AI Embedded Into Every Business Process
LLMs and agents wired directly into the CRM, telephony, ITSM, and back-office systems you already run — predictive lead scoring, automated workflows, intelligent decision engines, AI fraud detection. Workflows that compound, not demos that don't.
The Approach
Most AI projects fail at the operating model, not the model. A pilot impresses leadership, the team can't scale it past one workflow, the data is never quite clean, and the system goes quietly into a graveyard of POCs. Meanwhile the cost of inaction compounds — every untouched repetitive task is a small annuity paid to inefficiency.
My approach treats AI as integration work. The intelligent layer sits where the operational data already lives — CRM, telephony, ticketing, back-office — and the agents and models earn their place by removing measurable work, not by impressing a demo audience. LLMs do call QA, predictive models score leads against real outcomes, automation closes loops on tickets, and a fraud engine watches the flows that matter.
The result, repeatedly, is 30–60% productivity gain on the targeted workflows, measurable in hours saved per week and conversion uplift per cohort. The AI stops being a project. It starts being part of how the business runs.
Capabilities In Scope
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LLM-Powered Workflow Agents
Agents wired into CRM, ticketing, telephony, and back-office. They retire repetitive work — not just suggest it.
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Predictive Lead Scoring
Models trained on call and CRM data to rank leads by genuine conversion likelihood — agents work the queue worth working.
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AI Fraud Detection
Anomaly detection on transactions, identities, and call flows. Patterns flagged in minutes — investigators get a ranked queue.
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Decision Engines
Rule-and-model hybrids that price, approve, route, or escalate — with auditable reasoning and a human-in-the-loop where it matters.
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Workflow Automation
PowerShell, Python, and AI APIs orchestrated end-to-end. The boring 70% of every process replaced, the 30% that needs judgement preserved.
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CRM / ITSM / Voice Integrations
EspoCRM, Squaretalk, ServiceNow, M365 — meeting the platforms where they live, not migrating around them.
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Conversational Interfaces
Internal chatbots and copilots over the documents and systems your team actually uses — grounded in real data, not the open internet.
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AI Governance & Safety
Prompt hygiene, eval sets, drift monitoring, and the policy framework that keeps the AI defensible to regulators and the board.
Engagement Roadmap
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01
Workflow Discovery
Inventory the repetitive work. Quantify where time goes, where errors creep in, where decisions are made on partial data.
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02
Use-Case Selection
Choose targets where the value is large, the data is workable, and the operational risk is low enough to ship in weeks.
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03
Data & Eval Sets
Curate the training and evaluation data. Define the metric the model is actually optimizing against — before anything is trained.
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04
Build & Integrate
Wire the model into the CRM, telephony, or ITSM where it lives. Production integration, not a notebook on a laptop.
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05
Pilot & Measure
Run alongside the existing workflow long enough to prove the lift in real numbers — not in a benchmark suite.
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06
Scale & Govern
Roll out across the estate with monitoring, drift detection, and a governance framework that survives leadership turnover.
Measurable Impact
Numbers from real engagements in this domain.
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Productivity Gain
Average measured uplift across automated workflows in the targeted teams.
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Lead Conversion Lift
After predictive scoring re-orders the agent queue against real conversion data.
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Operational Cost Cut
Reduction on the workflows AI absorbed — measured by hours saved per week.
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Calls AI-Scored
In a single deployment — transcribed, QA-scored, and routed for human review.
Stack & Tooling
- Python
- OpenAI
- Anthropic
- LangChain
- PostgreSQL
- pgvector
- EspoCRM
- Squaretalk
- REST APIs
- Webhooks
- PowerShell
- Docker
Let's Talk AI Integration
Tell me the constraint, the timeline, and the outcome. I respond personally within 24 hours.