Why AI projects stall
Large Language Models (LLMs) can improve efficiency and margins, but only when they are prompted and tuned the right way. For most companies, getting there is harder than it looks.
The usual blockers:
- Hidden costs
- Accuracy problems, including hallucinations that keep teams from trusting the results
- Integration work with existing systems
- Sensitive data that has to be protected
- Infrastructure that has to be maintained
So projects burn time and budget at the pilot stage and never reach production.
How we get GenAI into production
Bolder gives you a secure way to bring GenAI into production. Instead of pilots that fail to scale, we build systems that integrate with your stack and handle high-volume workflows.
They plug into existing business systems in nearly any domain, language or dialect. You don't rebuild infrastructure or redesign workflows. The system adapts to your data pipelines, compliance requirements and security standards.
Most teams start with one focused use case and expand to other departments once it proves its value.
Results so far
- Legal teams cut contract review cycles from weeks to hours by running precise queries over thousands of agreements.
- Marketing teams analyzed campaign performance at scale and found insights that improved ROI.
- Customer support teams gave consistent, accurate answers, which reduced ticket backlogs and improved retention.
- Retail managers got a clearer view of purchasing patterns and customer feedback, which led to better product decisions and stronger sales.
Scaling what you already have
You don't need to build everything from scratch to get value from AI.
Many companies already run Retrieval-Augmented Generation (RAG) in pilots or early projects. Problems start when those pilots need to scale: indexes slow down, accuracy drops and maintenance gets expensive.
We don't rebuild what is already there. We take systems like RAG and scale them into production. That usually means:
- Better document chunking
- Reliable multilingual retrieval
- Observability for performance
- Connectors into your existing workflows
The result is a system that keeps up as your business grows.
Beyond AI: backend work
We don't only build AI systems. In one engagement, the priority was backend infrastructure for identity, access control and secure payment flows. We delivered a cloud-ready service with:
- Authentication
- Role management
- Payment gateway integration
It was built to scale and stay maintainable, which gave the client a clear path to monetization without adding technical debt.
The cost math
Every deployment is tied to measurable outcomes. Here is what that looks like when a legal review workflow moves to an AI-driven system:
| Team Size | Hours per work day | Avg. Hourly Rate | Team Daily Cost | Total Team Cost Per Month | Bolder Cost | Net Savings |
|---|---|---|---|---|---|---|
| 5 | 5 × 8 hours = 400 hrs | $20/hr | $8,000/day | $168,000/mo | $500/mo | $167,500/mo |
| 10 | 10 × 8 hours = 800 hrs | $20/hr | $16,000/day | $336,000/mo | $500/mo | $335,500/mo |
| 20 | 20 × 8 hours = 1,600 hrs | $20/hr | $32,000/day | $672,000/mo | $500/mo | $671,500/mo |
What changes
- Reviews that took days now close in hours.
- Support handling time drops by 40%.
- Tens of thousands in manual cost are replaced with a fixed, predictable spend.
Next step
If you have a pilot that stalled, or a workflow you want to move to AI, contact us and we can look at it together.