Solutions
Enterprise AI consulting and implementation services
Five practices — AI strategy and readiness assessment, forward deployed implementation, AI-ready talent, performance measurement and AI governance — that close the loop most AI programs leave open. Each is a standalone engagement; together they carry an organization from funding decision to a system that stays accurate.
DeployForward Deployed AI Implementation
Platform-agnostic talent. Real impact within 90 days.
Two distinct delivery models under one practice. The first is a general forward deployed team that identifies the right opportunity and builds it. The second is a specialized deployment of a pre-built agentic system tailored to a specific cost center domain. Both follow the same three-phase engagement structure, and both transfer knowledge and ownership to your team.
Delivery model 01
90-Day AI Forward Deployed Pod
A platform-agnostic team embedded inside your organization for 90 days, working alongside your people through three structured phases.
- We identify the opportunity, then build it in your environment
- By the time we leave, the solution is built, demonstrated, and owned
- Change management is part of the engagement, not a follow-on
CTO · IT Leaders · Product Ops · Engineering
Delivery model 02
Agentic Domain Systems
We deploy a pre-built agentic system scoped to your domain. Platform-agnostic talent means we work with your stack, not around it.
- Our proprietary platform arrives 80% pre-built for your cost center domain
- Configuration, integration, and rollout happen inside 90 days
- Managed Services keeps expanding it until you self-manage at full scale
HR and Onboarding · IT/Helpdesk · Contact Center · Finance Ops
90-Day AI Forward Deployed Pod
A platform-agnostic, forward deployed team embedded inside your organization for 90 days. We work alongside your people through three structured phases so that by the time we leave, the solution is built, demonstrated, and owned by your team.
Phase 1 · Month 1
Identify
Locate the highest-impact area for AI investment. We assess workflows, constraints, and readiness to find where effort produces real return.
Phase 2 · Month 2
Build
Build something that demonstrably works. By the end of this phase there is a functioning solution producing measurable impact, not a prototype.
Phase 3 · Month 3
Enable
Transfer ownership. Your team learns how to run it, extend it, and improve it. Change management is built into the engagement from day one.
Agentic Domain Systems: Deploy, Operate, and Expand
Three structured phases from workflow definition to a live agentic system to a team that knows how to run it.
Phase 1 · Month 1
Define
Map the domain workflows to be automated. Identify which processes are ready, where agents will have the most impact, and what success looks like.
Phase 2 · Month 2
Build
Deploy a working agentic system in your environment: a live, functioning agent producing real results inside your workflows by end of month.
Phase 3 · Month 3
Enable
Demonstrate and transfer operational ownership. Your team leaves ready to manage, monitor, and expand what was built.
Questions this practice answers
- Can we see real AI results in 90 days?
- What does an embedded AI team look like?
- How do we transition from pod to internal ownership?
- We have a repeatable, high-volume workflow. How do we automate it intelligently?
- How do we avoid building from scratch in a domain where patterns already exist?
- What happens after launch? Who manages and expands the system?
Roles we deploy
What comes next · Managed Services
The pod hands off to our Managed Services practice. We support and operate the system, continuing to expand it alongside your team until the organization reaches the maturity to self-manage. When you are ready to own it fully, we get out of the way.
Customer story · Healthcare payer
A national payer puts an agent in the live service desk in 90 days
After an eighteen-month sandbox proof-of-concept that never touched production, a forward deployed pod shipped autonomous Tier-1 resolution into the existing ITSM and handed over ownership in month three.
AmplifyAI-Enabled Talent Solutions
Every placement arrives AI-ready. That is the baseline.
Every IntePros placement, regardless of role, comes PCI-educated and AI-native. Engineers, PMO leads, ops talent, and beyond. There is no separate tier for AI-aware candidates. This is simply what our talent looks like. We staff teams that can operate in, alongside, and ahead of the AI tools already in your environment.
Standard 01
PCI-Enabled Workforce
All talent is educated on the Perpetual Change Imperative framework, ensuring AI-aware thinking is built in from day one, not trained after placement.
- Every engagement comes with this foundation
- AI readiness is pre-placement, not a post-placement add-on
- Talent who operate ahead of the tools in your environment
Engineering Teams · HR and TA Leaders
Standard 02
All Roles, One Standard
Software engineers, PMO, operations, QA, data roles, and beyond. The standard applies regardless of function, seniority, or engagement type.
- AI readiness is not optional in any function
- PMO and ops talent who accelerate transformation instead of stalling it
- One standard across contract, contract-to-hire, and permanent
PMO · Ops and Delivery · QA and Data
Questions this practice answers
- We need people who can actually work with the AI tools we are deploying, not catch up to them.
- How do we hire for a world where AI is part of every workflow, not a specialty skill?
- We need PMO, ops, or engineering talent that accelerates transformation rather than stalling it.
Roles we deploy
This is not a tier. It is the standard.
AI readiness is foundational to every IntePros placement, which means the people who run and extend what we build in Deploy come from the same bench.
Customer story · Life sciences
A clinical software group staffs 23 roles that were AI-native on day one
Delivery leadership needed PMO and engineering talent that could operate alongside deployed AI tooling from week one instead of being trained into it three months after onboarding.
MeasureAI Performance Intelligence
If you cannot measure it, you cannot fund it.
Think of it as analytics for how your organization uses AI, connected to the business outcomes that actually matter. We integrate employee AI engagement data into your existing business intelligence layer so you can tie tool adoption to cost savings, developer productivity to product revenue, and AI investment to defensible ROI.
Intelligence stream 01
Developer Productivity Intelligence
Map AI-assisted development activity (token usage, code generation, review, and iteration cycles) to product outcomes and revenue-generating features.
- Allocate resources toward the work that moves the number
- Surface token spending problems before they become cost crises
- Instrument pipelines for latency, error rates, and behavior drift
Engineering Leadership · Product Leaders
Intelligence stream 02
Enterprise Tool ROI Tracking
Connect internal AI tool usage (Copilot, assistants, and automation platforms) to cost savings and productivity metrics.
- Turn "we think it is working" into a board-ready business case
- Tie adoption to metrics your CFO will actually trust
- Identify which teams and tools generate real leverage
CIO / CFO · AI Enablement Leads · Transformation Officers
Questions this practice answers
- We are spending on AI tools. How do we prove it is worth it?
- Which developers, products, or teams are getting the most leverage from AI?
- How do we justify AI headcount and tooling in the next budget cycle?
- Where should we invest more tokens, more time, or more tooling?
Roles we deploy
Inside your existing BI stack
We integrate into the business intelligence layer you already run. No new dashboard nobody opens. The AI numbers land next to the numbers your leadership already reviews.
Customer story · Retail and consumer
A national specialty retailer proves $2.8M of margin from AI — and cuts three tools
Service deflection, catalog content and personalization were all live and none of it was measured against a holdout. Consent-aware measurement replaced anecdote before the peak season budget.
SustainAI Governance and Knowledge Systems
The infrastructure that keeps AI working, improving, and organizationally owned.
Most organizations deploy AI and then drift. Models degrade, policies lag, and institutional knowledge stays locked in people and systems that do not talk to each other. This practice is the infrastructure that prevents drift and turns AI deployment into a compounding asset rather than a depreciating one.
Practice 01
AI Governance and Responsible AI
Policies, risk frameworks, ethics checkpoints, and audit trail infrastructure, built around the PCI framework as a governance backbone.
- Model accountability and data handling
- Human oversight and change governance
- Auditable evidence for regulators, customers, and leadership
CAIOs · Legal and Compliance · Risk and Governance Leaders
Practice 02
Knowledge Management Systems
Capturing, organizing, and maintaining the institutional knowledge that AI systems depend on.
- Knowledge base architecture and taxonomy design
- Content governance that keeps knowledge usable
- Processes for keeping organizational knowledge current
CIOs · AI Enablement Leads · Knowledge Owners
Feedback Loop Design
Structured mechanisms for end users to rate, correct, and flag model outputs, feeding a reinforcement pipeline rather than disappearing.
Drift Detection
Monitoring model behavior over time to identify when outputs begin to degrade relative to baseline, triggering retraining before users lose trust.
Retraining and Versioning
Governance around when models get updated, how versions are tracked, how rollbacks work, and who approves promotion to production.
Evaluation Benchmarks
Client-specific test sets that measure domain accuracy, not generic benchmarks that mean nothing to the business.
Human-in-the-Loop Validation
Defining which decisions require human review before the model acts, and building those checkpoints into the workflow architecture.
Continuous Learning Infrastructure
The reinforcement pipeline that keeps domain AI systems accurate, current, and aligned with how your organization evolves. Training does not end at launch.
Questions this practice answers
- How do we ensure our AI systems stay accurate and trustworthy over time?
- Who owns the knowledge our AI systems depend on, and how do we keep it current?
- How do we demonstrate responsible, auditable AI use to regulators, customers, or leadership?
Roles we deploy
The through-line
The domain agent delivered through the Agentic Domain Systems engagement does not maintain itself. This practice is what ensures it keeps working, and keeps improving, long after deployment.
Customer story · Insurance
A specialty insurer stops silent drift in a claims agent six months post-launch
Escalation rates had crept up 14 points while the system answered in the same confident register. Benchmarking, governance and a real feedback loop turned a depreciating asset into a compounding one.
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