How are implementation engagements priced?

Fixed-scope pilots with defined acceptance criteria, then a rate-card or managed-service model for scale and run-state.

Can you work alongside an existing systems integrator?

Yes — we frequently deliver the AI and CX layer while an incumbent SI owns core platform and integration work.

What happens after go-live?

Either hand-back with documentation and training, or an AI managed service covering monitoring, evaluation drift and continuous tuning.

What is included in enterprise AI implementation services?

Architecture and build, platform configuration, data and retrieval integration, connectors to your systems of record, testing and evaluation, phased rollout, enablement and managed operations.

How much does AI implementation cost?

Pilots are typically fixed-scope engagements; production programs are priced by use case with separate lines for build, platform consumption and run. We model all three before kickoff.

How long does an AI implementation take?

A first production use case usually ships in 8–12 weeks. Multi-platform or multi-region programs run in phased waves after that.

Do you support AI implementation for regulated industries?

Yes — healthcare, insurance and financial services delivery with HIPAA and SOC 2 alignment, entitlement-aware retrieval, model-risk documentation and audit-ready evidence.

Which AI implementation use cases deliver value fastest?

High-volume, well-instrumented workflows: containment on repeatable intents, agent assist, after-call work, document intake and enterprise knowledge search.

What drives the cost of an implementation engagement?

Cost is driven by the number and complexity of integrations, the amount of retrieval and data work required, evaluation and testing depth, and whether the program is a single pilot or a multi-wave rollout. We scope build, platform consumption and run as separate line items so the driver of any cost change is visible.

How does implementation differ from Strategy & Consulting or doing it in-house?

Strategy & Consulting produces the roadmap and business case before funding; implementation is where that plan gets built, integrated and shipped to production. Compared with doing it in-house, we bring delivery teams who have already solved retrieval, connector and governance problems on prior programs, so the first production release doesn't have to fund that learning curve from scratch.

What does managed support cover after go-live, and what are the SLA hours?

Managed support covers monitoring, incident response, connector and integration maintenance, evaluation upkeep and periodic tuning, delivered under an SLA with business-hours coverage as standard and 24x7 follow-the-sun coverage available for production-critical workloads.

How is specialized implementation talent staffed and priced?

Implementation engineers and architects are staffed at a monthly rate per person against agreed scope, with a standard notice period for scaling teams up or down. Delivery is staffed from our Plainsboro, New Jersey headquarters, our Hyderabad global delivery center, London and Dubai, giving programs follow-the-sun coverage.

Implementation Services

AI implementation and integration services, from build to production rollout.

Pronix delivers enterprise AI implementation services across Amazon Connect, Genesys, NICE, Five9, Salesforce Agentforce, Google CCAI, Azure AI, AWS Bedrock, Gemini Enterprise, Anthropic, OpenAI, Kore.ai, Dialogflow CX and IBM watsonx. One partner, one delivery model — with retrieval, connectors, evaluation and governance wired in from day one, transparent AI implementation cost models, and controls built for regulated industries.

Book a strategy session See how we deliver

Once your strategy is funded, implementation is where value shows up. Our engineering practice ships production AI and CCaaS deployments in 8–14 weeks per use case, on your platform of choice, under a single delivery contract.

How it works

Implementation Services — the architecture at a glance.

The moving parts of a production deployment, from channels and orchestration to systems of record.

Talk to an architect

Enterprise AI stackreference architecture

Governance & Risk

PolicyPIIAudit

Applications

CopilotsAgentsSearch

Models & Orchestration

LLMsGuardrailsEval

Data & Retrieval

VectorWarehouseAPIs

Foundation

IdentityNetworkingObservability

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 · Implementation Servicesphased delivery

Assess

Inherit strategy artifacts and acceptance criteria.

wk 1

Design

Solution architecture, retrieval and integration plan.

wk 3

Pilot

One production-grade use case with measured KPIs.

wk 5

Implement

Build, integrate, evaluate, harden, document.

wk 7

Scale

LOBs, geographies, languages, additional use cases.

wk 9

Operate

Handover to Managed Services under one SLA.

wk 11

8–14 wk

Per production use case

10+

Partner platforms delivered

Delivery contract across the estate

Governance debt handed to ops

Not funded yet? Start with Strategy & Consulting for a CFO-ready roadmap first, then we implement — see /services/strategy-consulting.

The enterprise challenge

Implementation risk is where AI programs die.

Model quality, retrieval, connectors, identity, evaluations and change management all have to land together. Miss any one and the pilot never scales. Our delivery model closes those gaps as one integrated build.

Pilot-to-scale valley
Most enterprise AI pilots never make it to LOB rollout because retrieval, connectors and governance were bolted on late.
SI-led builds run over budget
Traditional SI models bill hours; we bill outcomes with fixed pilot scopes and clear acceptance criteria.
Platform sprawl
Different platforms per BU is fine — as long as one partner runs the same delivery model across all of them. That's us.

Capabilities

End-to-end implementation across every enterprise AI & CX stack.

One delivery model, ten+ partner platforms — with data, retrieval and governance built in.

Foundation-model implementation

Azure OpenAI, AWS Bedrock, Gemini Enterprise, Anthropic Claude, OpenAI Enterprise and IBM watsonx — deployed with SSO, DLP, retrieval and evaluations.

Agentic AI build

Autonomous agents on Agentforce, Copilot Studio, Kore.ai and Bedrock Agents — with tool APIs, human-in-the-loop and audit.

CCaaS implementation & migration

New builds, in-place upgrades and cross-platform migrations across Amazon Connect, Genesys Cloud CX, NICE CXone, Five9, Google CCAI and Microsoft Dynamics 365 CCaaS.

Conversational AI build

Dialogflow CX, Amazon Lex, Copilot Studio, watsonx Assistant and Retell — voice and digital, omnichannel.

Data & retrieval integration

RAG-grade knowledge, vector infra and entitlement-aware retrieval bound to your CRM, EHR, ERP and ITSM.

Testing, evaluations & rollout

Prompt regression, safety evals, load/perf and phased LOB rollout with acceptance gates.

Definition

What do AI implementation services include?

AI implementation services cover the delivery path from design to managed operation: solution and integration design, platform configuration, data and knowledge preparation, guardrail and policy setup, evaluation harness construction, migration or cutover, hypercare, and ongoing tuning under SLA. Delivered as a system integrator engagement, the scope includes the enterprise systems the AI must act inside — CRM, order management, EHR, core banking — not just the AI platform itself.

Also known as: AI systems integration, AI delivery services.

Typical first release: One workflow live in production within a quarter
Included: Integration, evaluation, guardrails, cutover and hypercare
Handover: Runbooks, evaluation sets and monitoring transferred to the client team

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

Inherit strategy artifacts and acceptance criteria.

Design

Solution architecture, retrieval and integration plan.

Pilot

One production-grade use case with measured KPIs.

Implement

Build, integrate, evaluate, harden, document.

Scale

LOBs, geographies, languages, additional use cases.

Operate

Handover to Managed Services under one SLA.

Where it lands

Use cases already in production with enterprise clients.

Voice AI + Agent Assist deployment

Containment bots, real-time coaching and after-call summarization on your CCaaS of record.

Enterprise copilot rollout

ChatGPT Enterprise or Copilot deployment with custom GPTs, DLP and identity controls.

Agentic workflow build

Autonomous agents for claims, service, RCM or back-office — with tool APIs and human-in-the-loop.

CCaaS migration

Cross-platform migration (e.g. legacy Genesys to Amazon Connect) with parallel-run and cutover.

Delivered by

Pronix service lines

Talent Hub — hire implementation engineers →

Runs on

Partner platforms we implement

Explore platform capabilities →

Industry patterns

Industries where this ships fastest

Financial Services
Healthcare Providers
Health Payers
Insurance
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 an AI implementation partner do that an internal team cannot?

An AI implementation partner brings pattern reuse across platforms, delivery staffing that does not compete with run-state priorities, and an evaluation and integration toolkit that exists on day one. Pronix delivers on Amazon Connect, Genesys, NICE, Five9, Salesforce, Google CCAI, AWS Bedrock, Azure AI and IBM watsonx with fixed-scope pilots and documented hand-back to internal teams.

Last reviewed 2026-08-05

Fixed scope, explicit acceptance criteria

Each engagement defines the metric, the traffic it is measured on and the date it is measured, so delivery is verifiable rather than open-ended.

Hand-back is part of the plan

Runbooks, evaluation suites, prompt and tool inventories and knowledge transfer sessions are delivery artifacts, not optional extras.

Cross-platform experience shortens decisions

Having implemented the same intent patterns on multiple platforms, we can size integration effort and known constraints before committing to an architecture.

The delivery artifacts you get, named up front.

Every enterprise CX and AI engagement is governed by the same six artifacts. They are contracted deliverables with owners and dates — the way a systems integrator works, not a discovery deck.

01
Reference architecture
A signed-off enterprise CX and AI reference architecture — environments, tenancy, network and identity boundaries, data flows and AI components — issued as a versioned diagram plus infrastructure-as-code, not a slide.
02
Integration surface
Every integration surface is inventoried before build: CRM and CTI, WFM, ticketing, payments, identity, data warehouse and event streams — each with contract, auth model, throughput, failure mode and owner.
03
Runbook
Operational runbooks per service: deploy and rollback steps, configuration change control, alert thresholds, on-call escalation paths and known-issue playbooks handed to your ops team at go-live.
04
Cutover plan
A wave-based cutover plan with entry and exit criteria per wave, dress rehearsal, freeze windows, number porting and data migration sequencing, rollback triggers and a go/no-go decision log.
05
Hypercare
A defined hypercare window after each wave — command centre, daily triage, defect burndown against severity targets, and a documented exit gate before the estate transitions to steady-state run.
06
SLA
Run-state SLAs and SLOs written into the engagement: severity definitions, response and restoration targets, change lead times, availability reporting and quarterly value review against the original business case.

How Implementation Services 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 implementation engineers where their own team is short. All four can run together under one commercial agreement.

Assessment and roadmap
A bounded Implementation Services 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 Implementation Services 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 Implementation Services 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
Implementation 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 Implementation Services 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 Implementation Services scope and price Hire implementation engineers

In depth

What enterprise AI implementation services cover: use cases, cost, regulated delivery and rollout.

Enterprise AI implementation services

Enterprise AI implementation services span foundation-model deployment, agentic build, CCaaS implementation and migration, conversational AI, retrieval integration and evaluation engineering. We deliver against fixed pilot scopes with published acceptance criteria, then scale line of business by line of business on the same architecture.

Fixed-scope pilots with acceptance gates
Retrieval, connectors and identity from day one
Phased LOB rollout with rollback plans

AI implementation cost

AI implementation cost has three parts: build, platform consumption and run. We size all three up front — token and inference forecasts, licensing, integration effort and managed operations — so the business case survives contact with production instead of surprising finance in month four.

Build, consumption and run modelled separately
Inference and token forecasting per use case
Cost tuning as a standing workstream

AI implementation for regulated industries

AI implementation for regulated industries adds the evidence layer: data residency, PHI and PII handling, access entitlements carried into retrieval, model-risk documentation, human review paths and audit trails for every automated action — designed with your risk and compliance teams, not retrofitted for them.

HIPAA, SOC 2 and model-risk alignment
Entitlement-aware retrieval and redaction
Audit trails and documented human review

AI implementation use cases

The AI implementation use cases that pay back fastest are high-volume and well-instrumented: voice and digital containment, agent assist and after-call work, document intake and extraction, enterprise knowledge search, and back-office exception handling.

Voice and digital self-service containment
Agent assist and after-call automation
Document intake, extraction and exceptions

Talk to us

Book an AI implementation consultation

Scope, pod shape, timeline and cost for taking your use case from pilot to production — with acceptance criteria agreed up front.

Scope and delivery-pod sizing
Integration and environment plan
Fixed-scope pilot proposal

Book an implementation consultation See a delivery walkthrough Contact the team

Explore next

Related capabilities and guides

Where this fits in our practice

cloud-native modernization consulting

Frequently asked

Questions buyers ask us first.

What is included in enterprise AI implementation services?: Architecture and build, platform configuration, data and retrieval integration, connectors to your systems of record, testing and evaluation, phased rollout, enablement and managed operations.
How much does AI implementation cost?: Pilots are typically fixed-scope engagements; production programs are priced by use case with separate lines for build, platform consumption and run. We model all three before kickoff.
How long does an AI implementation take?: A first production use case usually ships in 8–12 weeks. Multi-platform or multi-region programs run in phased waves after that.
Do you support AI implementation for regulated industries?: Yes — healthcare, insurance and financial services delivery with HIPAA and SOC 2 alignment, entitlement-aware retrieval, model-risk documentation and audit-ready evidence.
Which AI implementation use cases deliver value fastest?: High-volume, well-instrumented workflows: containment on repeatable intents, agent assist, after-call work, document intake and enterprise knowledge search.
What drives the cost of an implementation engagement?: Cost is driven by the number and complexity of integrations, the amount of retrieval and data work required, evaluation and testing depth, and whether the program is a single pilot or a multi-wave rollout. We scope build, platform consumption and run as separate line items so the driver of any cost change is visible.
How does implementation differ from Strategy & Consulting or doing it in-house?: Strategy & Consulting produces the roadmap and business case before funding; implementation is where that plan gets built, integrated and shipped to production. Compared with doing it in-house, we bring delivery teams who have already solved retrieval, connector and governance problems on prior programs, so the first production release doesn't have to fund that learning curve from scratch.
What does managed support cover after go-live, and what are the SLA hours?: Managed support covers monitoring, incident response, connector and integration maintenance, evaluation upkeep and periodic tuning, delivered under an SLA with business-hours coverage as standard and 24x7 follow-the-sun coverage available for production-critical workloads.
How is specialized implementation talent staffed and priced?: Implementation engineers and architects are staffed at a monthly rate per person against agreed scope, with a standard notice period for scaling teams up or down. Delivery is staffed from our Plainsboro, New Jersey headquarters, our Hyderabad global delivery center, London and Dubai, giving programs follow-the-sun coverage.

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 implementation services 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