From Prototype to Production: Lessons Scaling an AI Interview Platform

Introduction: The Reality Gap

Building a working AI prototype is exciting. Making it production-ready for hundreds of concurrent users, enterprise clients, and mission-critical hiring decisions is an entirely different challenge. As a founder who bootstrapped an AI interview platform from concept to production, I've learned that the gap between "it works on my laptop" and "it reliably serves thousands of users" is filled with technical challenges that textbooks rarely cover.

This article shares practical lessons from scaling an AI-powered interview platform, focusing on the technical, operational, and business challenges unique to production AI systems in the HR technology space.

The Journey: From MVP to Production

Phase 1: The MVP (Months 0-3)

What We Built:

• Basic chatbot using OpenAI API
• Simple Flask web server
• SQLite database
• Deployed on a single AWS EC2 instance
• ~20 test users from personal network

What Worked:

• Proved the concept
• Got initial user feedback
• Validated market need
• Cost: ~$200/month

What Broke Immediately at Scale:

• Database locks with concurrent users
• API rate limits during peak usage
• Memory leaks crashed server after ~100 interviews
• No queue system for processing
• Zero monitoring or observability

Phase 2: Production-Ready Architecture (Months 4-8)

Moving from prototype to production required rebuilding nearly everything:

# Before (MVP)
@app.route('/interview', methods=['POST'])
def conduct_interview():
    response = openai.ChatCompletion.create(
        model="gpt-4",
        messages=[{"role": "user", "content": request.json['message']}]
    )
    db.execute("INSERT INTO responses VALUES (?)", (response,))
    return jsonify(response)

# After (Production)
@app.route('/interview', methods=['POST'])
@rate_limit(max_calls=100, period=60)
@authenticate_user
@validate_input
async def conduct_interview():
    """Production-ready interview endpoint"""
    try:
        # Validate and sanitize input
        message = sanitize_input(request.json.get('message'))

        # Add to processing queue
        task_id = await queue.enqueue(
            'process_interview_response',
            message=message,
            user_id=g.user_id,
            session_id=g.session_id,
            priority='high'
        )

        # Return immediately, process asynchronously
        return jsonify({
            'task_id': task_id,
            'status': 'processing'
        }), 202

    except Exception as e:
        logger.error(f"Interview error: {str(e)}", exc_info=True)
        metrics.increment('interview_errors')
        return jsonify({'error': 'Internal error'}), 500

Challenge 1: Handling Concurrent Users

The Problem

AI interview platforms have unique concurrency patterns:

• Burst traffic: Companies hire in waves, not steady streams
• Long-running operations: AI inference takes 2-5 seconds per response
• Stateful conversations: Each interview is a multi-turn dialogue
• Peak hours: Most interviews happen 9 AM - 5 PM in each timezone

When we hit 50 concurrent users, everything broke:

• Database connection pool exhausted
• LLM API rate limits exceeded
• Server CPU maxed out
• Response times jumped from 2s to 30s+

The Solution: Queue-Based Architecture

import redis
from rq import Queue, Worker
import asyncio

class InterviewProcessor:
    def __init__(self):
        self.redis_conn = redis.Redis(
            host='redis-cluster',
            port=6379,
            db=0,
            decode_responses=True
        )

        # Separate queues for different priorities
        self.high_priority = Queue('high', connection=self.redis_conn)
        self.normal_priority = Queue('normal', connection=self.redis_conn)
        self.low_priority = Queue('low', connection=self.redis_conn)

    def enqueue_interview_task(
        self,
        message: str,
        session_id: str,
        priority: str = 'normal'
    ) -> str:
        """Add interview processing task to queue"""

        queue = self.get_queue_by_priority(priority)

        job = queue.enqueue(
            'workers.process_interview_message',
            message=message,
            session_id=session_id,
            job_timeout='60s',
            result_ttl=3600,  # Keep results for 1 hour
            failure_ttl=86400  # Keep failures for debugging
        )

        return job.id

    def get_job_status(self, job_id: str) -> Dict:
        """Check status of queued job"""

        job = Job.fetch(job_id, connection=self.redis_conn)

        return {
            'status': job.get_status(),
            'result': job.result if job.is_finished else None,
            'error': str(job.exc_info) if job.is_failed else None,
            'position_in_queue': job.get_position() if job.is_queued else None
        }

Worker Process:

# workers.py
import openai
from rq import get_current_job

def process_interview_message(message: str, session_id: str) -> Dict:
    """Background worker for processing interview messages"""

    job = get_current_job()

    # Update job progress
    job.meta['stage'] = 'retrieving_context'
    job.save_meta()

    # Get conversation context
    context = retrieve_conversation_context(session_id)

    job.meta['stage'] = 'generating_response'
    job.save_meta()

    # Generate AI response
    response = generate_ai_response(message, context)

    job.meta['stage'] = 'saving_results'
    job.save_meta()

    # Save to database
    save_interview_response(session_id, message, response)

    job.meta['stage'] = 'completed'
    job.save_meta()

    return {
        'response': response,
        'session_id': session_id,
        'timestamp': datetime.now().isoformat()
    }

Results:

• Handled 500+ concurrent users without degradation
• Average response time: 2.3 seconds (down from 30s)
• Clear visibility into processing queue
• Graceful degradation under extreme load

Challenge 2: LLM API Management

The Cost Problem

OpenAI API costs scale quickly:

• GPT-4: $0.03 per 1K input tokens, $0.06 per 1K output tokens
• Average interview: 50 turns × 500 tokens/turn = 25K tokens
• Cost per interview: $0.75 - $1.50

At 1,000 interviews/day: $750 - $1,500/day = $22,500 - $45,000/month

For a bootstrapped startup, this was unsustainable.

Cost Optimization Strategies

1. Intelligent Model Selection:

class ModelSelector:
    def select_optimal_model(
        self,
        task_complexity: str,
        required_quality: str,
        context_length: int
    ) -> str:
        """Select cheapest model that meets requirements"""

        if task_complexity == 'simple' and context_length < 4000:
            # Acknowledgments, simple follow-ups
            return 'gpt-3.5-turbo'  # $0.0015/1K tokens

        elif task_complexity == 'medium' and required_quality == 'standard':
            # Most interview questions
            return 'gpt-3.5-turbo-16k'  # $0.003/1K tokens

        elif context_length > 8000 or required_quality == 'high':
            # Complex evaluations, long context
            return 'gpt-4-turbo-preview'  # $0.01/1K tokens

        else:
            return 'gpt-4'  # $0.03/1K tokens

Savings: 40% reduction by routing simple tasks to cheaper models

2. Aggressive Caching:

import hashlib
from functools import lru_cache

class ResponseCache:
    def __init__(self):
        self.redis_client = redis.Redis()
        self.ttl = 3600 * 24 * 7  # 1 week

    def get_cached_response(
        self,
        prompt: str,
        model: str,
        temperature: float
    ) -> Optional[str]:
        """Check if we have cached response for this prompt"""

        # Create cache key
        cache_key = self.create_cache_key(prompt, model, temperature)

        # Check cache
        cached = self.redis_client.get(cache_key)

        if cached:
            metrics.increment('cache_hits')
            return json.loads(cached)

        metrics.increment('cache_misses')
        return None

    def cache_response(
        self,
        prompt: str,
        model: str,
        temperature: float,
        response: str
    ):
        """Cache response for future use"""

        cache_key = self.create_cache_key(prompt, model, temperature)

        self.redis_client.setex(
            cache_key,
            self.ttl,
            json.dumps(response)
        )

    def create_cache_key(
        self,
        prompt: str,
        model: str,
        temperature: float
    ) -> str:
        """Generate deterministic cache key"""

        content = f"{prompt}|{model}|{temperature}"
        return f"llm_cache:{hashlib.sha256(content.encode()).hexdigest()}"

Savings: 35% reduction from cache hits on common questions

3. Prompt Optimization:

# Before: Verbose prompt (1,200 tokens)
prompt = f"""
You are an AI interviewer conducting an interview for the position 
of {job_title} at {company_name}. 

The job description is as follows:
{job_description}  # Often 500+ tokens

The candidate's background includes:
{candidate_background}  # Another 300+ tokens

Previous conversation:
{full_conversation_history}  # Could be 400+ tokens

Guidelines for interviewing:
{lengthy_guidelines}  # 200+ tokens

Now, given the candidate said: "{candidate_response}"

Generate the next interview question.
"""

# After: Optimized prompt (400 tokens)
prompt = f"""
Interview: {job_title}
Key requirements: {extract_key_requirements(job_description)}  # 100 tokens
Recent context: {summarize_recent_turns(history, n=3)}  # 150 tokens
Candidate said: "{candidate_response}"
Next question:
"""

Savings: 65% token reduction per API call

Combined Result: 70% cost reduction ($22.5K → $6.75K/month)

Challenge 3: Database Performance at Scale

The Problem

Our SQLite database worked fine for prototyping but couldn't handle production load:

• Write locks blocked concurrent interviews
• No query optimization
• No connection pooling
• Slow full-text search
• No backup/recovery strategy

Migration to Production Database

# Production database schema
from sqlalchemy import create_engine, Column, Integer, String, Text, DateTime, Float, Index
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker, relationship

Base = declarative_base()

class Interview(Base):
    __tablename__ = 'interviews'

    id = Column(Integer, primary_key=True)
    session_id = Column(String(64), unique=True, nullable=False, index=True)
    candidate_id = Column(Integer, ForeignKey('candidates.id'), index=True)
    job_id = Column(Integer, ForeignKey('jobs.id'), index=True)
    status = Column(String(20), index=True)  # active, completed, abandoned
    started_at = Column(DateTime, nullable=False, index=True)
    completed_at = Column(DateTime)
    total_duration = Column(Integer)  # seconds

    # Relationships
    messages = relationship('Message', back_populates='interview')
    evaluation = relationship('Evaluation', back_populates='interview', uselist=False)

    # Indexes for common queries
    __table_args__ = (
        Index('ix_interview_candidate_date', 'candidate_id', 'started_at'),
        Index('ix_interview_job_status', 'job_id', 'status'),
    )

class Message(Base):
    __tablename__ = 'messages'

    id = Column(Integer, primary_key=True)
    interview_id = Column(Integer, ForeignKey('interviews.id'), nullable=False, index=True)
    sender = Column(String(10), nullable=False)  # 'ai' or 'candidate'
    content = Column(Text, nullable=False)
    timestamp = Column(DateTime, nullable=False, index=True)

    # For analytics
    tokens_used = Column(Integer)
    latency_ms = Column(Integer)
    model_version = Column(String(50))

    interview = relationship('Interview', back_populates='messages')

class Evaluation(Base):
    __tablename__ = 'evaluations'

    id = Column(Integer, primary_key=True)
    interview_id = Column(Integer, ForeignKey('interviews.id'), nullable=False, unique=True)

    # Scores
    overall_score = Column(Float)
    technical_score = Column(Float)
    communication_score = Column(Float)
    cultural_fit_score = Column(Float)

    # Analysis
    strengths = Column(Text)
    weaknesses = Column(Text)
    recommendation = Column(String(20))  # hire, maybe, no_hire

    created_at = Column(DateTime, nullable=False)

    interview = relationship('Interview', back_populates='evaluation')

# Connection pooling
engine = create_engine(
    'postgresql://user:pass@host:5432/interviews',
    pool_size=20,  # Base pool size
    max_overflow=10,  # Additional connections if needed
    pool_pre_ping=True,  # Verify connections before using
    pool_recycle=3600,  # Recycle connections every hour
    echo=False  # Don't log SQL in production
)

SessionLocal = sessionmaker(bind=engine)

Query Optimization

from sqlalchemy.orm import joinedload, selectinload

class InterviewRepository:
    def get_active_interviews_with_messages(
        self,
        limit: int = 100
    ):
        """Efficiently load interviews with related data"""

        session = SessionLocal()

        try:
            # Use eager loading to prevent N+1 queries
            interviews = session.query(Interview)\
                .options(
                    selectinload(Interview.messages),
                    joinedload(Interview.evaluation)
                )\
                .filter(Interview.status == 'active')\
                .order_by(Interview.started_at.desc())\
                .limit(limit)\
                .all()

            return interviews
        finally:
            session.close()

    def get_interview_statistics(
        self,
        job_id: int,
        start_date: datetime,
        end_date: datetime
    ) -> Dict:
        """Optimized aggregation query"""

        session = SessionLocal()

        try:
            from sqlalchemy import func

            stats = session.query(
                func.count(Interview.id).label('total_interviews'),
                func.avg(Interview.total_duration).label('avg_duration'),
                func.avg(Evaluation.overall_score).label('avg_score')
            ).join(Evaluation)\
             .filter(
                 Interview.job_id == job_id,
                 Interview.started_at.between(start_date, end_date),
                 Interview.status == 'completed'
             )\             .first()

            return {
                'total_interviews': stats.total_interviews,
                'avg_duration_minutes': stats.avg_duration / 60 if stats.avg_duration else 0,
                'avg_score': round(stats.avg_score, 2) if stats.avg_score else 0
            }
        finally:
            session.close()

Read Replicas for Analytics

class DatabaseRouter:
    """Route queries to primary or read replica"""

    def __init__(self):
        self.primary = create_engine('postgresql://primary:5432/db')
        self.replica = create_engine('postgresql://replica:5432/db')

    def get_session(self, read_only: bool = False):
        """Get appropriate database session"""

        if read_only:
            return sessionmaker(bind=self.replica)()
        else:
            return sessionmaker(bind=self.primary)()

# Usage
def generate_analytics_report(job_id: int):
    """Heavy analytics on read replica"""

    session = db_router.get_session(read_only=True)

    # Complex analytics queries don't impact production writes
    results = session.query(...).all()

    return results

Challenge 4: Monitoring and Observability

What to Monitor in Production AI Systems

import prometheus_client as prom
from datadog import statsd

class MetricsCollector:
    def __init__(self):
        # Prometheus metrics
        self.interview_counter = prom.Counter(
            'interviews_total',
            'Total interviews conducted',
            ['status', 'job_type']
        )

        self.response_latency = prom.Histogram(
            'ai_response_latency_seconds',
            'Time to generate AI response',
            buckets=[0.5, 1.0, 2.0, 5.0, 10.0, 30.0]
        )

        self.llm_cost_gauge = prom.Gauge(
            'llm_cost_dollars_daily',
            'Daily LLM API costs'
        )

        self.active_interviews = prom.Gauge(
            'active_interviews',
            'Number of interviews in progress'
        )

        self.error_counter = prom.Counter(
            'interview_errors_total',
            'Total interview errors',
            ['error_type', 'component']
        )

    def track_interview_message(
        self,
        latency: float,
        tokens_used: int,
        model: str,
        success: bool
    ):
        """Track metrics for each message"""

        # Response time
        self.response_latency.observe(latency)

        # Cost estimation
        cost = self.estimate_cost(tokens_used, model)
        self.llm_cost_gauge.inc(cost)

        # Send to DataDog for dashboarding
        statsd.histogram('interview.latency', latency)
        statsd.increment('interview.messages', tags=[f'model:{model}'])

        if not success:
            self.error_counter.labels(
                error_type='generation_failed',
                component='llm'
            ).inc()

Logging Strategy

import structlog
import logging

# Structured logging
logger = structlog.get_logger()

class InterviewLogger:
    def log_interview_event(
        self,
        event_type: str,
        session_id: str,
        **kwargs
    ):
        """Structured logging for interview events"""

        logger.info(
            event_type,
            session_id=session_id,
            timestamp=datetime.now().isoformat(),
            **kwargs
        )

    def log_error_with_context(
        self,
        error: Exception,
        session_id: str,
        context: Dict
    ):
        """Log errors with full context for debugging"""

        logger.error(
            "interview_error",
            session_id=session_id,
            error_type=type(error).__name__,
            error_message=str(error),
            stack_trace=traceback.format_exc(),
            context=context,
            timestamp=datetime.now().isoformat()
        )

# Usage
interview_logger = InterviewLogger()

try:
    response = generate_ai_response(message, context)
    interview_logger.log_interview_event(
        'message_generated',
        session_id=session_id,
        message_length=len(message),
        response_length=len(response),
        latency=latency
    )
except Exception as e:
    interview_logger.log_error_with_context(
        error=e,
        session_id=session_id,
        context={
            'message': message,
            'user_id': user_id,
            'job_id': job_id
        }
    )
    raise

Alerting Rules

# alerting_rules.yml
groups:
  - name: interview_platform
    interval: 30s
    rules:
      # High error rate
      - alert: HighErrorRate
        expr: |
          rate(interview_errors_total[5m]) > 0.1
        annotations:
          summary: "High error rate detected"
          description: "Error rate is {{ $value }} errors/second"

      # Slow response times
      - alert: SlowResponses
        expr: |
          histogram_quantile(0.95, ai_response_latency_seconds) > 10
        annotations:
          summary: "95th percentile response time > 10s"

      # High API costs
      - alert: HighAPICosts
        expr: |
          llm_cost_dollars_daily > 500
        annotations:
          summary: "Daily API costs exceeding budget"

      # Queue backup
      - alert: QueueBacklog
        expr: |
          redis_queue_length{queue="high"} > 100
        annotations:
          summary: "High priority queue backed up"

Challenge 5: Security and Data Privacy

Interview Data is Sensitive

Candidates share personal information:

• Employment history
• Salary expectations
• Personal projects and achievements
• Sometimes protected characteristics (inadvertently)

Security Implementation

from cryptography.fernet import Fernet
import hashlib

class SecureInterviewStorage:
    def __init__(self):
        self.cipher = Fernet(os.getenv('ENCRYPTION_KEY'))

    def store_interview_data(
        self,
        session_id: str,
        message: str,
        candidate_id: int
    ):
        """Securely store interview data"""

        # Encrypt sensitive content
        encrypted_message = self.cipher.encrypt(message.encode())

        # Hash session ID for privacy
        hashed_session = hashlib.sha256(
            f"{session_id}{candidate_id}".encode()
        ).hexdigest()

        # Store with encryption
        db.execute(
            """
            INSERT INTO messages 
            (session_hash, encrypted_content, candidate_id, created_at)
            VALUES (?, ?, ?, ?)
            """,
            (hashed_session, encrypted_message, candidate_id, datetime.now())
        )

    def retrieve_interview_data(
        self,
        session_id: str,
        candidate_id: int,
        requesting_user_id: int
    ) -> List[str]:
        """Retrieve interview data with access control"""

        # Verify requesting user has permission
        if not self.verify_access_permission(requesting_user_id, candidate_id):
            raise PermissionError("Unauthorized access attempt")

        hashed_session = hashlib.sha256(
            f"{session_id}{candidate_id}".encode()
        ).hexdigest()

        encrypted_messages = db.query(
            "SELECT encrypted_content FROM messages WHERE session_hash = ?",
            (hashed_session,)
        )

        # Decrypt messages
        messages = [
            self.cipher.decrypt(enc_msg).decode()
            for enc_msg in encrypted_messages
        ]

        # Log access for audit trail
        self.log_data_access(requesting_user_id, candidate_id, session_id)

        return messages

    def anonymize_for_training(
        self,
        interview_data: List[Dict]
    ) -> List[Dict]:
        """Anonymize data before using for model training"""

        anonymized = []

        for interview in interview_data:
            # Remove PII
            clean_messages = [
                self.remove_pii(msg)
                for msg in interview['messages']
            ]

            anonymized.append({
                'messages': clean_messages,
                'job_category': interview['job_category'],  # Keep for context
                'outcome': interview['outcome']  # Keep for training
                # Removed: candidate_id, company_name, specific dates
            })

        return anonymized

GDPR Compliance

class GDPRComplianceHandler:
    def handle_deletion_request(self, candidate_id: int):
        """Handle right to be forgotten"""

        # 1. Delete from primary database
        db.execute(
            "DELETE FROM messages WHERE candidate_id = ?",
            (candidate_id,)
        )

        db.execute(
            "DELETE FROM interviews WHERE candidate_id = ?",
            (candidate_id,)
        )

        # 2. Delete from backups (mark for deletion)
        backup_manager.mark_for_deletion(candidate_id)

        # 3. Delete from analytics databases
        analytics_db.delete_candidate_data(candidate_id)

        # 4. Remove from any cached data
        cache.delete_pattern(f"candidate:{candidate_id}:*")

        # 5. Log the deletion (required for compliance)
        compliance_log.record_deletion(
            candidate_id=candidate_id,
            requested_at=datetime.now(),
            completed_at=datetime.now()
        )

    def export_candidate_data(self, candidate_id: int) -> Dict:
        """Handle data portability request"""

        return {
            'interviews': self.get_interview_history(candidate_id),
            'evaluations': self.get_evaluations(candidate_id),
            'messages': self.get_messages(candidate_id),
            'metadata': self.get_metadata(candidate_id)
        }

Challenge 6: Enterprise Requirements

Multi-Tenancy

Supporting multiple companies on one platform:

class MultiTenantArchitecture:
    def __init__(self):
        self.tenant_cache = {}

    def get_tenant_context(self, request) -> Dict:
        """Extract tenant from request"""

        # From subdomain: acme.platform.com
        subdomain = request.host.split('.')[0]

        # Or from API key
        api_key = request.headers.get('X-API-Key')

        if api_key:
            tenant = self.get_tenant_from_api_key(api_key)
        else:
            tenant = self.get_tenant_from_subdomain(subdomain)

        return tenant

    @cache(ttl=300)
    def get_tenant_config(self, tenant_id: str) -> Dict:
        """Get tenant-specific configuration"""

        config = db.query(
            "SELECT * FROM tenant_configs WHERE tenant_id = ?",
            (tenant_id,)
        )

        return {
            'branding': config['branding_settings'],
            'features': config['enabled_features'],
            'limits': config['usage_limits'],
            'llm_config': config['llm_settings']
        }

    def enforce_tenant_isolation(self, query, tenant_id: str):
        """Ensure queries only access tenant's data"""

        # Add tenant_id to all queries automatically
        return query.filter(tenant_id=tenant_id)

# Middleware
@app.before_request
def inject_tenant_context():
    """Add tenant context to all requests"""

    g.tenant = multi_tenant.get_tenant_context(request)
    g.tenant_config = multi_tenant.get_tenant_config(g.tenant['id'])

SSO Integration

from onelogin.saml2.auth import OneLogin_Saml2_Auth

class SSOHandler:
    def handle_saml_login(self, request):
        """Handle SAML SSO authentication"""

        auth = OneLogin_Saml2_Auth(request, saml_settings)

        auth.process_response()

        if auth.is_authenticated():
            user_data = {
                'email': auth.get_nameid(),
                'attributes': auth.get_attributes(),
                'session_index': auth.get_session_index()
            }

            # Create or update user
            user = self.sync_sso_user(user_data)

            # Create session
            session = self.create_authenticated_session(user)

            return redirect(url_for('dashboard'))
        else:
            return "Authentication failed", 401

Challenge 7: Cost Management at Scale

Final Cost Breakdown (Production)

Monthly Costs for 10,000 interviews:

Infrastructure:
- AWS EC2 (application servers): $500
- AWS RDS (PostgreSQL): $300
- Redis Cache: $100
- Load Balancer: $50
- CloudFront CDN: $100

AI/ML:
- OpenAI API (after optimization): $6,750
- Whisper API (voice): $500

Monitoring & Tools:
- DataDog: $200
- Sentry error tracking: $50
- StatusPage: $50

Total: $8,600/month
Cost per interview: $0.86

Revenue Model

Pricing Tiers:
- Starter: $99/mo (100 interviews)
- Professional: $499/mo (1,000 interviews)
- Enterprise: $2,499/mo (10,000 interviews) + custom features

At 100 enterprise customers:
Revenue: $249,900/month
Costs: $8,600/month
Gross Margin: 97%

Lessons Learned

1. Start Simple, But Plan for Scale

Don't over-engineer the MVP, but understand what will break at scale:

• Use PostgreSQL from day 1 (not SQLite)
• Design for horizontal scaling
• Implement queues early
• Add monitoring before you need it

2. Optimize Costs Aggressively

AI API costs can spiral out of control:

• Cache everything possible
• Use cheaper models for simple tasks
• Optimize prompts ruthlessly
• Monitor costs daily

3. Security Can't Be an Afterthought

Interview data is sensitive:

• Encrypt at rest and in transit
• Implement access controls from start
• Plan for GDPR/privacy compliance
• Regular security audits

4. Observability is Critical

You can't fix what you can't see:

• Comprehensive logging
• Real-time metrics
• Alerting on key thresholds
• Error tracking with context

5. Enterprise Customers Have Unique Needs

Plan for:

• SSO integration
• Multi-tenancy
• Custom branding
• Compliance requirements (SOC 2, GDPR)
• SLAs and support expectations

Conclusion

Scaling an AI platform from prototype to production serving thousands of users requires solving challenges across infrastructure, cost optimization, security, and enterprise requirements. The technical decisions made early significantly impact long-term scalability and unit economics.

Key takeaways:

• Architecture matters: Queue-based processing handles concurrency gracefully
• Cost optimization is essential: 70% cost reduction possible through caching, model selection, and prompt engineering
• Monitoring enables scale: Can't optimize what you don't measure
• Security first: Privacy and compliance must be built in, not bolted on
• Enterprise-ready takes time: SSO, multi-tenancy, and compliance aren't trivial additions

Building production AI systems is hard. But with careful planning, aggressive optimization, and commitment to reliability, it's possible to build scalable, profitable AI products that serve real business needs.

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About the Author
Ademola Balogun is the founder and CEO of 180GIG Ltd, where he bootstrapped and scaled an AI interview platform from concept to production. With an MSc in Data Science from Birkbeck, University of London, he combines technical depth with entrepreneurial execution. His work focuses on building practical, scalable AI systems that solve real business problems while maintaining profitability and reliability.