Blog archive
423 Dasha articles about voice AI, conversational AI, and building production-ready agents.
2026
- Multilingual AI sales agents: A market-by-market deployment guide
Multilingual AI sales agents depend on speech, offers, knowledge, tools, compliance, and measurement sharing the same market context. The practical model is a shared runtime and business-logic layer with a versioned locale package, release gate, fallback path, and scorecard for each market.
- Voice AI vs Human Agents: How to Divide the Work
Comparing voice AI with human agents is a workload design decision. The useful question is which calls software can complete safely, which calls need human judgment, and how the two paths reconnect. That choice affects customer outcomes, staffing, cost, and the failure modes your team must manage.
- AI for Electricians: Tools, Use Cases, and Safe Workflows
AI for electricians covers more than phone answering. Electrical contractors can use it around scheduling, dispatch, estimating, plan and document review, job records, customer communication, and equipment monitoring. The right choice depends on company size, existing software, and whether the…
- How to Build Customer Trust in Voice AI
Voice AI can answer quickly and complete real work, yet one misleading introduction or incorrect account change can turn convenience into suspicion. Trust depends on the whole call: identity, conversation quality, task accuracy, user control, data handling, and recovery when something fails. For…
- AI in Clinical Trials: Evidence and a Safe Rollout Plan
AI can reduce manual work across protocol planning, recruitment, study conduct, and data review. In a clinical trial, speed has little value if it weakens participant protection or evidence reliability. The useful question is specific: which task should AI perform, how much influence should it…
- AI in Healthcare Communication: Where It Works and How to Deploy It Safely
AI can make healthcare communication faster, clearer, and available beyond office hours. It can also deliver the wrong instruction at exactly the wrong moment. A safe rollout starts with a bounded use case, controls matched to its risk, and a clear owner for every action and handoff.
- AI for Investigations: A Defensible Evidence Workflow
AI can shorten the slowest parts of an investigation: reviewing files, finding relevant moments in recordings, connecting names and events, and collecting structured information. It can also introduce a false fact into a case with confident wording. The difference comes down to workflow design.…
- AI for accounting firms: 7 controlled use cases
AI can help an accounting firm handle narrow, repeatable work. It can also produce a confident error, expose client information, or change a record without adequate authority. A useful program starts with a task boundary, a controlled data path, and a named reviewer. That keeps the technology in a…
- AI Voice Agents for Law Firms: A Safe Buying Guide
AI voice agents can help law firms answer and route calls, collect limited intake facts, schedule consultations, relay authorized status information, and transfer callers. Their safe role ends before legal advice, conflict decisions, deadline analysis, case evaluation, or other professional…
- Thoughtly vs Synthflow: A Practical 2026 Comparison
Thoughtly and Synthflow can both launch AI phone agents through a visual builder. The harder question is what happens after the first working call. Your choice affects telephony, CRM ownership, release testing, client management, and how much engineering control remains available at scale.
- Vocode vs Air AI in 2026: Current status and verdict
Vocode and Air AI used to represent opposite voice AI paths. Vocode exposed open-source building blocks to developers. Air AI sold a hosted calling product and related agency offers. That old comparison now hides the decision that matters: whether either option is viable for a new deployment. Here…
- AI in Ophthalmology: Clinical Evidence and a Safe Rollout Plan
Eye care produces structured images, repeated measurements, and time-sensitive referral decisions, which makes ophthalmology a strong field for narrowly defined AI. Strong study results can still fail when the camera, patient population, workflow, or handoff changes. A safe rollout starts by…
- 8 best Bolna AI alternatives for production voice AI
Bolna combines an India-focused voice platform with an open-source orchestration framework. Replacing it starts with deciding which part needs to change. A lower minute rate will not help if the new system loses language coverage, carrier control, testing, or the operating evidence your team needs.…
- Voice AI Cost-Benefit Analysis: A Practical ROI Model
A low per-minute price can still produce a poor voice AI investment. The financial result depends on which calls the agent can handle, how often it completes the intended task, how much human work remains, and whether the released capacity has economic value. A credible cost-benefit analysis…
- Six Voice AI Implementation Examples for Sales Teams
Voice AI case studies often headline an outcome while omitting the machinery that produced it. Implementation teams need the call trigger, data contract, conversation boundary, tool actions, escalation path, and measurement plan. We build Dasha for that production layer. These implementation…
- 8 Best Vapi Alternatives for Production Voice AI in 2026
The best Vapi alternative depends on which part of Vapi you want to replace.
- Voice activity detection: From audio frames to natural turns
Voice activity detection looks simple: label each audio frame as speech or non-speech. In a live voice agent, that decision controls whether a caller's first syllable survives, when the agent responds, and whether background noise triggers an interruption. Understanding the path from raw frames to…
- AI Agent Security: A Practical Architecture Guide
An AI agent can turn one bad model response into an account change, message, booking, or API call. That makes agent security an architecture problem for any team giving models tools, memory, or access to business data. We build and run production voice AI agents at Dasha, where a spoken turn can…
- OpenAI Realtime API: A practical guide for voice agents
The OpenAI Realtime API puts live audio, model reasoning, and spoken responses in one stateful session. That can shorten the voice path, but a working demo still leaves important architecture decisions to your team. Here is how the current API works, which transport to use, what it costs, and what…
- Bring your own carrier (BYOC): architecture, tradeoffs, and rollout
Moving a phone operation to the cloud often exposes a hidden dependency: the software platform also wants to become the telecom provider. Bring Your Own Carrier (BYOC) separates those decisions. That separation can preserve numbers, contracts, routing, and regional coverage, but it also leaves your…
- SIP Trunking Explained: Architecture, Setup, and Production Testing
SIP trunking looks simple on a network diagram. Production failures tend to hide in the details: signaling succeeds while audio fails, a codec mismatch rejects calls, or a campaign exceeds the carrier's call-rate limit. A sound design treats routing, media, security, capacity, and recovery as…
- White-label AI voice agent platforms: 5 options and how to choose
A white-label AI voice agent can put your brand in front of clients within days. The harder question is what sits behind that brand. Your choice determines who owns the customer experience, runtime, data, billing, support, and migration risk. The right platform depends on whether you are packaging…
- The voice AI stack: a practical seven-layer model
A voice agent can sound convincing in a demo and still fail on a live phone line. The difference is usually in the stack around the model: audio transport, turn detection, state, tools, cancellation, and the evidence your team gets when something goes wrong. A useful architecture therefore starts…
- 25 missed call message templates that make the next step clear
A missed call leaves both sides guessing. The caller does not know whether anyone noticed, and the recipient does not know why they called. A clear message closes that gap. For businesses with recurring call volume, we help technical teams build inbound voice agents that answer, qualify, and route…
- Voice Agent Evaluation: Metrics, Scorecards, and Release Gates
A voice agent can say the right words and still make the wrong change. It can also complete the task while giving the caller a slow, confusing experience. Voice agent evaluation turns call evidence into scores and release gates. A sound framework keeps outcomes, speech quality, timing, safety, and…
- Speech-to-text pricing for production voice AI
Published production streaming speech-to-text prices compared here start at $0.15 per hour. OpenAI lists an estimated live-transcription cost of $0.017 per minute, equal to $1.02 per hour at that estimate. Session billing, silence, channels, diarization, language features, minimums, retries,…
- AI Telemarketing: How to Build a Production Campaign
AI telemarketing uses software to prepare, place, handle, and evaluate sales calls. A conversational voice agent can contact permitted leads, ask approved qualification questions, answer bounded product questions, schedule a next step, update a customer relationship management (CRM) system, and…
- Voice Agent Testing: An End-to-End Guide for Technical Teams
Voice agent testing verifies that an agent can complete real tasks across text, browser audio, and phone calls before and after release. A reliable plan covers business rules, multi-turn conversations, tool calls, speech, turn-taking, interruptions, failures, transfers, and rollback. This guide…
- 7 Best AI Voice APIs for Developers in 2026
There is no single best voice AI API for every product. The right choice depends on which layers you want a vendor to run. We recommend Dasha for technical teams that want a managed production runtime with API control. Vapi is strong for hosted provider flexibility, Retell AI for integrated phone…
- What Is an AI Agent Runtime? Architecture and Platform Guide
An AI agent runtime is the execution layer that turns an agent definition into a running process. It manages the model-and-tool loop, session and workflow state, permissions, interruptions, failures, streaming, and telemetry. Some products also bundle hosting, long-term memory, sandboxes,…
- Best Enterprise Voice AI Platforms in 2026: 8 Compared
The right enterprise voice AI platform depends on the operating responsibilities your team can support. Dasha offers a managed runtime with APIs, Session Initiation Protocol (SIP), provider choice, and public usage pricing. Retell AI emphasizes integrated testing and live supervision; Vapi…
- 10 best AI voice agent platforms for production teams in 2026
Choosing the best AI voice agent platform comes down to what you want to build, how much of the stack you want to control, and how much production infrastructure your team is prepared to own. A polished demo is easy. Reliable telephony, interruption handling, tool calls, monitoring, failover, and…
- AI voice agent pricing: a practical guide to total cost
AI voice agent pricing in the self-serve examples reviewed here starts with advertised base rates from $0.05 to $0.14 per minute, before exclusions and required plan fees. That is only a starting point. Some rates cover the runtime alone, others include speech or model usage, and others add a…
- 7 Bland AI Alternatives for Production Voice AI
Compare seven Bland AI alternatives by testing, monitoring, security, telephony, human handoff, pricing, and the work required to switch.
- Bland AI vs Vapi: Which Stack Should You Run in Production?
Choosing between Bland AI and Vapi is an architecture decision. Compare how each platform handles providers, call flows, pricing, latency, concurrency, and production operations, with Dasha as a third option.
- Vapi vs OpenAI Realtime API: A Technical Decision Guide
Vapi and OpenAI Realtime can appear interchangeable when a team is budgeting a production voice agent, yet they sit at different layers. The decision changes who owns telephony, media transport, model choice, tools, testing, monitoring, and incident response. Technical teams need a current…
- Vapi vs Synthflow: Control, cost, and production fit
Vapi and Synthflow can both put a voice agent on the phone, but they use different operating models. Vapi gives developers a modular runtime with provider choice and usage-based hosting. Synthflow packages a visual build system, integrations, telephony options, and enterprise rollout support. The…
- AI for Client Account Management
AI in Client Account Management revolutionizes how businesses interact with clients, offering personalized experiences, automating routine tasks, and enhancing decision-making. This technology not only boosts efficiency but also strengthens client relationships, driving growth and satisfaction.
- AI for Airlines: Customer Service Pilots That Stay Grounded
The best first use of voice AI at an airline is a narrow customer-service pilot: answer from approved sources, retrieve live passenger data only through authorized systems, and transfer whenever the request is consequential or uncertain.
- AI for Asset Management Services
AI in Asset Management Services revolutionizes the industry by enhancing decision-making, optimizing portfolio performance, and reducing operational costs. This cutting-edge technology empowers firms to stay competitive and deliver superior value to clients.
- AI for Financial Auditing
AI in financial auditing revolutionizes accuracy and efficiency, uncovering insights that traditional methods might miss. By automating routine tasks, it allows auditors to focus on complex analysis, ensuring more reliable and timely financial assessments.
- AI for B2B Marketing: Workflows, Controls, and Metrics
AI can help B2B marketing teams research accounts, prepare content, qualify opted-in prospects, and keep campaign systems current. Useful deployments give models narrow tasks, verified data, limited tools, and a clear path to a person.
- AI for B2B Sales
AI is revolutionizing B2B sales by enhancing customer interactions, automating processes, and driving growth. Its ability to analyze data and predict trends empowers businesses to make smarter decisions, ultimately boosting efficiency and revenue.
- AI for Banking Services
AI is revolutionizing banking services by enhancing customer experiences, streamlining operations, and bolstering security. Its ability to analyze vast amounts of data in real-time offers unprecedented efficiency and personalization, making it an indispensable tool for modern financial institutions.
- AI for Behavioral Insights
AI in Behavioral Insights revolutionizes how businesses understand and predict customer behavior, offering unparalleled accuracy and efficiency. By leveraging advanced algorithms, companies can make data-driven decisions that enhance customer experiences and drive growth.
- AI for Budget Analysis
AI in Budget Analysis revolutionizes financial planning by providing accurate insights, automating tedious tasks, and enhancing decision-making. This technology empowers businesses to optimize resources and drive growth efficiently.
- AI for Business Operations Management
AI revolutionizes Business Operations Management by enhancing efficiency, reducing costs, and driving innovation. Its ability to analyze vast data sets and automate routine tasks empowers businesses to make smarter decisions and stay competitive in a rapidly evolving market.
- AI for Call Centers: Architecture, Handoffs, and QA
AI can handle a defined call-center transaction, assist an employee during a call, or structure completed-call data. The hard part is connecting the model to reliable systems, containing its authority, and transferring exceptions without losing context.
- AI for Oncology Research
AI is revolutionizing oncology research by accelerating discoveries, enhancing diagnostic accuracy, and personalizing treatment plans. This transformative technology promises to improve patient outcomes and streamline the research process, making it an indispensable tool in the fight against cancer.
- AI for Candidate Screening: A Human-Governed Design
AI can support candidate communication and collect job-relevant information, but it should not decide who advances. A defensible design keeps recruiters accountable, validates every criterion, accommodates candidates, and provides an audit and appeal path.