What is the difference between a conversational AI assistant and an AI agent?

Assistants answer, retrieve and route inside a scripted or intent-driven flow. Agents reason, use tools and complete transactions in your systems of record with authorization, evals and audit. Most enterprises need both — assistants for scoped conversations, agents for end-to-end resolution.

How do you pick between Kore.ai, Dialogflow CX, Watsonx and Agentforce?

By fit — CRM alignment, contact-center estate, model portability, governance, vertical accelerators and total cost. We run a platform-selection sprint that scores each against your top intents, data sources and target channels before you commit.

Can we migrate off a legacy IVR without disrupting live traffic?

Yes. We run parallel deployment with regression parity gates and canary cutover per queue and language, so nothing breaks and CSAT and containment are measured against the legacy baseline.

What does a typical engagement cost and how long does it take?

Time-and-materials or fixed-fee per phase. A first production assistant on a chosen platform typically ships in 6–10 weeks; voice deployments and multi-language rollouts run 10–16 weeks. Managed operations are a monthly subscription tied to volume and channels.

How long does it take to reach a first production release?

A scoped first release typically reaches production in 6 to 10 weeks: discovery and platform selection, then a pilot on one intent or channel before a governed rollout. Voice deployments and multi-language programs run 10 to 16 weeks because of the additional telephony and localization testing involved.

How is a conversational AI engagement priced?

Assessments and platform-selection sprints are fixed-fee, implementation is priced by milestone against agreed intents, channels and integrations, and managed operations run as a monthly tier. Cost is driven mainly by the number of channels, the depth of CRM/CCaaS integration and whether voice is in scope.

How does conversational AI differ from your AI business automation service, or building it in-house?

Conversational AI covers voice, chat and email interactions with your customers and employees, while AI business automation focuses on back-office document and case processing — the two are often paired but scoped separately. Building in-house is possible, but most teams lack a production evaluation practice and platform-specific implementation experience, which is where engagements stall.

What does managed support for conversational AI include, and what hours does it cover?

Managed support covers intent tuning, evaluation-gate monitoring, drift review, incident response and release management under a documented SLA. Standard coverage is business hours in your time zone, with 24x7 follow-the-sun support available for production contact-center deployments.

What do conversational AI specialists cost, and where is delivery based?

Conversational AI engineers and platform architects are billed at a monthly per-person rate with a standard notice period built into the staffing agreement. Delivery is led from our Plainsboro, New Jersey headquarters and staffed from our Hyderabad global delivery center, plus London and Dubai for regional coverage.

Which channels does Pronix cover for conversational AI?

Voice, web chat, mobile in-app messaging and business messaging channels such as WhatsApp and RCS, unified behind shared intent and context handling.

Do you build agents that take action, not just answer?

Yes — where a use case needs to update a system of record or complete a transaction, we design it as a tool-calling agent with scoped permissions and evaluation.

How is conversational AI quality measured after launch?

Containment, escalation quality and accuracy against a labelled question set, tracked continuously through the evaluation practice we put in place.

What is the difference between a conversational AI assistant and an AI agent?

Assistants answer, retrieve and route inside a scripted or intent-driven flow. Agents reason, use tools and complete transactions in your systems of record with authorization, evals and audit. Most enterprises need both — assistants for scoped conversations, agents for end-to-end resolution.

How do you pick between Kore.ai, Dialogflow CX, Watsonx and Agentforce?

By fit — CRM alignment, contact-center estate, model portability, governance, vertical accelerators and total cost. We run a platform-selection sprint that scores each against your top intents, data sources and target channels before you commit.

Can we migrate off a legacy IVR without disrupting live traffic?

Yes. We run parallel deployment with regression parity gates and canary cutover per queue and language, so nothing breaks and CSAT and containment are measured against the legacy baseline.

What does a typical engagement cost and how long does it take?

Time-and-materials or fixed-fee per phase. A first production assistant on a chosen platform typically ships in 6–10 weeks; voice deployments and multi-language rollouts run 10–16 weeks. Managed operations are a monthly subscription tied to volume and channels.

How long does it take to reach a first production release?

A scoped first release typically reaches production in 6 to 10 weeks: discovery and platform selection, then a pilot on one intent or channel before a governed rollout. Voice deployments and multi-language programs run 10 to 16 weeks because of the additional telephony and localization testing involved.

How is a conversational AI engagement priced?

Assessments and platform-selection sprints are fixed-fee, implementation is priced by milestone against agreed intents, channels and integrations, and managed operations run as a monthly tier. Cost is driven mainly by the number of channels, the depth of CRM/CCaaS integration and whether voice is in scope.

How does conversational AI differ from your AI business automation service, or building it in-house?

Conversational AI covers voice, chat and email interactions with your customers and employees, while AI business automation focuses on back-office document and case processing — the two are often paired but scoped separately. Building in-house is possible, but most teams lack a production evaluation practice and platform-specific implementation experience, which is where engagements stall.

What does managed support for conversational AI include, and what hours does it cover?

Managed support covers intent tuning, evaluation-gate monitoring, drift review, incident response and release management under a documented SLA. Standard coverage is business hours in your time zone, with 24x7 follow-the-sun support available for production contact-center deployments.

What do conversational AI specialists cost, and where is delivery based?

Conversational AI engineers and platform architects are billed at a monthly per-person rate with a standard notice period built into the staffing agreement. Delivery is led from our Plainsboro, New Jersey headquarters and staffed from our Hyderabad global delivery center, plus London and Dubai for regional coverage.

Conversational AI Consulting & Implementation

Conversational AI design and deployment across voice and digital channels — Pronix.ai.

Book a strategy session Explore the reference architecture

One team for strategy, platform selection, build and managed operations — so conversational AI doesn't stall between the pilot and the contact center.

Outcomes

Measurable outcomes, not slideware.

Conversational AI engagements are designed around a small set of business outcomes we commit to at kickoff.

Book a scoping call

ROI modellive calculator

Inputs

Containment on scoped intents40–70%

AHT reduction with assisted handoff25%+

First production assistant6–10 wk

Output

Regression parity on IVR migration99%+

How we deliver

Assess → Design → Pilot → Implement → Scale → Operate.

One delivery model that moves from strategy to production without handoffs — with managed operations built in.

See how we deliver

Implementation · Conversational AIphased delivery

Assess

Intent inventory, channel map, data readiness, platform fit.

wk 1

Select

Kore.ai / Dialogflow CX / Watsonx / Agentforce scoring.

wk 3

Design

Assistant vs agent portfolio, guardrails, evaluation harness.

wk 5

Pilot

One high-volume intent, one channel, live evals from day one.

wk 7

Implement

Production build, CCaaS + CRM integration, canary rollout.

wk 9

Operate

Managed ops, weekly eval gates, tuning and new use cases.

wk 11

40–70%

Containment on scoped intents

25%+

AHT reduction with assisted handoff

6–10 wk

First production assistant

99%+

Regression parity on IVR migration

The enterprise challenge

Most conversational AI programs stall on platform fit and evals.

The blockers are rarely the model. They are platform fit for your CRM and CCaaS estate, unclear deployment patterns across voice, chat and email, missing evaluation harnesses and a migration path off legacy IVR that respects live traffic.

Assistants vs agents
Teams pick one paradigm and get stuck. We design a portfolio — scoped assistants for containment, agents for end-to-end resolution — under one governance model.
Platform selection is a decision, not a preference
Kore.ai, Dialogflow CX, Watsonx and Agentforce each win in different estates. We score them against your intents, data, channels and total cost before you commit.
No production evaluation practice
Without golden sets, drift monitoring and human-in-the-loop review, quality regressions surface as complaints. We instrument first, ship second.

Capabilities

Everything needed to ship conversational AI to production.

Assistants and agents, platform-native, evaluated in production, integrated with your CCaaS and CRM.

Assistants vs agents design

Portfolio design across scoped assistants (FAQ, self-service, forms) and agents (transactional, tool-using) with clear ownership and escalation.

Platform selection

Structured scoring across Kore.ai XO / Agent Platform, Google Dialogflow CX, IBM Watsonx Assistant and Salesforce Agentforce — mapped to your top intents, data and channels.

Voice deployments

Low-latency voice agents on Amazon Connect, Genesys, NICE, Five9 and Google CCAI — with barge-in, DTMF, warm handoff and CRM screen-pop.

Chat and messaging deployments

Web, in-app, WhatsApp, RCS, SMS, Messenger and Teams — unified intents, context and knowledge across channels.

Email and case triage

Inbound email classification, extraction, response drafting and case routing — integrated with Salesforce, Dynamics, ServiceNow and Zendesk.

Evals and guardrails

Golden sets, offline/online evals, PII redaction, prompt-injection defense, toxicity detection, drift monitoring and human-in-the-loop review.

Legacy IVR migration

Assess, map and rebuild legacy IVR and first-gen bots into modern conversational AI with regression parity and canary cutover per queue and language.

Pricing model advisory

Per-conversation, per-minute, per-resolution and platform-license modeling — with total-cost-of-ownership across build, run and managed operations.

Reference architecture

The conversational AI reference architecture.

One conversational layer across voice and digital, wired into your CCaaS platform and CRM rather than bolted alongside them.

Customer channelsSystems of record

01
Customer channels
Voice, web chat, mobile app, WhatsApp, SMS and social — one conversation model, channel-specific presentation.
02
NLU & dialog orchestration
Intent, entity and LLM-based understanding, disambiguation, context carry-over and deterministic dialog policy for regulated flows.
03
Knowledge & fulfilment
Grounded retrieval over approved content plus transactional fulfilment through APIs — order status, payments, appointments, account changes.
04
Escalation & agent handoff
Warm transfer with full transcript, intent and authentication state so the customer never repeats themselves.
05
CCaaS & systems of record
Amazon Connect, Genesys Cloud, NICE CXone or Five9 alongside CRM, order management and billing.

Conversation lifecycle

Every conversation — voice or digital — runs the same six-stage loop.

Detect → Authenticate → Retrieve → Act → Resolve → Summarize. Each stage has explicit controls so voice AI, agent assist and automated QA plug into one operating model.

Detect

Identify intent across voice and digital with unified NLU, sentiment and language detection.

Intent model
Language detect
Sentiment
Channel context

Authenticate

Verify the customer with voice biometrics, OTP or federated SSO before any transaction.

Voice biometrics
OTP / step-up
SSO / KYC
Consent capture

Retrieve

Ground the conversation in governed knowledge and system-of-record data with citation and access control.

Governed RAG
CRM lookup
Order / policy data
Citations

Act

Execute transactions through scoped tool connectors — billing, scheduling, order and case actions.

Scoped tools
Per-action auth
Idempotency
Audit trail

Resolve or escalate

Complete the intent, or warm-transfer to a live agent with full context, transcript and next-best-action.

Warm handoff
Context payload
NBA to agent
Screen pop

Summarize & QA

Auto-summarize, code the disposition, and score 100% of interactions with automated quality and compliance detection.

Auto-summary
Disposition
Automated QA
Compliance flags

Trust & compliance spine

One policy layer across every stage — voice and digital.

Consent, redaction, residency and human oversight are enforced inside the conversation runtime — not in a separate ops process.

PII redaction & masking
Consent & recording rules
Regional data residency
Real-time policy guardrails
Human-in-the-loop escalation

Definition

What is conversational AI in an enterprise contact center?

Conversational AI is software that holds a natural-language conversation across voice and digital channels and resolves the customer's intent by acting in enterprise systems. Enterprise deployments combine intent understanding, retrieval from approved knowledge, transactional integrations, guardrails for what the bot may do unaided, and a clean handover to a human with full context when confidence or policy limits are reached.

Also known as: virtual agents, chatbots, voice bots.

Success metric: Resolution rate, not deflection rate
Failure mode: Containment without resolution, which raises repeat contacts
Requirement: Context-preserving handover to a live agent

Deflection vs containment vs resolution

Deflection vs containment vs resolution
Measure	What it counts	Why it can mislead
Deflection	Contacts kept out of the queue	Customer may re-contact another channel
Containment	Sessions ended in the bot	Includes abandonment
Resolution	Intents actually completed	The only measure tied to cost and CSAT

How we deliver

A six-step model, from assessment to managed operations.

Every engagement follows the same rhythm — so business, IT and delivery stay aligned from opportunity to outcome.

Assess

Intent inventory, channel map, data readiness, platform fit.

Select

Kore.ai / Dialogflow CX / Watsonx / Agentforce scoring.

Design

Assistant vs agent portfolio, guardrails, evaluation harness.

Pilot

One high-volume intent, one channel, live evals from day one.

Implement

Production build, CCaaS + CRM integration, canary rollout.

Operate

Managed ops, weekly eval gates, tuning and new use cases.

Where it lands

Use cases already in production with enterprise clients.

Voice assistant that resolves, not deflects

Authenticated, transactional voice agent on Amazon Connect / Genesys / NICE / Google CCAI that finishes the job and hands off with context.

Cross-channel digital assistant

One assistant across web, WhatsApp, in-app and RCS — with unified intent, session state and knowledge.

Legacy IVR migration to conversational AI

Rebuild a legacy IVR or first-gen bot on Kore.ai, Dialogflow CX, Watsonx or Agentforce with 99%+ regression parity.

Email and case triage

Automated classification, extraction and drafting for inbound email — routed into Salesforce, Dynamics or ServiceNow.

Platform selection sprint

A 3–4 week engagement that scores Kore.ai, Dialogflow CX, Watsonx and Agentforce against your intents, data and channels.

Delivered by

Pronix service lines

Runs on

Partner platforms we implement

Explore platform capabilities →

Industry patterns

Industries where this ships fastest

Financial Services
Insurance
Healthcare Providers
Health Payers
Retail & Ecommerce
BPO

See industry solutions →

Runs on

Grounded on your data. Governed on day one.

Every platform we implement is only as good as the retrieval, connectors and controls behind it. These are the horizontal solutions we ship with every engagement.

Not sure where to start? Score your organization in 10 minutes.Take the AI Readiness Assessment →

Quick answer

What does Pronix deliver in a conversational AI implementation?

Pronix designs and builds conversational AI — assistants and agents across voice, web, mobile and messaging channels — including platform selection, dialog and tool design, and a production evaluation practice, for enterprises replacing scripted bots or scaling a pilot into a governed program. Delivery runs from the Plainsboro, NJ headquarters with engineering delivered through the Hyderabad center, supported by London and Dubai for regional programs.

Last reviewed 2026-08-05

Assistants versus agents is a design decision

Each use case is scoped as a retrieval-and-answer assistant or a tool-calling agent based on whether it needs to take action, not by default to whichever pattern is fashionable.

Platform selection follows the estate

Recommendations weigh channel coverage, existing CCaaS and CRM integration, and total cost rather than defaulting to a single vendor.

Evaluation is built before scale

A labelled test set and evaluation harness run on every prompt or model change so accuracy and containment can be trusted before wider rollout.

How Conversational AI engagements are bought, supported and staffed.

Most enterprises start with an assessment, move into a fixed-scope build, keep it running under managed support, and add conversational AI engineers where their own team is short. All four can run together under one commercial agreement.

Assessment and roadmap
A bounded Conversational AI assessment: current-state review, prioritized use cases, target architecture, business case and a sequenced delivery roadmap.
Fixed price · 2–4 weeks typical
Fixed-scope build
A defined Conversational AI implementation — architecture, build, integration, testing, evaluation and a documented production release against agreed acceptance criteria.
Fixed price · 8–16 weeks typical
Managed run and support
Monthly operations for Conversational AI in production: release management, integration monitoring, configuration changes, model and agent evaluation and incident response under one SLA.
Monthly service tier · 24×7 coverage available
Staff augmentation
Conversational AI engineers, solution architects and delivery leads embedded in your team, reporting to your delivery manager.
Monthly per person · typically live in 2–4 weeks

Where Conversational AI delivery happens

Programs are led from our Plainsboro, New Jersey headquarters and delivered with our Hyderabad global delivery center, plus London and Dubai for EMEA and Middle East clients.

Support coverage

Business-hours support in your time zone as standard, follow-the-sun 24×7 for production contact center and agentic workloads, with named escalation and monthly service reviews.

Get a Conversational AI scope and price Hire conversational AI engineers

Frequently asked

Questions buyers ask us first.

What is the difference between a conversational AI assistant and an AI agent?: Assistants answer, retrieve and route inside a scripted or intent-driven flow. Agents reason, use tools and complete transactions in your systems of record with authorization, evals and audit. Most enterprises need both — assistants for scoped conversations, agents for end-to-end resolution.
How do you pick between Kore.ai, Dialogflow CX, Watsonx and Agentforce?: By fit — CRM alignment, contact-center estate, model portability, governance, vertical accelerators and total cost. We run a platform-selection sprint that scores each against your top intents, data sources and target channels before you commit.
Can we migrate off a legacy IVR without disrupting live traffic?: Yes. We run parallel deployment with regression parity gates and canary cutover per queue and language, so nothing breaks and CSAT and containment are measured against the legacy baseline.
How do you evaluate conversational AI quality in production?: Golden sets, offline and online evals, prompt/response scoring, PII and toxicity detection, drift monitoring and human-in-the-loop review — operated as a program with weekly gates, not a one-off script.
What does a typical engagement cost and how long does it take?: Time-and-materials or fixed-fee per phase. A first production assistant on a chosen platform typically ships in 6–10 weeks; voice deployments and multi-language rollouts run 10–16 weeks. Managed operations are a monthly subscription tied to volume and channels.
How long does it take to reach a first production release?: A scoped first release typically reaches production in 6 to 10 weeks: discovery and platform selection, then a pilot on one intent or channel before a governed rollout. Voice deployments and multi-language programs run 10 to 16 weeks because of the additional telephony and localization testing involved.
How is a conversational AI engagement priced?: Assessments and platform-selection sprints are fixed-fee, implementation is priced by milestone against agreed intents, channels and integrations, and managed operations run as a monthly tier. Cost is driven mainly by the number of channels, the depth of CRM/CCaaS integration and whether voice is in scope.
How does conversational AI differ from your AI business automation service, or building it in-house?: Conversational AI covers voice, chat and email interactions with your customers and employees, while AI business automation focuses on back-office document and case processing — the two are often paired but scoped separately. Building in-house is possible, but most teams lack a production evaluation practice and platform-specific implementation experience, which is where engagements stall.
What does managed support for conversational AI include, and what hours does it cover?: Managed support covers intent tuning, evaluation-gate monitoring, drift review, incident response and release management under a documented SLA. Standard coverage is business hours in your time zone, with 24x7 follow-the-sun support available for production contact-center deployments.
What do conversational AI specialists cost, and where is delivery based?: Conversational AI engineers and platform architects are billed at a monthly per-person rate with a standard notice period built into the staffing agreement. Delivery is led from our Plainsboro, New Jersey headquarters and staffed from our Hyderabad global delivery center, plus London and Dubai for regional coverage.

How we work

Engagement models that fit your program — advisory, build, run, or embedded pods.

Strategy & advisory Implementation Managed services Talent pods All engagement models →

Who we are

pronix.ai is the AI & CX systems integrator practice of Pronix Inc.

One accountable delivery model: US-based architecture and program leadership with global engineering pods running 24×7 build, cutover and hypercare.

Founded: 2010 · Pronix Inc
Headquarters: 666 Plainsboro Rd, Suite 1361, Plainsboro, NJ 08536
Delivery centers: United States · India (Hyderabad) · EMEA
Engagement model: Fixed-scope implementation, managed run, staff augmentation and T&M Agile Teams.

Certifications

AWS Certified (Solutions Architect, Developer)
Amazon Connect specialty
Genesys Cloud CX certified
NICE CXone certified
Salesforce certified (Service Cloud, Agentforce)
Microsoft Azure AI certified

Security questionnaires, controls documentation and named client references are available under NDA. More about Pronix Inc →

Next step

Book a working session with our conversational ai team.

30 minutes. Your architecture, your data, your KPIs. You leave with a concrete pilot outline and a business case worth defending.

Schedule the session →Read the playbook

Conversational AI consulting and implementation.

Measurable outcomes, not slideware.

Assess → Design → Pilot → Implement → Scale → Operate.

Most conversational AI programs stall on platform fit and evals.

Everything needed to ship conversational AI to production.

The conversational AI reference architecture.

Customer channels

NLU & dialog orchestration

Knowledge & fulfilment

Escalation & agent handoff

CCaaS & systems of record