The AI Automation Field Guide: From Pilot to Production in 90 Days
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A pragmatic 90-day roadmap to take AI automation from pilot to secure production.
Own your AI: Purpose-built models that know your business
Transform your proprietary knowledge into competitive advantage
with AI models that never leave your environment.
Why Small Language Models (SLMs) win
Specialized 1-2B parameter models outperform models 1000x their size on your specific tasks. It sounds counterintuitive, but the data is clear: when you train small models on your exact use cases instead of everything on the internet, they're faster, more accurate, and infinitely more secure. Here's why smart enterprises are choosing focused over massive.
10-50x faster
response times
SLMs process requests in milliseconds, not seconds. Your team gets instant answers, and your infrastructure costs stay predictable.
95% accuracy vs 70% from generic AI
When trained on your specific data and use cases, a 1B parameter model outperforms GPT-5 on your business tasks. Why? It's not distracted by trying to know everything.
Run entirely on your infrastructure
Small enough to deploy on your own servers. No API calls, no data leaving your environment, no compliance concerns. Complete control over your AI.
Small Models, Big Impact:
Real Client Results
See how financial institutions achieve breakthrough results with specialized models trained on their data — deployed in weeks, running entirely on-premise.
AI that knows your products
Major health insurer struggled with complex coverage questions. Generic AI gave wrong answers 40% of the time, risking compliance violations. We deployed a specialized model trained only on their policies. Now customers get instant, accurate coverage recommendations — from "preventive care options" to "pre-existing condition coverage." Impact: 34% more conversions, 68% fewer calls, zero compliance issues. Deployed in 5 weeks, runs entirely on-premise.
AI That Knows Your Customers
Regional bank wanted to stop wasting time on untargeted campaigns. Manual analysis took days, generic AI didn't understand their specific customer base.Now sales teams just ask: "Find SME clients with good payment history who might need working capital" or "Which dormant accounts are worth reactivating?" Gets answers in 2 seconds.Impact: Campaign ROI up, analysis time from days to seconds, runs entirely on bank's servers.
Why companies contact us to start over their AI pilots with SLMs
These companies had budgets, teams, and 6-month pilots. Here's what made them rethink everything.
"We spent $200K on prompts that still don't work"
They hired prompt engineers, built complex workflows, tested every LLM. But generic models gave generic answers no matter how clever the prompts. Banking clients couldn't get accurate product recommendations. Insurance clients got made-up coverage details. → Solution: Models trained on your actual products don't need clever prompts.
Our legal team shut down the whole project
Six months in, compliance finally reviewed where the data was going. API calls to OpenAI? Customer data in Azure? The pilot died instantly. All that work, killed by one security audit. → Solution: Everything runs on your infrastructure, no external dependencies.
It works great in demos, fails in production
The POC was brilliant. Then reality hit: 3-second response times, $30K monthly API bills, and hallucinations under load. What worked for 10 test cases broke at 10,000 real ones. → Solution: Small models built for production - millisecond responses, predictable costs.
"We built something nobody can maintain"
The consultants left behind a complex system. RAG pipelines, vector databases, prompt chains - it's technical debt that breaks constantly and no one understands. → Solution: Simple, purpose-built models that just work.
Your Path to Specialized AI
Every successful SLM deployment follows these proven steps.
1
Understand Your Unique Context
We map how your business actually works - not generic processes, but YOUR specific products, services, and customer interactions. This foundational understanding determines everything else.
2
Define Success Metrics
What does winning look like for you? Faster response times? Higher accuracy? Better conversion? We establish clear, measurable goals before any technical decisions.
3
Architect for Your Reality
Select the right open-source foundation and infrastructure approach based on your constraints - performance requirements, security policies, existing systems. No one-size-fits-all solutions.
4
Build Your Competitive Edge
Transform generic open-source models into specialized experts that understand your business. Test against real scenarios, ensure accuracy on edge cases. This is where your proprietary data becomes your AI advantage.
5
Deploy with full control
Launch in your environment, monitor real performance, continuously improve. You own the model, the infrastructure, and the improvements. No black boxes, no vendor lock-in, your AI gets smarter as your business evolves.
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How AI intern helps in research and analysis
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Ready to Own Your AI?
Stop renting generic models. Start building specialized AI that runs on your infrastructure, knows your business, and stays under your control.
