Conversational AI & Voice AI Development
Most conversational AI development fails the moment users go off-script. We build custom AI assistants and production-ready voice AI systems that are RAG-grounded, confidence-gated, and scoped to your product - no model memory, no hallucinated answers, from powered by ElevenLabs for real-time speed and accuracy.
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Delivery Timeline
Latency budget, data access, and conversation scope defined
Day 1
Working voice or chat system in staging
Week 1
Integrations, fallback paths, observability, and guardrails
Week 2-3
Production-ready deployment path with monitoring and audit logs
Week 4
4-6 weeks
Average time to MVP
Enterprise-grade security, compliance, and data control
Our Partners in Building What's Next
Three official partner credentials. Each one shows up in production code, not on a slide.
Voice AI agents in production for HIPAA, retail and provider workflows. Direct line to ElevenLabs roadmap and beta features.
Cloud architecture for AI workloads. Vertex, Gemini, BigQuery, Vector Search wired into client systems from day one.
Headless CMS integrations that ship 3× faster than traditional builds. Real-time content workflows for editors and engineers alike.
Delivering Impact
Code AI-Generated, Human-Audited
What Production-Ready Voice AI Actually Means?
Most voice AI demos work well in controlled environments. The real challenges appear after launch when real users, interruptions, and system load come into play. That’s where thoughtful architecture and guardrails start to matter.
At SoluteLabs, we design real time-streaming voice AI systems to run reliably in production from the beginning, not just in demos.
Most Teams Build
What Breaks in Production
What SoluteLabs Ships?
01.Single model voice pipeline
2-4s latency under load
Sub- 1.5s end-to-end pipeline
02.Generic LLM responses
Hallucinated answers on sensitive queries
Confidence gating + controlled tool execution
03.No interruption logic
Users interrupt, conversation breaks
Natural turn-taking + interruption handling
04.Voice added late
Compliance issues after launch
HIPAA, audit logs, and PHI controls from sprint one
05.No observability
Costs rise, quality drifts
Full tracing, drift detection, and latency monitoring
How We Build Voice AI That Does Not Fall Apart After Launch
At SoluteLabs, we design voice AI with real-world production in mind right from the start.
Latency spikes
We build our speech recognition, model inference, & voice syn-thesis into one streamlined pipeline. Each layer gets a specific time budget, making sure the entire interaction wraps up in under 1.5 seconds.
Voice Synthesis ElevenLabs
As an official ElevenLabs partner, we rely on production-grade voice synthesis that’s fast and clear. For us, the voice layer isn’t just a bolt-on; it’s central to the whole system.
Confidence Gating
Every response runs through a confidence scoring layer before it reaches the user. Below the threshold, the system flags un-certainty rather than guessing - on regulated queries, it routes to a human agent instead of generating from model memory. Every interaction, confidence score, and routing decision is logged.
Structured AI Specifications
We hardwire clinical terminology, compliance needs, and escalation rules directly into each system. That keeps conversations consistent and on point from start to finish.
Chatbots vs Voice AI
Chatbots tolerate delay. Voice AI does not. The architecture is different from the ground up.
CHATBOT-STYLE PIPELINE
Applied to voice
fails in prodVOICE-FIRST ARCHITECTURE
What SoluteLabs ships
production readyLatency
2–4s under real load
Latency
Sub-1.5s · STT 200ms · LLM 900ms · TTS 300ms
Turn-taking
Conversation breaks on interrupt
Turn-taking
Interruption handled · redirect logic active
User context
Cold start every session
User context
Account history loaded at session start
Compliance
Retrofitted after launch
Compliance
HIPAA controls defined in sprint one
Monitoring
Users report issues first
Monitoring
Drift detected before it reaches users
Conversational AI & Voice AI Services
Most conversational AI breaks when users go off-script. Most voice AI breaks when it hits production load. We build both: grounded in your product data, designed for edge cases, and integrated into the workflows your users already rely on.
AI assistants built around your product context, documentation, permissions, terminology, and user workflows. Every response is grounded in your actual data, not model memory.
RAG-Grounded Responses
RAG chatbot development using your documentation, product data, and internal knowledge sources
Confidence scoring, source citation, and fallback handling built into the response flow
No hallucinated answers presented to users as fact
Product and User Context
Custom AI assistants built around your product flows, terminology, and support logic
User permission scoping so the assistant only answers based on what the user is allowed to access
Session context, account context, and product usage history loaded where needed
Enterprise Conversational AI
Secure conversational AI architecture with SSO, RBAC, and identity-provider integration
Multi-tenant AI platform design with isolated conversation data and retrieval indexes
Audit logging for every query, response, user identity, and resolution outcome
Multilingual Conversational AI
Multilingual chatbot development with language detection and routing
Locale-aware retrieval pipelines indexed per language
Per-language quality monitoring and drift detection for global conversational AI systems
SaaS AI assistants, enterprise knowledge bots, customer support automation, multilingual AI assistants, and secure conversational AI inside existing products.
Voice AI systems built across STT, LLM inference, tool execution, and TTS as one latency-budgeted pipeline. Sub-1.5s response time is treated as an architectural constraint, not a post-launch optimization goal.
Voice Pipeline Architecture
Real-time voice AI development across STT, LLM or tool call, and TTS
Defined latency budget for each layer of the voice pipeline
Sub-1.5s end-to-end response target designed into the first architecture review
ElevenLabs Voice Synthesis
ElevenLabs-powered voice synthesis for production-grade voice agents
Voice layer designed as part of the core system, not bolted onto a chatbot
Voice persona, pacing, & response length calibrated per use case
Interruption and Turn-Taking
Turn-taking architecture for natural speech flow
Mid-sentence interruption handling, pause behavior, and redirect logic
Silence handling across short pauses, extended pauses, and fallback routing
User Context Injection
Account history, preferences, support context, and product data loaded at session start
Repeat users are not treated like first-time callers
Channel handoff to chat, SMS, or human agent with conversation context preserved
SaaS AI assistants, enterprise knowledge bots, customer support automation, multilingual AI assistants, and secure conversational AI inside existing products.
Voice systems that work in controlled testing often degrade under real workloads. Latency climbs, accuracy drifts, and inference costs scale faster than usage. We profile the full pipeline and fix the layers creating production risk.
Latency Optimization
Full latency audit across STT, LLM inference, tool execution, and TTS
Layer-by-layer profiling to identify where response time is lost
Re-architecture of the bottleneck instead of surface-level tuning
Model Routing and Cost Control
LLM cost reduction through model routing by task complexity
Frontier models used only when reasoning depth requires them
Simpler turns routed to faster, cheaper models automatically
STT and Domain Accuracy
STT fine-tuning and terminology handling for domain-specific vocabulary
Persistent domain instruction layer for terminology, escalation rules, and compliance constraints
Behavior survives model updates and team changes because instructions are versioned
Production Monitoring
Voice system scaling with latency, quality, fallback, and cost monitoring
Drift detection when accuracy degrades under real usage
Real-time sentiment classification per utterance, with escalation thresholds defined to route distressed interactions to a human agent before the caller disengages
Live voice systems with inconsistent latency, rising inference costs, accuracy drift, poor call completion, or high abandonment.
In regulated industries, a wrong answer isn't a UX problem - it's a liability. Every query, response, and routing decision needs to be traceable before the first user conversation happens.
Healthcare and Regulated AI Architecture
HIPAA-compliant voice agent architecture with PHI controls and PII redaction
Encrypted storage, data residency requirements, and access controls defined in the architecture spec
Healthcare voice AI and clinical voice assistant workflows built with compliance from sprint one
Confidence Gating
Clinical, financial, and legal queries routed through tool-only execution where required
Model memory is not used as a source for high-stakes regulated responses
Low-confidence responses routed to fallback, clarification, or human review
Audit and Traceability
Every interaction logged with user identity, intent classification, confidence score, response, and outcome
Regulatory documentation package with architecture diagrams, data flow docs, and security controls
Full interaction audit trail for security, compliance, and operational review
Industry-Specific Domain Skills
HealthTech: HIPAA, FHIR, clinical routing, medication and patient data access
FinTech: regulatory disclosure, fraud escalation, risk workflows
Legal: privilege boundaries, jurisdiction-aware handling
Healthcare voice AI, HIPAA-compliant voice agents, FinTech voice AI, clinical assistants, regulated industry AI, and enterprise systems where every interaction must be traceable.
