Tagieff.ai
AI CONSULTING // SYSTEMS THAT SHIP

AI Consulting Services Ottawa - BUILD AI
THAT WORKS.

Practical AI systems for real business problems. Not demos. Not prototypes. Production systems that handle the boring work so your team can focus on what matters.

Workflow Automation

AI that handles repetitive tasks. Your team focuses on judgment calls.

Custom Agents

Purpose-built agents for your specific problems. Not generic chatbots.

System Integration

AI that works with your existing tools. Not another silo.

APPROACH // HOW I BUILD

AI SHOULD
EMPOWER TEAMS.

Good AI is boring. Because it works every time. It handles the repetitive stuff so humans can do what humans do best: make judgment calls, build relationships, and solve novel problems.

Start Small

Pick one workflow. Automate it well. Prove the value before scaling.

Ship Fast

Working prototypes in days, not months. Iterate based on real usage.

Build to Last

Production-grade systems. Monitoring. Error handling. The boring stuff that matters.

THE FUNDAMENTALS // HOW AI AGENTS ACTUALLY WORK

THREE PILLARS OF
USEFUL AI

AI agents aren't magic. They're systems with three components. Understanding these helps you evaluate what AI can realistically do.

AI Agent
Requires all three pillars

Clear, specific directions that tell the AI how to approach a task. Not magic prompts. Just well-structured guidance.

How I Work With You

I translate your expertise into AI instructions

Great instructions are built from domain-specific knowledge. How your team actually executes tasks. I work with you to extract that knowledge and translate it into precise, effective instructions for your AI agent.

In Practice

When you receive a customer email, classify it as support, sales, or billing. For support requests, check if there's an existing ticket.

Effective

"Identify the prospect's main objection, timeline, and competitors. Output as JSON."

Vague

"Analyze this and tell me what's important."

What You Need
1Specific inputs & outputs
2Edge case handling
3Example responses
4Format requirements

Good instructions are like good documentation. If you can't explain it to a person, you can't explain it to AI.

Which pillar is weakest in your workflows?That's where we start.
Let's Figure It Out
THE DIFFERENCE // WHY MY AI ACTUALLY WORKS

EVALS ARE
EVERYTHING.

Most AI projects ship once and hope for the best. I run structured evaluations, collect real feedback, and iterate until the outputs are genuinely useful. Here's what that actually looks like.

01
Deploy
Ship initial version to real users
02
Measure
Track adoption, accuracy, edge cases
03
Collect
Gather feedback on failures
04
Iterate
Update prompt, repeat until good
Real Example: Lead Classification Agent
v1
Initial Prompt
12% adoption rate
Prompt
Classify this lead as hot, warm, or cold.
Output
Classification: WARM
Issue Found

No reasoning. No actionable next steps. Sales team ignored it.

v2
After first eval round
34% adoption rate
Prompt
Classify this lead and explain the signals you detected. Suggest a follow-up action.
Output
Classification: WARM
Signals: Mentioned budget, no timeline.
Action: Send case study.
Issue Found

Better, but "warm" was too vague. Team still had to interpret.

v3
After feedback integration
89% adoption rate
Production
Prompt
Classify this lead using our 5-tier system (COLD/NURTURE/WARM/HOT/URGENT). Extract: timeline, budget signals, buying stage, objections. Output recommended action with priority level.
Output
Classification: HOT (Tier 4)

Signals:
• Timeline: Q2 (explicit)
• Budget: $50k range (disclosed)
• Stage: Comparing options
• Objections: None detected

Action: Priority outreach within 4hrs
Priority: HIGH
Result

89% adoption rate. Sales team now relies on it daily. Average response time to hot leads dropped from 2 hours to 12 minutes.

What I Deliver

Every AI system comes with an eval framework

Baseline metrics before deployment
Structured feedback collection
Version-controlled prompt history
Clear success criteria we agree on upfront
Iteration cycles until metrics hit targets
Documentation of what worked and why
COMMAND CENTER // DEPLOYED INFRASTRUCTURE

AGENTS
IN PRODUCTION.

Purpose-built AI agents handling real work, right now. Select a unit to see what it does and how I can build one for you.

TOTAL UNITS04
SYSTEM STATUS
OPERATIONAL
AVG COST/RUN$0.02

Market Intelligence Unit

Marketing Assistant

ACTIVE
TOTAL RUNS
1,847
AVG RUNTIME
2.3 min
ERROR RATE
0.8%
CAPABILITIES
  • Ingests sales data to identify segments
  • Scrapes G2, Capterra, & TrustRadius
  • Cross-references ZoomInfo data
  • Builds comprehensive ICP reports
TECH STACK
[ANTHROPIC API][RELEVANCE AI][N8N][APIFY][GOOGLE WORKSPACE]
How I Build This For You

I build the intelligence layer your sales team needs

Your market data is scattered across platforms. I connect the sources, build the pipelines, and deploy agents that surface actionable insights automatically.

Want an agent built for your workflow?

Tell me the problem. I'll tell you if an agent can solve it.

Start a Conversation
PROOF // AI SYSTEMS IN PRODUCTION

Real
Results

These aren't demos. These are production systems handling real work for real businesses.

AVG TIME TO VALUE: 6 WEEKS
AVG ROI: >500%
Marketing Services

ENTERPRISE AGENCY

Workflow Automation • Custom Agents • System Integration

$90K/MO SAVED
15 Agents Deployed

Built an internal AI agent platform that automated reporting, data entry, and analysis across 50+ enterprise accounts. Replaced manual processes that were eating 200+ hours per month.

The Problem

Manual reporting across 50+ clients, data copying between tools, reactive insights instead of proactive alerts

The Solution

Custom agent platform with 15 autonomous agents handling different workflows, BigQuery integration, real-time dashboards

Time to production: 4 months
Key Results
$90K
Monthly Savings
15
Agents Deployed
$1.08M
Annual Impact
Data Operations

TAM VERIFICATION

Data Automation • API Integration • Scale Operations

99.9% COST REDUCTION
500x Volume Increase

Automated the verification of Total Addressable Market data. Replaced manual analyst research with an AI system that validates company data at scale.

The Problem

$11/hour manual research, 100 records/day capacity, human error and inconsistency

The Solution

Python automation with SerpAPI integration, structured data extraction, validation pipelines

Time to production: 3 weeks
Key Results
$0.01
Cost Per Record
50K
Records/Day
99.9%
Accuracy
Content Operations

CONTENT SYSTEM

Content Automation • API Integration • Workflow Design

10X VELOCITY
83% Time Savings

End-to-end automation of the content brief process. AI handles topic research, keyword clustering, and competitor gap analysis. Work that used to take 2-3 hours per brief.

The Problem

2-3 hours per content brief, inconsistent research depth, manual keyword mapping

The Solution

OpenAI + Ahrefs API integration, automated brief generation, standardized output templates

Time to production: 6 weeks
Key Results
20 min
Per Brief
83%
Time Saved
Briefs/Month
Customer Success

CLIENT HEALTH SCORE

NLP • System Integration • Custom Scoring

CHURN PREDICTION
Real-time Alerts

A custom scoring engine that ingests unstructured data from Gong calls and Slack channels to predict client health and churn risk. Turned gut feelings into data.

The Problem

Lagging churn indicators, subjective account health assessments, surprise cancellations

The Solution

NLP pipeline for sentiment analysis, Gong + Slack API integration, proactive risk scoring

Time to production: 8 weeks
Key Results
Real-time
Sentiment
Proactive
Risk Alerts
Data-driven
Retention

Note: Company names anonymized for confidentiality. These systems are in production today. Full details available for serious conversations.

READY TO BUILD
SOMETHING REAL?

Tell me about your workflow. I'll tell you if AI can help, and how.

Let's Build