We built the platform.
Now we build on it for you.

I started Canall.ai to focus on one particular problem I had lived. I ran a SaaS provider with unhappy customers, waiting too long to get the value they paid for because of delayed data integrations. We did not staff internally to handle the degree of data engineering required, and initial integrations fell into the trap of we-will-do-anything-for-your-logo customization — very difficult to maintain. No blame there. We had a platform that could scale, and operations that could not scale with it — not without paying a heavy cost in owning that last mile.

My role with the SaaS provider ended. When one door closes, another opens. I had a lot of thoughts about LLMs and agentic platforms at that particular moment, and decided to focus my deep knowledge of them toward solving for the most acute issue I'd suffered from — painful integrations — in the domain I knew best: data engineering.

What if we deployed a Claude-Code-like experience as a widget that was aware of the integration points for the SaaS platform it lived inside?

Aware of the APIs and SFTPs and S3 buckets; aware of the SaaS platform's domain; possibly even aware of the customer personas. An agent that could build and run a pipeline dynamically, provided with the skills and secure roles to deliver data integration solutions — with complete optionality in their ultimate ownership and hosting.

And now I had the time to build it. I had identified a broad opportunity, and I had a working stack of many AI dev tools — Claude Pro, ChatGPT Plus, Z.ai, OpenRouter credits, Ollama Cloud, NVIDIA NIM, Windsurf — with the know-how to use all of them fluently together. Add decades of hands-on experience architecting and delivering enterprise solutions directing multiple teams for large financial data services, blue chip used-car dealerships, and Naval contractors. So I built the vision.

That's Canall.ai. Or it's the original premise of Canall.ai. As we delivered on that premise, we realized the platform we'd built had solved a much broader set of problems — problems that spoke to a wide and interesting segment of businesses operating today.

The Canall.ai agent platform runs anywhere.

Any cloud. On-premises. The LLMs it uses can run anywhere too: on-premises, air-gapped, or against any of the inference providers out there — frontier or otherwise. There is observability. There is a robust evaluation framework to maintain quality efficiently. There is an agent tuning arena. There is fluent, simple agent design, configuration, and deployment.

There is policy-based governance, integrated. There is a secrets broker — the agent never gets a key directly. There is nearly infinite extensibility in how the agent integrates with your business, dictated by your requirements to control your costs and your data. Skills, connectors, and capabilities snap in rapidly, and the library has already grown significantly from our initial engagements.

I built Canall.ai to solve one type of problem, and quickly realized the platform — in the right hands — could solve so many more.

There are businesses that are wary of where their data is really going when they use AI. They don't feel comfortable with the multitude of AI offerings out there — whether direct from the frontier provider, or vaguely integrated into the SaaS platforms they already pay for and use. They have no clear or lasting assertions about the provenance of that data, after their SaaS provider wired in some new AI feature hastily.

Many companies aren't comforted by the cost risks either. The AI offerings integrated directly into SaaS providers and dangled for low-to-no-cost introductory deals tend to adjust at renewal — after your metered usage indicates just how much your business actually values the service. And when it comes to agentic solutions, business leaders read about large bills from agents run amok, burning expensive tokens, stuck in a loop — with no relief from the LLM host. You took valuable cycles from their GPUs. Pay up.

They also read about security risks with MCPs, and supply-chain attacks in the open-source libraries that ship in so many platforms with inconsistent security postures.

There are concerns about hitching your cart to the right horse.

Business leaders have watched the landscape shift perpetually — the capabilities of the various LLMs and agentic platforms on offer, the partnerships, the tools. If you adopt their agentic platform, you are adopting their LLM. Changing costs can keep you locked in. So you're hoping you picked the right one.

It's a confusing landscape. And yet companies of all sizes are led by people who, by now, have personally interacted with some form of AI chat, coding tool, or agent — and have been convinced it can be applied to problems they already see. The inefficiencies that hold them back from delivering the most value, the best experience, to their customers.

Canall.ai can help these businesses. We can help you assess your own unique needs and deliver a solution that manages for all of these uncertainties, as best as anyone can.

Some companies prefer to standardize on a major frontier provider — using those LLMs for chat today, and building their automations and agent workflows on the same frontier-managed frameworks tomorrow. There's a clear logic to it: a known vendor, a familiar contract, a strategy that's easy to defend internally. For organizations where that tradeoff fits, it's a defensible path.

We're not building Canall.ai for them. We're building it for the ones who have looked at that same picture and decided they'd rather own their independence than rent their convictions.

We built the platform. Now we build on it — bespoke — for you.

— Mark Avallone, on behalf of the team at Canall.ai