Applied AI research for small and medium businesses.

01 — Our Approach

01.1

Own your AI Strategy

If you outsource your AI infrastructure and become dependent on expensive LLMs, paying exorbitant fees, switching costs will dramatically increase over time. Applying latest AI research and technologies can give small and medium sized businesses a cost and speed advantage over the largest of competitors.

01.2

AI that Learns Your Business

Applied AI is not the general AI that everyone uses like ChatGPT, or Claude, but focused surgical applications, use cases and tuning that is measured, verified, and cost efficient. If you're not orchestrating your own data correctly with the right context and harness, you'll be giving up that intelligence and moat.

01.3

Gets Smarter Over Time

Literally self healing, self optimizing AI with built-in human-in-the-loop feedback. When we work together, any system we develop uses custom curated evaluations that measure performance, use techniques like automatic reflection optimization, and feed back those improvements, and repeat the cycle to achieve an autonomous self optimizing system that learns from your data.

01.4

Protect Your IP

Why would you outsource your IP and business processes outside your network for frontier LLMs to learn how to do it better, and sell it to your competitors? Your traces, interactions, prompts, customer conversations are your IP and competitive moat and it would be a massive loss if you've never used or captured them to the benefit of your own business.

02 — Why Own Your AI

Own your intelligence, don't rent it.

Every time you engage with an AI service, you're paying for a recurring spend with zero access to the improvement loop and end up paying forever and compounding nothing.

Curating your own data pipeline and intelligence gives you control and ownership over your AI systems and allows you to improve them continuously. Smaller models can be more performant at specific tasks than expensive frontier AI.

Leaking IP

In almost all cases, it's not worth the risk of sending your critical IP and data to a third party that could risk your competitive advantage.

You don't have to give up your data and sovereignty to get the best performance AI offers. Implementing your own AI data pipeline can be in many cases more performant and 50x cheaper.

Flying Blind

Generic LLMs hallucinate and fail silently. Without custom evaluations, a confidently wrong answer looks identical to a correct one, and that risk ships straight to your customers.

Treat AI like a research discipline. Implement custom evals that score accuracy on your actual use cases, and optimizers like DSPy and GEPA tune against those metrics so you can prove the system is getting better instead of hoping.

Vendor-Locked

Pricing changes, model updates, deprecation, or model swap can break production overnight if you're not prepared for a switch. AI technology changes so fast that without a provider-agnostic strategy, you're at risk of high costs to rebuild and change.

Build infrastructure and systems once that adapt and learn your business using examples, history, documents, traces, making swapping models and painstaking prompt engineering a pastime.

Let's build.

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