Most AI programmes stall between the pilot and the P&L. We take one bounded use case into production on the platforms you already own, measure it against the baseline we agreed before we started, and only then talk about scale.
- Financial ServicesLarge US Bank
- InsuranceNational P&C Insurer
- Healthcare ProvidersLarge National Health System
- Health PayersRegional Managed Care Payer
- Retail & EcommerceGlobal Omnichannel Retailer
- BPO & OutsourcingGlobal BPO
- Technology & SaaSGlobal Enterprise SaaS
- Telecom & UtilitiesNational Telecom Operator
Named client logos available under NDA on request.
Production evidence
Six delivered programmes with client-approved metrics. Client names appear only where disclosure is permitted; otherwise the profile is given instead.
Agentic first-pass claim triage with a mandatory human review before any denial — 11 minutes to under 4, with zero CMS audit findings.
Read the case study →A full contact center cutover onto Amazon Connect inside one peak-season window, with conversational IVR live on day one.
Read the case study →Real-time agent assist rolled out across a global seat base and multiple languages, tuned per client programme rather than per vendor default.
Read the case study →Model routing, prompt caching and an evaluation gate in CI took run-rate inference cost down without touching answer quality.
Read the case study →Document intake, verification and exception routing automated end to end, with every agent action logged and replayable for audit.
Read the case study →Sample-based QA replaced with automated scoring of every interaction, so coaching targets behaviour instead of a 2% sample.
Read the case study →Engagement models
Three ways to start. We do not publish a price list: every enterprise estate has a different integration surface, data maturity and regulatory posture, so we scope your engagement in a 30-minute discovery call and send a written estimate we can stand behind in a statement of work.
For what sits inside each model — practices, platforms, delivery locations and support coverage — see Pronix.ai capabilities →
Diagnostic
A fixed-fee assessment that tells you which AI use cases are worth funding, in what order, and what each is worth.
- Use-case inventory scored on value, feasibility and risk
- Baseline metrics and a defensible ROI model per use case
- Target architecture and platform recommendation
- Sequenced 12-month roadmap with funding gates
Best for: Boards that have approved AI in principle but not in budget.
Discuss diagnostic →Production pilot
One use case, live in production with real traffic, measured against the baseline we agreed up front.
- Working system in production, not a sandbox demo
- Integrations into your systems of record
- Evaluation harness, guardrails and human-in-the-loop review
- Measured before/after outcome report and a scale decision
Best for: Teams that need one credible production result to unlock scale funding.
Discuss production pilot →Scale & run
Multi-use-case delivery plus managed run: we build the platform, then operate and improve it against SLAs.
- Blended pod: solution architect, engineers, CX/AI specialist, delivery lead
- Platform, MLOps/LLMOps and FinOps ownership
- Quality, safety and cost governance with monthly reporting
- Roadmap execution against agreed business outcomes
Best for: Enterprises moving from a proven pilot to a portfolio of live AI workloads.
Discuss scale & run →Tell us what you are trying to change.
A delivery lead — not a sales development rep — replies within one business day with an indicative scope, timeline and a written estimate for your situation.
How Enterprise AI and CX delivery 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 AI and CX engineers and architects where their own team is short. All four can run together under one commercial agreement.
Assessment and roadmap
A bounded Enterprise AI and CX delivery 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 Enterprise AI and CX delivery 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 Enterprise AI and CX delivery 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
AI and CX engineers and architects, solution architects and delivery leads embedded in your team, reporting to your delivery manager.
Monthly per person · typically live in 2–4 weeks
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.
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.
Pricing and engagement questions
How do you price an engagement?
Per scope, not per price list. A diagnostic is a fixed fee, a production pilot is fixed-price against an agreed milestone plan, and scale-and-run is a retained monthly pod. The number depends on integration count, data readiness, compliance scope and the number of channels or languages involved — so we scope it in a 30-minute discovery call and send a written estimate rather than publish a band that would not apply to you.
Why don't you publish prices?
Because every enterprise estate we have worked in has a different integration surface, data maturity and regulatory posture, and a published band would either overstate a simple engagement or understate a complex one. We would rather spend 30 minutes understanding your scope and give you a number we can stand behind in a statement of work.
Is the diagnostic fee credited if we proceed?
Yes. The diagnostic fee is credited in full against a production pilot booked within 90 days of the final readout, so the assessment is not a sunk cost if you decide to build.
What does a production pilot actually deliver?
A working system handling real traffic in your environment — integrated with your systems of record, wrapped in guardrails and human-in-the-loop review, and measured against a baseline agreed before we start. You also receive the evaluation harness so your team can keep testing after we hand over.
Do you work with our existing platform vendors?
Yes. Pronix.ai is a systems integrator, not a software vendor. We deliver on the platforms you already own or are evaluating — Amazon Connect, Genesys, NICE CXone, Five9, Kore.ai, Microsoft, Salesforce — and we will tell you when your incumbent platform is the right answer.
How quickly can we start?
A diagnostic typically starts within two weeks of a signed statement of work. Pilot mobilisation depends on environment access and security review; most enterprise clients are running discovery inside three weeks.
Who owns the intellectual property?
You do. Code, prompts, evaluation sets and documentation produced in your engagement are yours, delivered into your repositories and cloud accounts. We retain no runtime dependency on Pronix.ai infrastructure.
What does support cover once we are in production, and during which hours?
Scale-and-run covers release management, integration monitoring, configuration changes, agent and model evaluation and incident response under one SLA. Business-hours coverage is standard, with follow-the-sun 24×7 available for production contact center and agentic workloads, a named escalation path and monthly service reviews.
Can we hire your specialists into our own team instead?
Yes. Staff augmentation is priced monthly per person by role and seniority, with a standard 30-day notice period on either side and engineers typically live inside two to four weeks. Our talent hub lists the platform and role combinations we staff.
Where do the people delivering our engagement sit?
Our Plainsboro, New Jersey headquarters, our Hyderabad global delivery center, and our London and Dubai teams — assembled as onshore, overlapping-hours or offshore blends on the same programme, with one accountable delivery lead.
How does Pronix.ai structure and price an AI engagement?
Pronix.ai engages through three commercial models: a fixed-scope Diagnostic that produces a decision-ready assessment and business case, a time-boxed Pilot that puts one use case into production with measured outcomes, and a Scale engagement that runs a portfolio with managed operations. Scope, not a price list, determines the commercial shape.
What you enter
- The use case or business problem
- Industry and regulatory context
- Scope: single workflow, multi-workflow or enterprise portfolio
How it is calculated
- 1.Identify the decision the engagement has to support: assess, prove or scale.
- 2.Match that decision to the Diagnostic, Pilot or Scale model.
- 3.Define the outcome the engagement is accountable for.
- 4.Set the phase plan, team shape and duration around that outcome.
- 5.Agree the commercial structure against defined scope and use cases.
What you get back
- Recommended engagement model
- Phase plan with outcomes per phase
- Team shape and duration
- Route into a scoped commercial conversation
Built for: Enterprise buyers deciding how to start or scale an AI and CX programme.
Does Pronix.ai publish fixed prices for AI engagements?
No. Enterprise AI and CX scope varies with use case, integration surface, data readiness and regulatory context, so commercials are set against a defined scope rather than a published rate card.
What is the right first engagement for an enterprise starting with AI?
A fixed-scope Diagnostic when the portfolio and priorities are unclear, or a time-boxed Pilot when the use case is already agreed and the question is whether it survives production conditions.