NeoTek Solutions designs and builds conversational AI and contact center architecture for teams that answer the same questions all day. Conversational AI lets people talk or type with an AI assistant to get answers and complete tasks. The architecture combines chat and voice channels, speech services, a large language model (LLM) and your business systems. In a contact center, it also hands conversations to live agents and helps them while they work, and customers can always reach a person.


When to Use This Architecture

This architecture fits organizations that handle high volumes of repeat questions and routine requests. Examples include appointment changes, order status, benefits questions, password resets and billing inquiries. It also fits contact centers that want to shorten after-call work and help new agents find answers faster.

A simpler option is sometimes better:

  • If most questions have short, fixed answers, a searchable help center may be enough.
  • If you only need call routing, standard menu options in your phone system may do the job.
  • If callers mostly need a human for complex, emotional or high-risk issues, start with agent-assist rather than self-service.

Core Components

Channels

We let your customers reach the assistant through web chat, mobile apps, SMS, messaging apps or phone. We build a channel layer that normalizes each message into a common format. The same logic then serves every channel, so you maintain one assistant rather than several.

Speech-to-Text and Text-to-Speech

Speech-to-text converts a caller’s voice into text the system can process. Text-to-speech turns the assistant’s reply into natural-sounding audio. Both must handle accents, background noise and domain terms like drug names or product codes.

Conversation Orchestrator

We build the orchestrator that manages the dialogue, tracks context and decides what happens next. It combines an LLM for free-form requests with defined flows for tasks that must follow exact steps. We keep identity verification and payments in those fixed flows, never in the model.

Intent Handling With an LLM and RAG

An intent is what the user wants to do, such as reschedule an appointment. The LLM identifies the intent and extracts details like dates and account numbers. For questions, retrieval-augmented generation looks up approved knowledge articles so answers stay accurate.

CRM and Ticketing Integration

We integrate the assistant with your CRM, the system that stores customer records and interaction history. Through secure APIs it looks up accounts, creates cases and updates tickets. For complex, multi-step tasks we hand off to an AI agent with approvals.

Live-Agent Handoff

When the user asks for a person, or the assistant is unsure, the conversation transfers to a live agent. The handoff passes a summary, verified identity and collected details. The customer does not have to repeat themselves.

Agent-Assist

We build agent-assist that supports your human agents during live conversations. It transcribes the call, suggests knowledge articles, drafts replies and summarizes the interaction afterward. Your agent stays in control and decides what to use.

Analytics

Conversation analytics groups contacts by topic, outcome and sentiment. Leaders see why customers call, where the assistant struggles and which knowledge articles need work. Transcripts feed evaluation and improvement.

Compliance Recording and Consent

We build recording and transcription to follow the consent rules for each channel and location. The system plays or displays your disclosures, records consent and applies your retention policies. We pause or redact sensitive data, such as card numbers, from recordings and transcripts.


How It Works

Conversational AI architectureA customer’s chat or call arrives through the channel layer, which gives disclosures, captures consent and uses speech services for voice. The orchestrator verifies identity, the LLM with RAG identifies intent and answers from approved knowledge or calls CRM and ticketing APIs, and the response is delivered. Complex requests transfer to a live agent with a summary and agent-assist, and transcripts and outcomes flow to analytics.CustomerChat or callDisclosuresand consentAI + recording noticesChannelsChat, messaging, voiceSpeech-to-textText-to-speechOrchestratorFlow + identity checkLLM + RAGIntent, detailsand answersKnowledgeApproved contentCRM andticketingVia APIsAgent-assistSuggestions,wrap-up notesLive agentTakes over162345587Transfer with summaryANALYTICS AND COMPLIANCERecordingsTranscripts, per consentAnalytics andevaluationOutcomes + feedback9Logging and analyticsCompliance or human agent
  1. A customer starts a chat or places a call, and the channel layer receives it.
  2. The system gives any required AI and recording disclosures and captures consent.
  3. For voice, speech-to-text converts the caller’s words into text.
  4. The orchestrator verifies identity through a defined flow when account access is needed.
  5. The LLM identifies the intent and extracts key details.
  6. The assistant answers from approved knowledge or calls CRM and ticketing APIs to complete the task.
  7. Text-to-speech or the chat window delivers the response.
  8. If the request is complex or the user asks for a person, the conversation transfers with a summary.
  9. Agent-assist helps the live agent, then drafts the wrap-up notes.
  10. Transcripts, outcomes and feedback flow to analytics and evaluation.

Security, Governance and Guardrails

Access control. The assistant can only reach account data after identity verification. Service accounts for integrations have scoped, least-privilege permissions.

Data protection. Audio, transcripts and logs are encrypted in transit and at rest. Sensitive values are redacted before storage and before reaching the model where possible. We design for HIPAA requirements in healthcare settings and support business associate agreements with providers where required.

Guardrails. The assistant stays on approved topics and uses approved knowledge. It does not give medical, legal or financial advice beyond published guidance. An LLM gateway can add content filtering and audit logging.

Evaluation. Before launch, we test with real conversation samples, including difficult and off-topic requests. Every change to prompts, flows or knowledge reruns those tests.

Monitoring and human oversight. Supervisors review escalations, low-confidence conversations and customer feedback. Customers can always reach a person.


Reference Stack by Cloud

Component Microsoft Azure AWS Google Cloud
Voice and contact center Azure Communication Services Amazon Connect Contact Center AI
Speech-to-text and text-to-speech Azure AI Speech Amazon Transcribe and Amazon Polly Speech-to-Text and Text-to-Speech
Bot and dialogue flows Azure Bot Service Amazon Lex Dialogflow CX
LLM Azure OpenAI Amazon Bedrock Vertex AI
Knowledge search Azure AI Search Amazon OpenSearch Service Vertex AI Search
Integration Azure API Management and Azure Functions Amazon API Gateway and AWS Lambda Apigee and Cloud Run
Recording storage Azure Blob Storage Amazon S3 Cloud Storage

Open-source and on-premises options also fit, such as open-source speech models, existing contact center platforms and Kubernetes. NeoTek Solutions is vendor-neutral and integrates with the phone, CRM and ticketing systems you already run.


Common Pitfalls

  • Automating complex or sensitive conversations before simple ones are working.
  • Making it hard for customers to reach a person.
  • Letting the LLM handle identity verification or payments without defined flows.
  • Handing off to agents without passing context.
  • Answering from unreviewed content instead of approved knowledge.
  • Recording or transcribing without the required disclosures and consent.
  • Ignoring analytics that show where the assistant fails.

Where We Apply It

In healthcare, assistants help patients with scheduling, directions, billing questions and pre-visit instructions, with clinical questions routed to staff. In financial services and insurance, they support claim status, policy questions and agent-assist for service teams. In retail, they handle order tracking, returns and product questions. In government and public services, they answer common resident questions and route service requests.

Example scenario: A patient access center fields many calls about rescheduling and directions. A voice assistant handles routine requests and transfers others with a summary, while agent-assist drafts call notes.


How Can NeoTek Solutions Help You Build an Assistant?

Most teams already know which contacts fill the queue. The work is automating them without frustrating customers.

  • What we buildChat and voice assistants with defined flows for identity and payments, answers from approved knowledge, live-agent handoff and agent-assist for your team.
  • Built on your systemsWe integrate your phone platform, CRM, ticketing and approved knowledge sources, so the assistant reads and updates the records agents already use.
  • How we workShort cycles with AI-assisted delivery and human review, an evaluation set of real conversations agreed early and a pilot queue you can try.
  • Skills on the teamAI and machine learning engineers, data engineers, cloud and security specialists and QA in one team, covering speech, retrieval, integration and testing.
  • What makes us differentOne team covers strategy, build and staffing, so we can hand the assistant to your contact center team or stay on alongside them.
  • Privacy and a route to a personWe handle disclosures, consent and redaction of sensitive values, design for HIPAA requirements where needed and keep a clear route to a human.

Tell us what your agents spend their day on. Book a free AI consultation and we will suggest a first use case and a realistic path to going live.


Frequently Asked Questions

What do you build for our contact center?

We build chat and voice assistants that answer your customers’ questions and complete routine tasks. We connect them to your phone platform, CRM, ticketing and approved knowledge, then add live-agent handoff and agent-assist for your team.

How is what you build different from the chatbot we already have?

Scripted chatbots follow fixed menus and keyword rules. We build assistants that understand free-form language and answer from your approved knowledge using retrieval. We keep fixed flows for sensitive steps such as identity checks, so both approaches do what they are good at.

What does the agent-assist you build do?

It supports your human agents while they are on a live contact. It transcribes calls, suggests answers from your knowledge articles and drafts summaries afterward. Your agent decides what to say and what to save.

How do you hand a conversation to a live agent?

When a customer asks for a person, or the assistant is unsure, we transfer the conversation to your live agent. We pass along a summary, the verified identity and the details already collected. Your customer does not have to repeat anything, and we always keep a route to a person.

Can you build this for a healthcare setting?

Yes, with the right design. We usually have the assistant handle scheduling, billing and general information while routing clinical questions to your staff. We design for HIPAA requirements and work within your compliance program, though we claim no certification.


Plan a Better Customer Conversation

Tell us which requests fill your queues and which systems your agents use. We will design an assistant and agent-assist approach that fits. Learn more about our generative AI solutions or talk to an AI architect.

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