Anyone can prompt ChatGPT until something "works." But integrating AI into real systems—with real data, real users, and real stakes—requires deep understanding of your stack, your data structures, and where things actually break.
We're here to solve problems. Sometimes that means AI. Sometimes it means a better spreadsheet. We'll tell you the truth about what AI can and can't do for your specific situation—before you spend a dime.
The gap between a demo and production isn't code—it's understanding how your entire system works together.
Prompts until it compiles. Ships and prays.
Understands the stack. Builds for scale.
The difference? We've spent years building and fixing integrations, data pipelines, and production systems. We know where AI fits—and more importantly, where it doesn't.
The difference between a prompt that "works" and one that builds a real system isn't wordcount—it's understanding what can go wrong.
Why it matters: The vibe prompt creates a liability. The expert prompt creates a system with business logic, safety rails, and accountability built in.
Why it matters: The vibe prompt optimizes for 'engagement' which often hurts conversion. The expert prompt optimizes for revenue with measurable outcomes.
Why it matters: The vibe prompt destroys list health. The expert prompt protects deliverability while systematically improving performance.
Why it matters: The vibe prompt gives you a number you can't trust. The expert prompt gives you a decision-support system with uncertainty quantification.
The pattern? Expert prompts encode failure modes, business constraints, and operational reality. Vibe prompts hope for the best.
We've seen these patterns destroy budgets and timelines. Learn from others' expensive mistakes.
AI that runs in a notebook doesn't mean it runs in production. Real systems have latency requirements, rate limits, concurrent users, and data that doesn't look like your test set.
Reality check: We've seen teams spend 6 months on an AI feature that couldn't handle 10 concurrent requests.
AI is only as good as the data it sees. Most companies don't realize their data is messy, inconsistent, or incomplete until the AI starts hallucinating.
Reality check: 80% of AI project time should be spent on data—most teams spend 80% on the model.
Getting AI to talk to your CRM, your inventory system, and your support desk is harder than building the AI itself. Every integration is a potential point of failure.
Reality check: We've rescued projects where the AI worked perfectly—but couldn't actually connect to anything.
API calls add up. Without proper caching, batching, and model selection, your AI feature can cost more than the revenue it generates.
Reality check: One client was burning $12k/month on API calls for a feature that could've cost $800 with proper architecture.
Real cases where vibe-coded AI passed the demo—then failed in ways nobody anticipated.
E-commerce brand launched an AI customer service bot. Demo looked perfect—friendly, helpful, on-brand.
No guardrails. Bot started promising refunds, discounts, and free shipping to anyone who asked nicely. No validation against actual policies.
$47k in unauthorized refunds before anyone noticed. 3 weeks to fix. Brand reputation hit.
Lesson: AI without business logic constraints is just an expensive liability.
DTC brand added AI product recommendations. Initial A/B test showed 'promising engagement.'
Model was trained on browsing data, not purchase data. Recommended products people looked at but never bought. Pushed high-margin items nobody wanted.
Conversion dropped 23% over 6 weeks. Revenue loss estimated at $180k before they killed the feature.
Lesson: Training data determines behavior. Wrong data = confidently wrong recommendations.
Implemented AI demand forecasting. Looked great in the pitch deck with fancy charts.
No handling for seasonality, promotions, or supply chain delays. Model couldn't distinguish between 'out of stock' and 'low demand.'
Overstocked $340k in slow-moving inventory. Stockouts on bestsellers during Black Friday.
Lesson: AI can't understand context it was never given. Domain expertise isn't optional.
Marketing team implemented AI-powered email personalization. Open rates initially improved.
AI started combining data incorrectly. Sent 'We miss you!' emails to active customers. Recommended baby products to customers who bought a gift once.
Unsubscribe rate spiked 340%. Complaints flooded support. Email list health destroyed.
Lesson: Personalization without data hygiene is just automated embarrassment.
Competitor monitoring + dynamic pricing AI. Promised to 'always stay competitive.'
No floor prices set. Competitor had same tool. Both AIs kept undercutting each other in an automated death spiral.
Margins dropped 67% on top SKUs overnight. Took 48 hours to notice. Lost $89k in margin.
Lesson: Automation without constraints amplifies mistakes at machine speed.
AI tool to summarize support tickets for the team. Worked great in testing.
No PII filtering. AI included customer credit card digits, addresses, and health info in summaries visible to all agents.
Compliance violation. Emergency audit. Legal costs exceeded $200k. Nearly lost enterprise contracts.
Lesson: AI doesn't understand privacy. You have to build that understanding into the system.
AI lead scoring to prioritize sales outreach. Sales team was excited to 'focus on hot leads.'
Model trained on closed-won deals only. Learned to score leads that looked like past wins—which were all from one industry. Deprioritized diversification efforts.
Pipeline dried up in new verticals. Missed $400k in expansion revenue. Sales team blamed marketing.
Lesson: Historical data encodes historical biases. AI will optimize for your past, not your future.
AI-generated product descriptions and blog posts. Content team loved the speed.
No plagiarism checking. AI reproduced competitor copy nearly verbatim. Also made up product claims that weren't true.
Cease and desist from competitor. FTC inquiry for false advertising. $150k in legal fees.
Lesson: AI-generated content needs human review. 'Fast' isn't worth 'lawsuit.'
Every one of these could have been prevented with proper architecture, guardrails, and someone who's seen these failure modes before.
The gap between "it works" and "it works in production" is measured in years of hard-won knowledge.
| Area | Novice Approach | Expert Approach |
|---|---|---|
| System Architecture | Builds AI as an isolated feature | Designs AI as part of your entire data ecosystem |
| Data Pipeline Design | Feeds raw data directly to the model | Builds preprocessing, validation, and transformation layers |
| Error Handling | Hopes the API doesn't fail | Implements graceful degradation, fallbacks, and retry logic |
| Cost Management | Discovers costs after the bill arrives | Builds with token budgets, caching, and model tiering from day one |
| Security & Compliance | Sends customer data to any API | Understands PII handling, data residency, and audit requirements |
| Monitoring & Debugging | No visibility into what the AI is doing | Builds observability, logging, and performance tracking |
From your perspective—what actually changes when you have experienced AI partners.
Every AI project has landmines. We've already stepped on them—so you don't have to waste months and budget discovering what doesn't work.
No 'MVP' that needs to be rebuilt. We design for scale, security, and maintainability from the start.
We'll tell you honestly if AI will pay for itself—and if not, what alternatives actually make sense.
AI systems need tuning, monitoring, and updates. We don't disappear after launch.
We document everything and train your team. When we're done, you own the system—not just the invoice.
AI implementations that survive contact with reality.
Cut through the AI hype. We identify where AI actually moves the needle for your business—not where it's just expensive novelty.
Purpose-built agents that handle real work: customer support, data processing, content generation. Not chatbots that frustrate users.
Connect AI to your existing workflows. Automate the tedious, error-prone tasks your team dreads.
Turn your data into actionable forecasts. Inventory, demand, churn, LTV—models that actually inform decisions.
Seamlessly integrate AI capabilities into your existing stack. OpenAI, Anthropic, custom models—we make them work together.
Guardrails, monitoring, and governance baked in. AI that's reliable, explainable, and doesn't embarrass your brand.
Clear options. Real ROI. No mystery pricing.
From $10k
Best when: We want AI, but we're not sure what's real vs hype.
From $15k/mo
Project-based
Best when: We need one real AI agent in production that doesn't embarrass us.
From $8.5k
Best when: AI works… but it's expensive, flaky, or not connected.
$5k–$12.5k/mo ongoing
We launched AI and need it maintained like any critical service. Monitoring, updates, incident response, monthly ROI reviews.
Starting at $15k
Your team is using AI already — you need standards, not chaos. Audit, playbook, code review guidelines, live training.
Give a kid a hammer—they'll build a crooked birdhouse and call it done. Give a master carpenter the same hammer—they'll build a structure that lasts generations. AI is just a hammer. The difference is who's swinging it.
Same tools, different understanding
Result: A birdhouse that falls apart in the first storm.
Same tools, decades of context
Result: A skyscraper that stands for decades.
Both teams using the same AI tools. The delta is expertise.
Loading chart...
The uncomfortable truth: AI amplifies whatever you already have. If you have deep systems knowledge, AI makes you dangerous. If you don't, AI just helps you create bugs faster.
Your developers are using AI whether you like it or not. The question is: are they using it in ways that help—or ways that will cost you later? We help you establish the systems and standards that turn AI from a liability into a force multiplier.
We train your team to understand AI capabilities and limitations—so they stop treating it like magic and start treating it like a tool.
Teach your developers to write prompts that account for edge cases, guardrails, and business logic—not just prompts that 'look right.'
Establish team-wide standards for when and how to use AI in your development workflow. Stop the chaos of everyone doing it differently.
Train senior engineers to spot the telltale signs of vibe-coded AI output—and catch the bugs before they hit production.
Help leadership understand what AI is actually saving (or costing) in terms of velocity, quality, and technical debt.
Configure AI assistants, IDE integrations, and code review tools specifically for your stack and coding standards.
We assess your current AI usage, identify risks and opportunities, and deliver a customized training program that turns your team into responsible AI power users.
Starting at
$15k
for teams up to 10 developers
Real applications. Real results. No sci-fi fantasies.
Book a call. We'll assess your situation and tell you honestly whether AI makes sense.