its own.
Internal acronyms.
System names.
Project names.
Product terminology.
Team abbreviations.
Legacy terminology everyone somehow knows except the person who started three weeks ago.
A general-purpose AI model may understand what API, Kubernetes or latency means.
It won't automatically know that an acronym inside your organization refers to a specific internal system, initiative or process.
That's why hmm? includes a company glossary.
The glossary gives hmm? additional context about the terminology an organization uses, helping make technical explanations more relevant to the people inside that organization.
Because sometimes the most confusing language at work isn't technical jargon.
It's company jargon.
How does hmm? work in Microsoft Teams?
hmm? for Microsoft Teams brings technical translation into a place where work conversations are already happening.
When you encounter a confusing technical message, you can use hmm? within your Teams workflow to get a clearer explanation and additional context.
The experience is designed to make the distance between:
"I don't understand this."
and
"Now I do."
as short as possible.
Rather than copying a technical message, opening another AI application, explaining your role, adding context and figuring out the right prompt, hmm? is purpose-built around understanding technical communication.
And because company-specific terminology can be stored in the glossary, hmm? can incorporate more of the language your organization actually uses.
Why real-time understanding matters
There's an inherent limitation to relying entirely on post-meeting summaries:
The conversation has already happened.
Understanding something 30 minutes later doesn't always help when the decision is being made right now.
Maybe you would have asked a different question.
Maybe you would have identified a product implication.
Maybe the technical constraint changes something you're responsible for.
Maybe you simply would have been able to contribute more confidently.
That's why we're interested in something beyond better meeting notes.
We're interested in real-time understanding.
hmm? is being built around the idea that people should be able to get clarity closer to the moment confusion happens — whether that's inside a written conversation or, eventually, while a meeting is happening.
Who is AI technical translation for?
You don't need to be completely nontechnical to benefit from technical translation.
In fact, many people who work closely with engineering understand most of the conversation.
The problem is often the remaining 10%.
It's the unfamiliar architecture term.
The infrastructure conversation that suddenly goes deeper than usual.
The acronym everyone else seems to know.
The explanation where you understand every individual word but somehow not the sentence.
AI technical translation can be useful for:
- Product managers working closely with engineering
- Project and program managers coordinating technical work
- Consultants working with technical client teams
- Executives evaluating technical decisions and risks
- Marketing and go-to-market teams working with technical products
- Customer-facing teams communicating with engineering
- New employees learning an organisation's terminology
- Anyone who has ever nodded through a technical conversation they didn't completely understand
You don't need to become an engineer.
You need enough context to do your job well.
Why not just use a general-purpose AI assistant?
You absolutely can.
General-purpose AI tools can be excellent at explaining technical concepts.
Copy a technical statement into one, ask it to explain the statement in plain English, and you'll often get a useful answer.
The opportunity isn't to create another place where you can ask AI a question.
It's to remove the steps between confusion and understanding.
Today that process might look like:
- Realize you don't understand something.
- Copy the technical statement.
- Open another application.
- Paste it.
- Explain your role.
- Write a prompt.
- Add relevant company context.
- Read the answer.
- Return to the original conversation.
We think there's a better question:
What if understanding was simply part of the workflow?
That's the experience hmm? is working toward.
AI is making technical work faster. Understanding needs to keep up.
AI is changing how quickly technical work gets done.
Code can be generated faster.
Documentation can be created faster.
Ideas can be prototyped faster.
Analysis can happen faster.
But as technical output accelerates, the rest of an organisation also has more to absorb.
More changes to understand.
More decisions to communicate.
More context to transfer.
More conversations moving at a faster pace.
AI can increase how quickly organisations produce information.
The next opportunity may be helping people understand it just as quickly.
Because faster work only creates value if the people who need to act on it can keep up.
Understanding shouldn't require pretending
Technical collaboration works best when people can ask questions.
But realistically, people don't stop every conversation every time they miss something.
Sometimes the meeting is moving too quickly.
Sometimes you don't realize you're confused until three sentences later.
Sometimes you don't want to derail the discussion.
And sometimes you just nod.
We think there's a better option.
hmm? is building AI for the moment between "I don't understand that" and "now I do."
Because you shouldn't have to be the most technical person in the room to understand what's happening in it.
Stop nodding along. Start understanding.