How do you handle AI accuracy or hallucination risk?

Guardrails, human-in-the-loop review points, and testing against real failure cases before launch, not after.

What if the AI feature doesn't work the way we imagined?

Covered by the same Zero-Risk Launch Guarantee as everything else we build.

Do you build the AI model, or integrate existing ones?

Integration-focused: connecting proven models (OpenAI, Claude, Llama) to your product, not training foundation models from scratch.

Most AI features never earn back their build cost.

AI Products & Systems

AI workflows and agents scoped around what actually drives revenue, not features that just look impressive in a demo.

50+ Shipped

10+ Years

Zero-Risk Launch Guarantee™

The problem

The AI feature is live. Nobody's using it.

Most "AI failures" we get called in on aren't technical. They're the same four mistakes, in this order:

You shipped an AI feature nobody uses

Usage graphs flatline after week one. The "wow" in the demo never became a habit.

The AI is right 95% of the time: users remember the 5%

One bad answer in front of a customer erodes more trust than ten good ones build.

Nobody can explain the ROI in one sentence

If the business case needs a slide deck to justify, it's a feature, not a filter.

Your data isn't ready, but the roadmap says Q2

RAG on messy, unstructured docs returns messy, unstructured answers.

6 feature requests this quarter

ChatbotAuto-summarizeSmart searchAI onboardingVoice assistantDoc Q&A

↓ ROI Filter: one sentence, in $ or hours saved ↓

Shipped (2)

Smart search

Doc Q&A

Skipped (4)

Chatbot

Auto-summarize

AI onboarding

Voice assistant

Your guide

10+ years building software before "AI" meant more than autocomplete. We've integrated OpenAI, Claude, and Llama into production systems that actually get used, and turned down more AI feature requests than we've built, because most fail the ROI test before they reach code.

The AI Build/Skip Checklist

Before we write a line of AI code, we run every idea through this. Most "AI features" fail this checklist, and that's the point.

Does this replace a task someone does more than 10x a week, or is it a novelty?
Can you explain the ROI in one sentence, in dollars or hours saved?
Will it still be useful if the AI is wrong 1 in 20 times?
Is there a non-AI version that's 80% as good and ships in half the time?
Do you have the data to make this AI actually accurate, or are you hoping it'll figure it out?
If this fails silently, does it cost you a customer, or just an afternoon?

The plan

How we actually do it.

Filter

Every idea run through the Build/Skip checklist before a design file gets opened.

Prototype the risky part first

The failure mode gets tested in isolation (hallucination, latency, cost) before anything gets built around it.

Ship with guardrails

Human-in-the-loop checkpoints and fallback paths for when the model gets it wrong, because it will.

Watch usage, not launch day

Real adoption tracked past week one; features that don't get used get cut, not defended.

What's Included

Where AI pays for itself

Every feature gets scoped against real usage first: 10x/week tasks, measurable ROI in hours or dollars saved. If it fails that bar, we don't build it, no matter how good the demo looks.

Agents that don't need babysitting

Guardrails and human-in-the-loop checkpoints tested against real failure cases before an agent ever talks to a live customer, not after.

Your data, answering in your voice

RAG built on your own docs, tickets, and CRM, so answers are grounded in what's actually true for your business. Stays yours. No vendor lock-in.

50+

Products Shipped

10+

Years Experience

“Witsberry didn't just build something that worked; Rikaz told me what features not to build, which saved me weeks of wasted development. Conversions jumped 60%+, we hit 18% monthly growth — a team that actually thinks before they code.”

- Syan, CEO of FS Analytics

If nothing changes

What happens if you skip the filter

Most AI features we're asked to fix later didn't fail technically. They failed the ROI test on day one and got built anyway. Six months later: a maintenance burden nobody uses, an API bill nobody budgeted for, and a roadmap slot that could've shipped something that mattered.

Free Tool

Startup Runway Calculator

Not sure an AI feature is worth the build time? Run the numbers on your runway first.

Try it free

Common Questions

Almost never a custom model. API integration ships faster in nearly every case, and we'll tell you upfront if your product is the rare exception that actually needs one.

Other ways we help

SaaS Applications

Multi-tenant SaaS platforms built to scale from day one, architected for the users you have, not a hypothetical roadmap.

Learn more

Mobile Apps (iOS + Android)

Production-ready iOS and Android apps built for retention, not just a listing in the App Store.

Learn more

Product Websites & Landing Systems

Conversion-first websites designed to validate and sell, not just 'exist'.

Learn more

Ready to turn your vision into Revenue?

Let's build your market-ready product. No jargon, no fluff, just execution.

Book a Free CTO Risk Audit

The AI feature is live. Nobody's using it.

Most "AI failures" we get called in on aren't technical. They're the same four mistakes, in this order:

You shipped an AI feature nobody uses

Usage graphs flatline after week one. The "wow" in the demo never became a habit.

The AI is right 95% of the time: users remember the 5%

One bad answer in front of a customer erodes more trust than ten good ones build.

Nobody can explain the ROI in one sentence

If the business case needs a slide deck to justify, it's a feature, not a filter.

Your data isn't ready, but the roadmap says Q2

RAG on messy, unstructured docs returns messy, unstructured answers.

The AI Build/Skip Checklist

Before we write a line of AI code, we run every idea through this. Most "AI features" fail this checklist, and that's the point.

Does this replace a task someone does more than 10x a week, or is it a novelty?

Can you explain the ROI in one sentence, in dollars or hours saved?

Will it still be useful if the AI is wrong 1 in 20 times?

Is there a non-AI version that's 80% as good and ships in half the time?

Do you have the data to make this AI actually accurate, or are you hoping it'll figure it out?

If this fails silently, does it cost you a customer, or just an afternoon?

What's Included

Where AI pays for itself

Every feature gets scoped against real usage first: 10x/week tasks, measurable ROI in hours or dollars saved. If it fails that bar, we don't build it, no matter how good the demo looks.

Agents that don't need babysitting

Guardrails and human-in-the-loop checkpoints tested against real failure cases before an agent ever talks to a live customer, not after.

Your data, answering in your voice

RAG built on your own docs, tickets, and CRM, so answers are grounded in what's actually true for your business. Stays yours. No vendor lock-in.

Common Questions

Almost never a custom model. API integration ships faster in nearly every case, and we'll tell you upfront if your product is the rare exception that actually needs one.