Speech recognition software development

AI agent development company Belitsoft develops custom voice AI agents that can answer phone calls, understand what the caller wants, pick up on context and tone, and speak as naturally as a human. If you do not want to build an AI agent from scratch, we help you choose the right AI agent development platform based on response speed, how good it can connect with your existing systems, your budget, and how your staff works, not just on who offers the most features. What the Custom AI Voice Agent Solution Can Do A voice AI agent works in real time - listens, understands the request, connects to your internal systems, completes the task, and confirms the result on the same call. It responds fast, does not talk over people, handles interruptions, and can complete tasks in your systems while staying on the call. During the call, the Ai voice agent can book appointments, check billing, take payments, update records, and transfer to a human when request falls outside defined rules or confidence levels. The caller doesn't need to repeat information during the transfer. An AI voice agent passes the call to a human with the full context attached. A voice AI agent lets you automate full customer conversations from lead qualification and product demos to onboarding, renewals, collections, or post sale support. A voice AI agent platform can scale to very high volumes, even up to one million simultaneous calls, after configuring telephony and cloud for that. What Benefits Custom AI Voice Agent Brings AI agents can cut support costs, and reduce missed calls by 50%, process 100% of inbound calls with a 30% transfer rate, achieve a 100% increase in resolution rate, ensure 100% consistent messaging tone and language, maintain sub-500 ms latency, and deliver an average response time under 7 seconds. Voice AI agent deployment can automate 60%+ customer support across dozens of products in several weeks. Significant reductions in operational costs in labor-intensive areas result from cutting down wait times and boosting response efficiency, effectively multitasking during peak times and after hours, and reducing operating and staffing expenses. The result fosters stronger customer relationships and loyalty, boosting engagement, satisfaction, and long-term retention. Custom AI Voice Agent Develop, Implementation & Support We manage the entire cycle of agent creation, from requirement gathering and design to implementation, testing, and deployment, making seamless integration with existing business systems, smoothly and without unforeseen risks. We identify which agent type best fits your industry-specific use cases, prepare high-quality data for training your LLMs, create ground-up model architectures, and fine-tune pre-trained foundation models. We develop dialogue models and voice personalities, continuously monitor performance, and quickly address issues as they arise. Our certified agentic AI experts serve trading, financial services, healthcare, and other verticals. Professional forward deployment engineer-led implementation ensures a smooth and secure deployment, fully managed. We help you avoid model lock-in, choose from the largest LLM providers, or bring your own model. We analyze agentic Voice AI platforms' pricing model, measure response delays during call, test how easily the AI agent platform connects to your systems, and check how fast it's possible to adjust prompts, workflows, and rules. Then we select an AI agent platform that fits your call types, launch a small pilot, review recordings and metrics, and help you scale from there.

What is Healthcare Voice Recognition Software? This type of medical software converts voice inputs into traditional written documents. It automates tiresome and mistake-prone operational tasks, providing clinicians with a tool to create and submit medical records in seconds. To reduce the clinical documentation burden, different solutions are used in clinical practice: Medical scribes (traditional, in-person staff) Virtual scribes (remote workers, often located offshore) Medical speech recognition software (physicians must actively dictate notes) Ambient speech recognition software(which passively listens to doctor-patient interactions and extracts relevant medical details, focusing solely on automatic note generation) Artificial intelligence (AI) assistants (voice-controlled EHR helpers that doctors speak to directly, allowing the AI to perform commands such as documentation, EHR navigation, order entry, communication, and workflow automation). Medical Documentation Challenges Challenges without Modern Voice Recognition Software A huge administrative burden forces physicians to spend additional hours on documentation. The problem leads to increased rates of errors, higher possibility of psychological issues among staff, and worsened productivity.  Doctors perform multiple activities during their interactions with patients. They take symptoms, assess health factors, review previous reports and health history, as well as schedule additional tests and appointments with other specialists. Clinicians need a tool that would be able to perform several tasks: transcribe voice in real-time, comprehend subtleties of the medical context to take notes correctly, and understand doctors’ commands. Challenges with Legacy Voice Recognition Software The healthtech market offers various solutions. However, their functionality differs, limiting some of the clients’ expectations. Here are the challenges that prevent efficient clinical workflow while utilizing voice recognition software. Finding the balance between the speed and accuracy of the tool might be difficult. Some voice-based digital assistants offer excellent accuracy while being slow in functioning. Others perform faster, but they require manual editing. Both options are frustrating for doctors and may add to aggregating stress Manual editing demands time, forcing users to examine, edit, and submit the notes to the electronic health record (EHR), therefore preventing instant availability of the data Difficulties with managing the system might occur, as doctors’ voices vary in tone, pitch, and accent. Those variations affect the accuracy of transcription Hampered transition between commands and dictations makes the functionality of an integrated voice assistant inconvenient When manual editing is required, privacy issues might occur. Those staff members who check the accuracy of the records need access to sensitive patient data, creating additional risk.  AI-assisted Voice Recognition Software Functionality Modern voice recognition tools are meant to become “invisible” doctors’ digital assistants. Such assistants provide clinicians with necessary patient data, accurately make notes, and answer special queries. The software usually performs the following features: Workflow integration and usability features Fit into the workflows of each individual clinician and understand different medical specialties, from behavioral health to surgery Combine voice dictations and commands in one tool with a seamless transition from the prior to the latter to enable clinicians to deal with documentation, coding, and looking through the data in the EHR  Analyze user’s intentions, navigation events, or cursor movements, allowing doctors to naturally dictate the data and then request details to fill in documentation sections if necessary  Comprehend the nuances of the country-specific healthcare environment  Provide additional assistance with answering inbox messages, compiling referral letters, and other administrative tasks. Data integration and accuracy features Integrate a large language model (LLM) to learn patient data, health history, and other relevant contexts to help doctors create notes Integrate the data with key systems, such as EHR, in two directions, i.e., using the EHR data to generate the note and leveraging the actual note to complete the fields in the EHR. Combine the information about medical context, i.e., patient details, doctor’s specialty, type of appointment, etc., with medically customized automatic speech recognition to select relevant terms and generate highly accurate notes. Keep a record of the notes, track and understand the text dictated at various sections, including situations involving issues like slow wifi or poor-quality microphones  Compile patient summaries using automated LLM-based summarization workflows to enable practitioners to prepare for patient visits and analyze data from previous emergency department visits, inpatient visits, and appointments with specialists   Benefits of modern AI-assisted Voice Recognition Software Healthcare systems that have already utilized AI and medical speech recognition products report the following improvements: 79% of users reported better documentation quality, 70% saw reduced burnout and fatigue, 81% of patients saw greater physician focus, 72% reduction in documentation time, 40% decrease in after-hours work, including weekend work, 20% increase in practice satisfaction, according to the research Single solution instead of applying several ones Supporting a broad number of clinicians of various specialties Allowing doctors to have a natural interaction with their colleagues and patients in the decision-making process Easy switching between dictation, request, command, or query options and relevant accuracy while performing those actions Elimination of the necessity to type and click while compiling documentation, resulting in high doctors’ speed How Belitsoft Can Help Healthcare software development companies like Belitsoft help healthtech startups create, customize and support speech recognition software solutions with the following capabilities: Real-time voice transcription for doctors to dictate notes Seamless switching between taking notes and processing voice commands Automatic synchronization of doctors’ notes with internal systems (EHRs, etc.) Customized note sections for doctors to easily navigate through medication history, health maintenance, etc. Embedded personalized templates for doctors Integrated ICD-10 diagnosis codes to enable automatic coding and billing Compliance with health systems’ requirements Multi-language interface Augmentation with a Google Chrome extension which helps to create notes conveniently and more. If you're looking for expert assistance in data infrastructure, data platforms, workflow engineering, and cloud development (including AWS, Azure, and Google Cloud), as well as hybrid or on-premises environments, Belitsoft healthcare software development company offers outsourced expertise to meet your needs.

Below is a list of the top companies for conversation intelligence and keyword spotting across calls. 1. Belitsoft Conversation Intelligence Belitsoft Conversation Intelligence is a speech analytics system to assess communication quality in phone calls and dialogues. It can be deployed as a cloud SaaS product or on-premises within a company's network. The system focuses on accurate speech recognition with accuracy of up to 95% on stereo recordings. It also provides in-depth analysis of each conversation, covering more than 50 parameters, including diction, speaking rate, pauses, emotional tone, script compliance, and call outcome. Each call generates a transcript. The software automatically recognizes who is speaking and then scores how well they follow the sales process using checklists. Each checklist step is scored 0 (missed it), 0.5 (did it but not well), or 1 (did it successfully). It marks important things that happen during the call. For example, when a customer objects or mentions a competitor. It also looks for signals that indicate buying interest. You can customize checklists to match your sales process. The system connects to your existing CRM and phone system. It shows dashboards for managers, sales people, and trainers to see how their team is doing. The software can also send automated coaching advice to agents through messaging apps such as Telegram. Companies can start using Belitsoft Conversation Intelligence with AI services like ASR and language models (GPT, Gemini) from outside. Later, they can switch to comparable open-source models hosted on their own on-premise GPU infrastructure. This keeps the recognition and analytics work exactly how they want it. It also makes sure that regulations and internal security standards are followed. Deployment and data control Belitsoft Conversation Intelligence is available for on premise deployment. This is important for customers that prioritize data security and strict compliance with local regulations. Global market leaders can also be deployed on-premise, but their projects are complex and expensive, with long implementation timelines and enterprise level licenses. Belitsoft Conversation Intelligence is ideal for faster on-premise rollout with shorter deployment times and individual pricing that is acceptable for mid-sized companies. It fits organizations where a pure cloud setup is not allowed or where a hybrid model is required, with some services kept locally and others in the cloud. This is relevant for banking and government in countries where the use of foreign cloud providers is restricted. Price and scale Belitsoft Conversation Intelligence costs less than many bigger competitors. Prices start around $420 a month for 10,000 minutes. That makes conversation analytics affordable for most mid-sized companies. The focus segment is mid-sized and growing companies, where Belitsoft Conversation Intelligence can compete by delivering about 80 to 90% of the functionality of market leaders at a lower cost. A regional bank or a network of clinics with a call center of about fifty agents can cover its main needs such as transcription, script based search, emotion detection and quality scoring with the standard product and pay significantly less than with the largest providers in this market. Functionality and focus Usually, businesses assume they must buy very expensive software from industry giants to get deep insights. Belitsoft Conversation Intelligence is designed to match top analytics systems. Despite being a smaller or more custom provider, its technical capabilities (AI analysis of phone calls) are equal to those of the famous market leaders. It tracks more than 50 indicators that cover speech technique (Is the agent talking too much and not letting the customer speak? Are there awkward pauses suggesting the agent doesn't know the answer? Is the agent cutting the customer off?), emotions (Is the customer frustrated, angry, or happy? Does the agent sound supportive? Identifying when a conversation turns hostile), script adherence (Did they say the mandatory legal disclaimer? Did they ask for the sale? Did they handle objections correctly? Did they use the standard brand greeting?) and the call outcome (Was a sale made? Was a follow-up booked? Was the technical issue solved?), which is comparable to what large platforms monitor. One of the differences is the focus on the mechanics of speech. It detects things like filler words (um, ah, like), how agents fill awkward silences, and whether their grammar/language is accurate. This is specifically designed for training and coaching. It helps managers correct bad habits in individual agents to make them sound more professional. Global solutions focus more on sentiment analysis, net promoter score, churn drivers and related metrics to improve customer experience. However, both groups of products address similar areas such as service quality, customer satisfaction and compliance. Belitsoft Conversation Intelligence can stand out through flexible configuration for a specific business. Large products also support configuration, but this often needs vendor or integrator teams. Moreover, if you ask a giant software company to build a brand new feature just for your specific company, they will likely say no. Their product is standardized for thousands of users. As an engineering focused company, Belitsoft can offer a more personalized setup. If you need a metric that doesn't exist yet, they will write the code to create it for you. For example, if you are a niche medical supply company, your own implementation of Belitsoft Conversation Intelligence will know your specific medical jargon. Integrations and implementation Check out the case study on implementing Belitsoft Conversation Intelligence for a client in the automotive industry. Large platforms usually offer analytics as an element inside a suite that covers call recording, routing, workforce management and other functions. Belitsoft Conversation Intelligence is a specialized analytics service. It connects to existing telephony or customer relationship management systems through API. For many customers this independence is an advantage. They can keep their current telephony stack and add Belitsoft Conversation Intelligence on top as an analytics module without changing the rest of the system. Competing vendors often sell a full package that includes telephony, virtual agents and analytics together. Not every company wants or is able to replace all components at once. Belitsoft Conversation Intelligence is attractive for customers that already have a stable call infrastructure but lack analytics. They can buy only the analytics module. This is relevant for outsourced call centers that operate many projects and do not want to redesign their platform around a single vendor. They need a flexible call analytics tool that works with different systems. Belitsoft Conversation Intelligence is most beneficial for mid-sized and upper-mid-sized customers for whom leader products are either too complex or too expensive. Typical examples are regional banks and insurance companies, networks of medical clinics, retailers with call centers from 50 to 200 agents, online businesses that sell actively by phone and public sector entities that must store data locally. These customers value fast payback and configuration flexibility. Belitsoft Conversation Intelligence can be deployed for them within a few weeks, while large corporate solutions are usually implemented over several months. 2. CallMiner (Eureka) CallMiner Eureka is not for small businesses. It is designed for large enterprises and large-scale quality control. The software is built to process millions of hours of calls and terabytes of text from giant contact centers of banks, hospitals, or telecom giants (AT&T, Verizon, UnitedHealthcare). CallMiner analyzes every way a customer interacts with a company. A customer can complain on X/Twitter, then email support, and finally call the hotline. It ingests audio (calls), text (email/chat), and social media. This gives the company a unified view of the customer journey. The AI goes beyond just transcribing words and looks for intent (why are they calling?), emotion/tone (how are they feeling?), and signs of fraud (theft patterns or suspicious behavior). The system automatically deletes credit card numbers or social security numbers from the recording so the company does not violate privacy laws (GDPR, PCI, HIPAA). It does not just analyze the call after it is done but assists agents during calls. If a customer says "I want to close my account", the software can display a retention offer on the agent's screen. CallMiner analyzes interactions to help you understand why performance has dropped (for example, thousands of customers complained about the new billing interface update). 3. NICE (Enlighten AI, Nexidia) NICE Enlighten AI, previously Nexidia, is a large-scale contact center platform with a comprehensive set of interaction analytics tools. It extracts insights from all communication channels and provides a view of customer satisfaction, complaints, sales effectiveness and other key performance indicators. The company is known for phonetic indexing and strong artificial intelligence that combine real time and historical analytics for high-volume tasks that require high accuracy and performance. Agentic AI features allow the system to make decisions during a call. It can check script and policy compliance in the moment or automatically redirect a call when customer sentiment drops. The platform scales to very large datasets, supports many languages and has a version hosted inside the European Union to support General Data Protection Regulation requirements. Typical customers are large enterprise contact centers in banking, airlines, retail and other sectors that need speech analytics together with a full workforce optimization and workforce engagement management stack that covers recording, scheduling and training with integrated analytics. These companies expect deep reporting configuration and strong guarantees of regulatory compliance. 4. Verint Speech Analytics Verint is historically known for call recording and workforce engagement systems. It has been the industry standard for recording calls and managing employee schedules (Workforce Management/WFM) for decades. Companies that already use Verint to schedule their 5,000 agents find it easy to simply switch on the analytics module rather than buy a new tool from a different vendor. The system acts like an automated auditor. If a debt collector threatens a customer (illegal) or a banker promises a specific return on investment (illegal), Verint flags it immediately. For banks and insurance companies, a bad customer experience is annoying, but a regulatory violation can cost millions in fines. Verint prioritizes preventing fines. Verint offers an all-in-one suite. Analytics module detects that Agent Steve is struggling with closing sales. WFM module automatically schedules a training session for Steve during his next low-volume shift. Most competitors focus only on analytics, whereas Verint connects analytics directly to HR and scheduling tools. Financial services, insurance, healthcare, and government are industries that are terrified of data leaks and instability. Because Verint has a long track record and specializes in data security, IT directors at major banks trust it. They view it as a low-risk purchase because the company is a large, publicly traded entity with a reputation for reliability. 5. Observe.AI Observe.AI is a relatively new vendor founded in 2017 that focuses on AI support for quality control in contact centers. It's a platform for automating quality assurance and agent coaching. The system automatically transcribes calls, scores them against predefined criteria and helps scale quality checks that were previously done manually. Its strengths include sentiment analysis, keyword and phrase search, intelligent scoring that discover the most important deviations and built-in workflows for reviewing mistakes and training agents. Observe.AI integrates with popular CRM and telephony systems, which simplifies deployment, and is often chosen by contact centers that want to improve traditional call listening and training. Typical customers are mid-sized and large commercial contact centers in e-commerce, technical support and retail that want to improve service quality and agent productivity with a cloud-based AI product. These companies value fast software as a service rollout and ready-made metrics for call evaluation. 6. Balto Balto is a virtual prompter. As soon as the customer complains about the price, Balto's AI hears it and immediately pops up a script on the agent's screen with the correct discount offer. It fixes the mistake while the call is still happening, potentially saving the sale. If other solutions detect compliance violations after they happen (so you can punish the agent), Balto tries to prevent them. It tracks a checklist and if the agent is about to hang up but hasn't read the mandatory legal rights script, Balto will flash an alert before the call ends. Balto is usually an overlay. You don't replace your current phone system (like RingCentral, Five9, or Zoom) but simply plug Balto into it. Typical customers are companies that need to improve conversations while they are happening. This includes mid-sized sales and contact centers where every call has high value, such as outbound sales teams, debt collection departments and service focused call centers with strong conversion targets. Balto is often chosen by teams that want to quickly raise the performance of new or weaker agents through on screen guidance and reduce errors during the call. 7. Gong.io Gong focuses on B2B sales and client meetings. The platform automatically records sales calls and video conferences, transcribes them, and analyzes how each deal is progressing. Gong detects mentions of competitors and customer objections, compares how top performers speak versus weaker performers, and estimates the probability of closing a deal based on the content of the conversations. It gives the sales team visibility into negotiations, such as which topics come up, where customer interest drops, and whether the sales method is followed. Key features include insight discovery to grow revenue, deal forecasting based on conversations, and speech pattern analysis such as the talk time ratio between manager and client and the use of required phrases. Typical customers are commercial teams in technology, software as a service, and financial services, from startups with small sales teams to large corporations that want to scale their best sales practices. Gong has become a standard for companies that manage sales quality using data from conversations. In this area, Belitsoft Conversation Intelligence competes with Gong in the analysis of sales communications. 8. Genesys Cloud CX (Speech & Text Analytics) Genesys Speech Analytics is the native analytics module from Genesys, a major contact center vendor, designed mainly for users of the Genesys Cloud platform. Genesys provides the core call center infrastructure such as automatic call distribution and interactive voice response. Within this stack, Speech Analytics lets customers automatically analyze all calls and written interactions, perform speech recognition, run topic analysis on customer requests, assess sentiment and measure agent empathy. Because it is built into Genesys Cloud, integration is seamless and analytics data is immediately available for supervisors inside the same interface. The module tracks the share of talk time between customer and agent, key topics in the dialogue and script adherence. A recent addition is Agent Empathy Analysis, which scores empathy based on vocabulary and tone. Genesys also offers real time functions such as Agent Assist, also called Copilot, that suggests responses and knowledge during the call and alerts supervisors to issues such as dropping customer satisfaction. Typical customers are large contact centers that already run on Genesys Cloud and prefer an integrated solution from one vendor. These are usually companies with tens or hundreds of agents, such as major retailers, telecommunications providers and banks that use Genesys for routing and extend their setup with the built-in analytics module. 9. Uniphore Uniphore offers a conversational AI platform for enterprises using generative AI and multimodal analysis to extract insights from conversations. Their Conversation Insights Agent analyzes all interactions and provides answers to business questions such as "What are the top three reasons for customer dissatisfaction this week?". Uniphore has a proprietary AI infrastructure. Its Business AI Cloud includes in-house LLMs and Emotion AI engines, avoiding reliance on external services. The platform tracks emotions and tone during conversations, giving real-time feedback (for example, suggesting that an agent show empathy when detecting customer irritation). Additional modules include U-Assist for real-time guidance and post-call automation (such as ticket creation and conversation summaries) and U-Analyze for reviewing all recordings with quality, compliance, and CX scoring. Typical clients are large enterprises and global corporations ready to adopt AI technologies to enhance customer service. Uniphore is used in banking, telecommunications, and aviation, where companies look to apply technologies such as voice biometrics, facial expression analysis (computer vision) during video calls, and automatic CRM population based on conversation data. 10. Cresta Cresta is a conversation intelligence platform that combines AI-based agent coaching with process automation. It not only analyzes conversations. The Cresta Omnichannel AI Agent module provides a report on the performance of both human and virtual agents. Cresta focuses on low latency and fast guidance. It uses ultra fast transcription to show prompts during conversations and can be configured for specific business needs without programming skills. Its models are trained on the company data with full transparency for the customer. The goal is to move from simple reporting on what was good or bad in a call to direct intervention during the interaction and improvement of the outcome with artificial intelligence. Typical customers are large technology driven companies and contact centers that want joint work between AI and human staff. Cresta is often deployed where there is already a high volume of interactions and established processes and the management team wants to grow revenue and service quality through AI-based coaching, for example in large e-commerce businesses, software as a service vendors with big phone sales teams and premium customer service operations.