Conversational AI and Voice AI Development built for the conversations you are already having.

Conversational AI development builds software that understands human conversations, spoken or written, and turns them into structured, usable data. In practice that means transcription (Whisper, AssemblyAI), language-model processing (GPT-class models), and integration into the tools your team already works in. It covers call intelligence, meeting intelligence, voice pipelines and conversation analysis. The same phrase is used across the industry for customer-facing chatbots and voice agents that talk back; that is the other half of the category, and this page is about the listening half.

There is no honest headline price, so we scope before we quote. Four things drive the number: how much audio you process, how many systems the output has to reach, how high the accuracy bar is, and what compliance obligations the data carries. The 30-day pilot keeps the first commitment small and fixed in scope, and the estimate for the full build comes out of what the pilot measured. If you want an ongoing team instead, that is team extension, billed monthly per team member.

In our delivery experience the answer is weeks rather than quarters, provided the recordings exist and somebody can grant access to them. Our fixed-scope conversation intelligence pilot runs 30 days and ends in an accuracy readout on your own recordings and a decision. A production build after that depends mostly on how many of your systems the output has to reach, which we map during scoping.

Yes, and it is usually the larger half of the work. Talkmetry, our own product, is call intelligence living inside HubSpot. Snaplore works standalone or alongside Slack and Google Workspace, and its assistant joins meetings on Zoom and Google Meet; keeping those integrations stable across fragmented platforms was the hard part of building it. We map your integration surface during scoping, because that is where the schedule risk lives.

Buy when a generic feature set fits your process and your recordings can live in someone else's cloud. Build when the value sits in your own workflow, vocabulary or compliance rules, or when the output has to land inside your systems. We operate our own products, so we can read your case honestly, and sometimes the answer is a tool.

We do not know until we run it on your recordings, and neither does anyone quoting you a percentage before hearing them. Accents, crosstalk, line quality and domain vocabulary move accuracy more than the choice of model does. So the pilot builds an evaluation set out of your own conversations and scores the output against it, which is the AI QA and evals discipline applied to audio. You get the numbers, including where the pattern falls short.

Conversation data is as sensitive as data gets. We are ISO 27001:2022 and ISO 9001:2015 certified, renewed through a multi-stage audit with Quay Audit UK, and we design each build around data minimization, access controls and your residency requirements, including where transcription runs, where model processing runs, and what is kept afterwards. Prompt injection and data leakage are reviewed with our in-house security practice, a dedicated security engineer plus a part-time senior security consultant. The people who would handle your recordings are unicrew's own employees, working in Ukraine, Poland, Estonia and the UK. We are not SOC 2 certified and will not claim otherwise.

The proven core is Whisper and AssemblyAI for transcription, GPT-class models for processing, WebRTC for real-time audio and video, and AWS for infrastructure. That is the stack behind Snaplore, and we pick per case rather than per habit. The pattern matters more than the logo list: audio in, transcription, model processing, structured output inside the tool your team already works in.

Every full-service vendor claims transcription, processing and integration, this one included, so the category separates nobody. Three checks do, they work from outside, and they should be put to us as well. First, ask what accuracy anyone will commit to before hearing your audio, and how that number would be produced and on whose recordings. Ours is that nobody honestly can, which is why a pilot starts by building an evaluation set out of your own conversations. Second, ask where the audio goes and what is kept, because transcription and model processing each run somewhere and retention is a decision rather than a default. Ours is settled in writing at scoping, before a recording moves. Third, ask for a link to a review rather than a score: each quotation in the grid above opens the client's own Clutch interview.

When your team handles a handful of conversations a week, because a person reading them is cheaper than any pipeline we could build. When nobody will own the structured output, since a field nobody agreed to use changes nothing. And when what you need is a contact-center voice bot on a telephony platform in a few weeks: hire a specialist in that platform, because on that job we would be learning your dialer on your budget. If the job is not a conversation at all, that is AI agent development.

Three things: a sample of real conversations to pilot on, a technical contact who can arrange secure access to them, and agreement on the single output that would matter most, whether that is a call summary, a compliance check, or a field in your CRM.