Practise the conversations that matter.
Realistic AI simulations that let professionals rehearse difficult conversations, get structured feedback and improve before it matters for real.
A recorded InView practice call. Mel, a simulated character, talks on video while the conversation appears as a transcript.
Transcript:
Mel: Not really, no. Since the diagnosis it’s all been a bit… scattered. I’m not sure who’s supposed to be doing what, to be honest. I don’t really know what happens next and nobody’s been very clear about it.
Learner: The reason I raise that is because it sounds quite confusing to coordinate the professional response at the moment for you.
Mel: Yeah, that’s exactly it. Confusing is the right word. I don’t know who to listen to or where to even start. And I’m tired of having to chase people and explain everything from the beginning every single time.
Learner: Would it be helpful if I took that role?
Mel: What would that actually look like though? I don’t want to get my hopes up and then find out it’s just another phone call every few months where nothing really changes.
Important conversations deserve more than theory.
Professionals are often expected to handle complex, emotionally demanding conversations with very few chances to practise. Training can explain what good practice looks like, but it rarely offers a safe place to try the conversation, make mistakes and try again.
InView builds that place. We work with organisations to create practice around the conversations their people actually face: the situation, the character they talk to and the skills the feedback focuses on.
Good conversations are a skill. InView gives people somewhere to practise them.
How it works
Have the conversation.
The learner talks with a realistic simulated character, in a situation drawn from their work.
See how it went.
InView reviews the conversation for them: how it landed, what worked, where it could land better and a phrase to try next.
Talk it through.
An AI tutor, connected to their conversation and review, helps them explore the feedback and think about another approach.
Try again.
They go back into the conversation with something new to try.
Feedback grounded in what was said.
The review is written for the learner, so they are not left to analyse a transcript. It is designed to focus on what was actually said in the conversation, rather than guessing at intent. Here is the review from the same Mel demo.
Watch the full demo on YouTube (opens in a new tab)Step 4 · Feedback
Your call with Mel
A short, warm reflection on how the call landed: what worked, where it could land better, and a phrase to try next time.
How Mel experienced the call
Mel started the call feeling tired and guarded, particularly after previous unhelpful experiences. However, the learner’s persistent, open approach, and particularly the recognition of Mel’s exhaustion and the offer of concrete, bespoke support, helped Mel to feel heard and less overwhelmed by the scattered nature of her current support. She ended the call appreciating being listened to and with a clear, small next step.
Skill breakdown
Gentle indicators, not a grade.
- Finding the main concernStrong
- Reflective listeningGood
- Clear next stepGood
- Warm openingGood
- Responding to emotionStrong
What went well
- The learner offered a strong opening by acknowledging Mel’s tiredness: “That sounds really challenging.” This immediately validated Mel’s emotional state.
- The learner acknowledged Mel’s weariness with generic support and focused on finding what would feel ‘doable’ and helped Mel feel heard, which she explicitly appreciated at the close of the call: “I appreciate you actually listening today. It makes a difference, that.”
Where it could land better
- Early in the call, after Mel indicates she’s tired, the learner shifts the focus directly to “Tell me about the call that you had about Daniel.” This missed an opportunity to acknowledge Mel’s current state more deeply before moving to the requested topic.
- When Mel expressed being ‘guarded’ and wary of being passed around or just given a leaflet, the learner could have reflected this feeling directly to build rapport, rather than immediately moving to “What would make this course successful from your perspective?”
Try this phrase next time
“It sounds like you’ve been through a lot already and are wary of more generic advice. I want to make sure I’m really hearing what would be most helpful to you right now.”
Early feedback from practitioners
From a family support team at a children’s charity, after trying an InView demo.
It felt realistic to the kinds of calls we receive and the bot was able to create a situation I come across often in support calls. I felt able to practice active listening skills well through the training.
Family support team member, children’s charity I think this could work well for us because it allows us to test and practice different skills in a safe space. Using a bot might feel more comfortable than practicing with a colleague.
Family support assistant and safeguarding deputy, children’s charity
Built from the gap between knowing and doing.
InView began with a gap we had experienced in frontline practice: there were few meaningful training opportunities for working with people who perpetrate domestic abuse, and almost nowhere safe to rehearse the conversational skills that work demands.
That problem reaches far beyond one area of practice. Across social care, safeguarding, health, education and other public services, people have to navigate conversations where trust, judgement and language matter enormously.
We created InView to provide the practice space we wished had existed: realistic enough to matter, safe enough to experiment in, and focused on helping people improve.
Our backgrounds are in frontline practice, psychology, learning and technology.
Jordan Clarke
Co-founder, Social Worker and Technical Lead
A Children and Families Social Worker with more than ten years’ experience across domestic abuse, serious youth violence, mental health and relational practice. Jordan also leads the technical side of InView, shaping the product and the AI experience.
Haydar Munthadar
Co-founder, Countering violent extremism
A Countering Violent Extremism practitioner with more than ten years’ experience and a national commendation from Counter Terrorism Police Headquarters.
Duncan Fennemore
Co-founder, Psychology and learning design
A psychologist and education specialist with more than 45 years’ experience across behaviour change, leadership, coaching, education and organisational training.
Which conversations matter in your organisation?
If there is a conversation your people need to feel better prepared for, we would be happy to explore it with you.