DeepRails: stop AI hallucinations from embarrassing your business

DeepRails detects AI hallucinations in real time and automatically fixes them so that only accurate answers reach your customers. For small businesses that use chatbots, automated support, or AI-generated responses, DeepRails acts like a safety rail: it watches the AI, catches nonsense, and swaps it for facts. If you run a small shop, a support team of one or two, or a growing SaaS product that leans on automation, DeepRails is built to help you keep answers honest and customers happy.

Why small businesses should care

When your AI makes up facts, your customers notice. Wrong product info, bogus dates, and flaky instructions lead to confused customers, extra tickets, and damage to your reputation. Small teams can’t afford that extra churn. DeepRails lowers the risk by checking AI output in real time and correcting it before a customer ever sees it. That saves time, reduces refunds, and keeps your brand looking smart.

1) Improve customer service accuracy with reliable AI responses

If your team uses an AI assistant to answer common questions, DeepRails sits between the AI and the customer. It flags answers that are unsure, fact-checks them, and either corrects or blocks hallucinations. Practical steps:

• Connect DeepRails to your chatbot or support pipeline.
• Set rules for what counts as a hallucination (dates, numbers, product specs).
• Let DeepRails auto-correct low-risk errors and send alerts for high-risk ones.

Result: fewer follow-up tickets, less time spent explaining mistakes, and customers who actually trust the bot.

2) Enhance trust in automated systems

Customers trust clear, accurate answers. Use DeepRails to build that trust by ensuring automation doesn’t invent details. For example, if your AI is recommending service times or pricing, DeepRails can verify those details against your database and rewrite any shaky responses.

Tip: Add a “verified by DeepRails” flag in conversation logs so your human agents know which replies were auto-checked. It’s a small change that improves transparency and team confidence.

3) Reduce customer complaints related to misinformation

Misinformation costs time and money. DeepRails cuts down complaints by intercepting wrong answers — especially the ones that sound very confident but are totally wrong (the sneaky ones). Small businesses often get hit hardest because they don’t have a big customer success team to fix things fast.

How to use it:

• Prioritize the most costly mistake types (refunds, pricing, delivery estimates).
• Train DeepRails’ rules to watch those areas closely.
• Route any suspicious replies to a human for quick review.

4) Streamline support processes with accurate data

When AI replies are accurate, your support workflow runs smoother. DeepRails reduces rework by preventing false answers from creating loops of back-and-forth. You’ll see fewer reopened tickets and more first-contact resolutions.

Example workflow:

• Customer asks a question.
• Your AI drafts an answer.
• DeepRails checks it against your data and fixes any hallucination.
• Customer gets a correct answer; support agent only steps in when needed.

5) Maintain brand reputation through quality assurance

Small businesses depend on word of mouth. One public mistake can spread quickly. DeepRails helps protect your brand by catching risky AI statements before they go public — on chat, email, or social posts.

Practical setup:

• Set protection thresholds for external channels (social and public chat).
• Quarantine uncertain replies or require human approval for public posts.
• Keep logs so you can audit and improve the AI’s performance.

Pricing summary

Pricing details were not available at the time of writing. Contact the vendor for current plans and demo options.

Pros and cons

• Pros:
  • Real-time detection of AI hallucinations keeps customer-facing content accurate.
  • Auto-fix capability reduces manual workload for small teams.
  • Improves customer trust and reduces complaints.
  • Works as a safety layer — you don’t need to replace existing AI tools.
  • Good fit for businesses where facts really matter (pricing, delivery, specs).
• Cons:
  • May require setup and tuning to match your product data and rules.
  • Some edge cases will still need human review; it’s not a magic button.
  • If you have no structured data to check against, fixes may be limited.
  • Without visible pricing, budget planning is harder for tiny teams.

Conclusion

DeepRails is a practical guardrail for small businesses using AI to talk to customers. It doesn’t promise to make AI perfect, but it does stop the worst kinds of mistakes before they hit your customers. If you value accuracy and want fewer complaints, DeepRails is worth testing. Start with a single use case — like order tracking or pricing — and build from there. A small setup now can save you big headaches later.

Ready to see if DeepRails fits your workflow? Ask for a demo and test it on your most common customer queries. If it prevents one wrong answer, it’s already paid for itself.