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.

Implementation Services

AI implementation 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, Vertex AI, 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.

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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
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, Vertex AI, 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, 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.

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.

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.

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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.

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

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.

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