AI & Intelligent Automation
AI that pays for itself — not another proof of concept.
Stratifi’s AI practice is run by practitioners who deploy intelligent automation in live, multi-location businesses. We find the use cases worth funding, build them on your data, and measure the return.
What we deliver
From AI strategy to agents in production.
AI strategy & roadmapping
A prioritized, costed plan that ties every AI initiative to a business outcome.
- AI readiness assessment
- Use case scoring by value and effort
- Governance, risk and acceptable-use policy
AI agent development
Custom agents that answer questions, complete tasks and work inside the tools your teams already use.
- HR, IT and operations support agents
- Integration with Microsoft 365, help desk and line-of-business systems
- Adversarial and safety testing before launch
Innovation task force
We stand up and run a cross-functional team that turns ideas into delivered, measured AI projects.
- Intake and prioritization process
- Executive sponsorship model
- Quarterly value reporting
Data & warehouse strategy
The data foundation for reliable reporting and AI: architecture, pipelines and data quality.
- Data warehouse design and build
- Consistent metric definitions
- Integration of practice, ERP and CRM data
Reporting & analytics
Dashboards leaders actually use, built on numbers everyone agrees on.
- Power BI dashboards and data models
- KPI frameworks by role and location
- Predictive and trend analysis
Executive AI advisory
A trusted advisor for your leadership team and board as AI reshapes your industry.
- Board and leadership briefings
- Vendor and platform evaluation
- AI investment reviews
Agentic development
How we design and deploy AI agents.
We build agents that do real work inside real businesses — grounded in your own knowledge, bounded by rules you set, and measured against outcomes you care about.
- Model the work
Start with the workflow, not the model
We map the task end to end, decide what the agent should handle and what stays with a person, and agree on how success is measured.
- Ground it
Answers from your approved knowledge
Agents draw on your documents, systems and rules — so responses are sourced and current, not invented.
- Guardrails
Permissions, escalation, adversarial testing
Access controls, a clear hand-off to a human, and testing against misuse before anything goes live.
- Fit in
Where your teams already work
Deployed into the tools people use every day, integrated through secure, supported connections.
- Measure
Prove the value, then extend
Track resolution rates, time saved and accuracy after launch, and expand the agent's scope only where the numbers justify it.
Platforms
Platform-neutral. Enterprise-ready.
We recommend the platform that fits your data, security requirements and existing investments — not the one we happen to resell.
- Claude / Anthropic
- OpenAI GPT
- Microsoft Copilot
- Azure AI
- Power BI
- SharePoint
- Python
FAQ
Questions we hear often
Where should a mid-sized company start with AI?
Start with a short list of high-volume, rules-heavy tasks where you already have data — employee and customer questions, reporting, document handling, scheduling. We score each use case by value and effort, then build the top one or two first so you see a return before you scale.
What is an AI agent?
An AI agent is software that uses a large language model to understand a request, look up information in your systems, and take action — answering an HR policy question, drafting a report or routing a ticket. Unlike a simple chatbot, an agent can reason through multi-step tasks with guardrails you define.
Is our data safe when we use AI?
We design AI solutions around your security and privacy requirements, using enterprise platforms such as Azure AI and Microsoft Copilot where data stays within your tenant. Every deployment includes access controls, testing against misuse, and clear human oversight.
Which AI platforms do you work with?
Claude (Anthropic), OpenAI GPT models, Microsoft Copilot, Azure AI and Power BI. We are platform-neutral and recommend what fits your environment, budget and risk profile.
Do we need a data warehouse before we can use AI?
Not always. Many AI agents work directly with documents and existing systems. But if you want trustworthy analytics or predictive insights across locations, a well-designed data warehouse is the foundation — and we can build both in parallel.
Next step
Find the AI use cases worth funding.
In a 30-minute consultation we’ll talk through your processes and data, and point to where AI can create measurable value first.