Build or buy? It's a question almost as old as enterprise software itself. Large organisations have always built their own technology. Sometimes it's because their requirements are too specific for what's on the market. Sometimes it's about control, security or integration. And sometimes it's because an engineering team looks at an existing product and thinks: we could build that.
Live captioning is no different. And today, you can probably build your own live captioning tool faster than you think. So why are teams that have already done exactly that turning to CaptionHub after investing huge sums of money over 2-4 year time periods of lost audience engagement?
AI has changed the economics of building software. Tools like Claude Code, Cursor and GitHub Copilot mean teams can build and prototype at a speed that would have seemed ridiculous a few years ago. You can connect a speech-to-text model, layer in translation, build an interface around it and have something genuinely impressive working remarkably quickly.
But while AI has made building easier, it hasn't made the build-vs-buy question disappear. If anything, it's made the answer more interesting.
We're increasingly talking to teams that have already built live captioning in-house and then discovered its limitations when it really matters: captions dropping out halfway through an event, important talent names being completely mangled, profanity filters failing to catch what they should, or translations going badly wrong in front of a local audience.
These aren't hypothetical edge cases. They're real things that happen when a system that worked perfectly well in development meets the unpredictable, very public environment of live production.
There's nothing wrong with building and experimenting internally — it's how some of the best enterprise technology gets created. But a live broadcast or flagship event is a high-stakes place to discover what your internal tool hasn't accounted for yet. There's very little room for trial and error when the audience is already watching and there’s no opportunity to rewind. For the teams we speak to, that's exactly what brought them to CaptionHub: they don't want their next major live moment to be another test of the technology.
And that's really the distinction. Getting captions onto a screen is increasingly straightforward. Delivering accurate, perfectly synchronised multilingual captions reliably every time you go live is a very different standard.
So yes, AI has made live captioning easier to build, but not easier to get right. The gap is between something that works and something you can trust in front of your largest live audience. From the teams we’ve spoken to who've already crossed that gap — or tried to — these four things come up again and again.
1. Accuracy gets much harder in the real world
One of the things teams tend to discover when an internal live captioning tool moves into production is just how different the real world looks from the happy path.
One speaker, clear audio, a familiar language, no background noise, no unexpected names, no provider outage, no one talking over anyone else. It works beautifully, and then you put it into a real event.
Suddenly you're dealing with a panel discussion where three people are talking over each other without accurate speaker detection, a keynote speaker whose name the model has never encountered, a product launch with dozens of proprietary terms, a live sporting event where the pace of speech changes constantly, or an international audience that needs captions in multiple languages simultaneously.
This is exactly where generic speech recognition starts to struggle. People and product names, specialist terminology, fast-moving content and brand-specific language are some of the hardest things to get right. A wrong name might seem like a minor accuracy issue in a transcript; put it on a giant screen in front of a live audience and it becomes a very different problem.
This is exactly the kind of complexity Timbra, CaptionHub's live captioning and translation platform, is built to handle. Teams can add their own terminology and context so names, brands and specialist language are understood within the context of the event, rather than relying on a generic model to figure it out on the fly.
Then there's the quality of the captions themselves because speech can be messy. People interrupt each other, use idioms, change direction halfway through a sentence and speak with different accents. Add translation and profanity filtering across multiple languages and what looked like a relatively simple workflow gets complicated quickly.
CaptionHub's Natural Captions® technology is purpose-built for spoken language, using context to improve grammar, sentence structure, punctuation and pacing so captions feel natural rather than machine-generated. It understands idioms and meaning even when speech is fast, messy or imperfect, helping deliver captions that are broadcast-ready without relying on teams to constantly clean up raw AI output.
Accuracy is also only useful if the caption arrives when the audience needs it. In a live environment, even a highly accurate caption becomes a poor experience if it appears several seconds after the speaker has moved on. As you add transcription, translation, processing and delivery into a workflow, every stage has the potential to introduce latency.
The production standard isn't simply generating the right words. It is delivering accurate, natural captions to the right destination, perfectly synchronised with the content — consistently, when you're live and there's no opportunity to try again.
2. Production-ready means planning for things to fail
Then there's what happens when something underneath your system fails.
If you're building your own solution around a single transcription provider, you're inheriting that provider's availability as part of your own system. If they go down, you go down. For a normal piece of software, that might be an inconvenience but for llive captioning, it can mean your accessibility layer disappears halfway through an event.
Production-grade resilience means designing for that possibility from the outset. That includes multiple providers, failover logic, monitoring that identifies when something has gone wrong, and the ability to switch without disrupting the audience experience. Timbra uses primary and secondary transcription services with failover options, while translation can work across leading providers with fallback when a preferred provider doesn't support a particular language.
But technical redundancy is only one part of reliability. There's also what happens when something unexpected goes wrong five minutes before you're due to go live.
This is another challenge we see with internally built tools: the people who know the system best aren't necessarily the people responsible for running the broadcast or live event. Live captioning might be one of dozens of technologies an engineering team supports, the engineer who originally built it may have moved onto another project or left the business, and fixing an issue now means competing with every other engineering priority.
As the tool becomes more business-critical, so does the resource required to support it. Provider changes, new integrations, unexpected edge cases and last-minute production requirements all need someone with the knowledge and availability to respond.
That's a significant part of what teams get when they move to CaptionHub. Customers have direct access to senior technical expertise, with response times measured in minutes rather than support queues and many layers of escalation. Our senior product and engineering teams stay close to customers too, so the expertise required to keep live captioning running isn't dependent on one internal engineer being available at exactly the right moment.
Production ready isn't just about how a system performs when everything goes to plan. It's about the technology, infrastructure and expertise already being in place when it doesn't.
3. The requirement quickly outgrows the tool you originally built
There's another pattern we see when teams turn to us after building internally: the requirement they have today often isn't the requirement they originally built for.
Let’s say you start with live captions for a stream, then someone from another department wants captions in the room, another team wants the transcript for VOD, marketing is now asking for clips for social, a new market is launched and suddenly you need translation, your events portfolio has scaled and you now have multiple events, one team needs access through an API but another workflow needs to be automated.
Before long, the original captioning tool is being asked to support an increasingly complex content operation. That doesn't mean the original build failed and actually often, the opposite is true, it worked well enough that more of the business wants to use it.
But success changes the requirement. Now the decision is whether to continue adding functionality internally, introduce additional point solutions around it, or move to a platform that already supports those workflows. If your existing localisation operation already involves one vendor for live captioning your broadcast, another for your in-room events, another for transcription, another for translation, another for VOD and someone else for voiceover, it's understandable that building internally can initially look like a route to simplification.
But the choice isn't necessarily between stitching together five vendors and building an entire technology stack yourself. Timbra sits within CaptionHub's wider cloud-native localisation platform, supporting live, linear, VOD, in-room and social content alongside transcription, translation, workflow automation and voice technology.
Instead of continuing to build adjacent functionality around a standalone live captioning tool, teams can consolidate those workflows onto a platform already designed to bring them together.
The architecture matters too. Timbra takes a cloud-first approach, sitting outside the signal chain so teams can add and scale captioning without rebuilding the rest of their broadcast infrastructure. Live feeds can come in via HLS, RTMP and SRT, with captions delivered into the platforms and players you're already using, including Brightcove, Mux, YouTube, and Dolby OptiView.
Whether you're broadcasting live sport to a global fanbase, producing a flagship conference for a Fortune 500 client or captioning a product launch across five time zones, Timbra delivers perfectly synchronised multilingual captions wherever they need to go — from broadcast and streaming to in-room screens and audience devices.
Some of the teams we speak to didn't start looking for CaptionHub because their internal live captioning stopped working. They started looking because the requirement had outgrown the tool they originally built.
4. The economics change when you compare like with like
AI has undoubtedly changed the economics of the building where what once required a significant engineering project can now be prototyped remarkably quickly, and that deserves to change the build-vs-buy calculation. However, comparing the cost of building a working prototype with the cost of specialist technology isn't comparing like with like.
The comparison only becomes meaningful when both solutions are held to the same production standard: accuracy across real-world speech, perfect synchronisation, multilingual scale, resilience when providers fail, security, monitoring, integrations, QA, technical support and the ability to perform consistently across every live environment where the business needs it.
Then there's the cost over time. Models and providers change, security requirements evolve, integrations need updating, new languages and use cases emerge, the original engineers move onto other priorities and suddenly what began as a relatively contained build becomes technology the business needs to maintain and develop for as long as it relies on it.
The real cost of an internal captioning solution isn't just building version one. It's also committing to owning version 17 three years from now. And that's the pattern behind many of the teams that turn to CaptionHub after building internally.
They haven't necessarily failed to build live captioning. Often, they've successfully proved that they can. What changes is the calculation around what it takes to keep that technology production-ready: the ongoing engineering resource, maintenance, infrastructure, security upkeep, support and development required as providers change, requirements expand and the business expects more from it.
The decision stops being “can we build this?” and becomes “is live captioning a product we want to keep investing in and developing ourselves?” And that's where the build-vs-buy calculation starts to look very different.
What these teams are really looking for
The teams that turn to CaptionHub after building internally aren't usually looking for access to better AI. Their engineering teams have access to the same rapidly evolving models and tools as everyone else.
They're looking for everything required to turn that technology into something they can rely on in production: accurate, perfectly synchronised captions; infrastructure and integrations built for live workflows; failover and monitoring; security and quality controls; terminology management; and technical expertise that's there when something unexpected happens.
And increasingly, they're looking beyond the original live captioning requirement too. As use cases expand across languages, teams, formats and markets, what started as a single internal tool can become part of a much bigger localisation operation.
That's the environment Timbra has been built for.
Trusted by some of the world's biggest brands, including NVIDIA, AWS, BBC, Augusta Golf and more, Timbra is designed for teams across broadcast, live production, localisation, accessibility, content operations and digital media who need multilingual captions they can rely on when content is live.
Captions reach live players within seconds, with support for 250+ simultaneous languages, while failover options help keep captions running if a provider becomes unavailable.
Timbra is cloud-first and context-aware by design, combining leading AI transcription and translation technology with the infrastructure and controls needed to use it reliably at broadcast scale. Custom terminology and context help teams get the names, brands and specialist language that matter to them right, while integrations across existing broadcast workflows mean captioning can fit into the technology they're already using.
And because Timbra sits within CaptionHub's wider localisation platform, those workflows don't have to stop at live. The same platform can support live, linear, VOD, in-room and social content alongside transcription, translation, workflow automation and voice technology.
The story we're seeing isn't that businesses tried to build live captioning and couldn't, they did build it. What changed was the standard they needed it to meet: from working to production-ready; from a single use case to a growing global requirement; and from an engineering project to technology the business would need to support, maintain and develop long-term.
AI has lowered the barrier to building live captioning. It hasn't lowered the bar for putting it in front of an audience.
For a growing number of teams, reaching that bar consistently is why they turn to CaptionHub.
If you're weighing up whether to keep building live captioning in-house or move to a platform built for production, book a demo to see how CaptionHub's Timbra handles live multilingual captioning at scale.
