AI That Does Actual Work, Not Demos
Automations grounded in your own data, built with OpenAI, Claude and Gemini, and wired into the systems you already run.
about service
The Difference Between an AI Demo and an AI Feature
A demo works once, on a prepared example. A feature works on your messy real data, every day, and fails safely when the model is wrong. We built BD Automation for our own agency: it encodes years of business development knowledge into a queryable system that scores incoming jobs, drafts proposals grounded in real portfolio records, and improves as it is used. We built CVKOM in Flask and Python with OpenAI behind it, and added AI features to Descripio for Amazon sellers. The common thread is grounding. A model answering from your data beats a cleverer model guessing.
What you get
What an AI project gets you
Answers grounded in your data
Retrieval over your own records, so the output cites something real instead of sounding confident and being wrong.
A failure mode you chose
What happens when the model is unsure is a design decision. Silence, a fallback or a human handoff, decided deliberately.
Cost you can see
Spend logged per feature, so you know what each automation costs to run before the bill arrives.
The boring integration done
The value is in the plumbing to your existing systems, which is the part demos skip.
Model choice that fits the job
Cheap models where quality does not matter, strong ones where it does. Not everything needs the expensive one.
Something a human can override
Automation that assists rather than replaces, with a person able to correct it.
How an AI build runs with us.
01
Discovery
What the task actually is, and whether AI is the right tool. Sometimes the honest answer is a database query.
02
Grounding and data
What the model reads from, how it is retrieved, and what happens when the answer is not in there.
03
Build and evaluate
Tested against real examples, not curated ones. Cost and accuracy measured before it goes near users.
04
Ship with guardrails
Rate limits, cost logging, fallback behaviour and a way for a human to step in.
Technologies we use
The stack we reach for most on this kind of work. Every one of these has shipped on delivered client work.
python
openai api
claude api
google gemini
node js
laravel
vector databases
rest api
This is probably the right fit if one of these sounds familiar
- →Your team spends hours on work that is mostly reading, sorting and summarising.
- →You tried an AI tool and it produced confident nonsense about your own business.
- →You want AI inside your product but cannot risk destabilising what already works.
- →You have knowledge locked in documents and people, and it does not scale.
- →You need to know what this costs to run before you commit to it.
- →You have been sold an AI strategy and want someone to tell you what is actually buildable.
We work with clients in the USA, Europe and the Gulf. Tell us what you have and what it needs to do, and we will tell you honestly whether we are the right fit.
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