AI is getting more capable. Put the capability to work.
Frontier models are advancing in reasoning, multimodal understanding, tool use and complex task execution. Open-weight models are making increasingly capable AI more flexible to deploy and operate.
Ales Analytics builds the systems that connect these capabilities to your data, infrastructure and workflows.
Private AI setup
Your AI, your data, your infrastructure, your rules
A working AI environment inside your own cloud account or your own building, with the choice of model left open.
You can show, with a diagram and a log, where every AI request in your organisation goes and who saw it.
- Open-weight models you host, and frontier models called through your own gateway, chosen per task
- Your identity provider, your networking, your keys, your regions
- Usage, cost and latency visible from the first day, broken down by team and by task
What we build and run inside it
The knowledge people ask questions of, the work that gets shorter, the routing that keeps the bill sensible, and the upkeep that stops all three drifting. Each can be taken on its own.
Company knowledge AI
Answers from what your company already knows
The contracts, manuals and procedures you already hold, answerable in a sentence, with the source printed next to the answer.
A question that took an afternoon of searching returns the current answer in a sentence, with the source printed next to it.
- Permissions checked at the moment a question is asked, using the roles your systems already hold
- One system named as the authority for each fact, so two versions stop competing
- Every answer prints the document, the section and the revision it read
AI workflow automation
The work itself gets shorter
One repeated piece of work, rebuilt so most of it runs on its own and a person approves the part that matters.
A named workflow runs in a fraction of the time it took, and one person still approves the step that matters.
- Models, ordinary code, rules and your existing systems, each used for what it is good at
- A human approval gate at the step where being wrong would be expensive
- Scoped around a named workflow, with the stopping point agreed before anything is built
AI cost and model optimisation
The right model for each task
A routing layer that sends routine work to small models and keeps the large ones for what genuinely needs them.
You stop paying frontier rates for questions your own documents already answer, and you can show quality held.
- Each kind of task scored on quality, cost, latency, privacy and reliability before it is routed anywhere
- Routine questions answered by small models inside your own boundary
- Every routing decision logged, so you can see what went where and why
Managed AI and continuous improvement
It still works six months after the launch
Watched, scored and improved for as long as it runs, with the numbers reported whichever way they move.
Six months after launch the system still performs, and you have the numbers to show it.
- Answer quality, retrieval accuracy, cost, latency and failure rates watched continuously
- Real user feedback and the edge cases people hit, fed into the next round
- Every change scored against your own test set before it is promoted
Tell us what you are trying to do, in as much or as little detail as you have. You will be talking to the people who build these environments, and you will get a straight answer back.
Start with a written view of where you stand
Tell us what you are running and what you are considering. You get our assessment in writing, and you keep it whatever you decide.