Digitize your handwritten notes and never lose sight of the again
A retrieval system that responds from the company's actual knowledge base - every answer traceable to its source page.
(an_AI_studio_by_super_interactive)
AI is raw material. It belongs in the build, not center stage.
We build AI-powered digital products that hold up - accessible, scalable, robust — with a decade of strategy, design, development and machine-learning practice behind them.
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Strategic discovery
We map where AI genuinely earns its place in your product - and where it doesn't. You leave with a plan, not a pitch.
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AI implementation
Retrieval, fine-tuning, evaluation, infrastructure. Ten years building ML systems that ship and stay shipped.
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User Experience design
Interfaces where the model does its work quietly. The product leads; the AI serves it.
- (engineering)
- (design)
- (accessibility)
- (ux/ui)
- (research)
- (usecases)
- (evaluation)
- (machinelearning)
- (implementation)
- (strategy)
- (analytics)
(work)
Built, shipped, still running.
A selection of products where the AI does its job and stays out of the way.
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A retrieval system that responds from the company's actual knowledge base - every answer traceable to its source page.
(how_we_work)
Less theatre, more method.
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(01)
Listen first
We start with your product, your users and your data — not with a model. One week of focused discovery, ending in a written assessment of where AI helps and where it would only add weight.
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(02)
Prove it small
A working prototype on your real data within weeks. Measured against evaluation criteria we agree on up front, so the decision to continue is based on evidence.
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(03)
Build it properly
Design, engineering and ML practice in one team. Accessible, scalable, robust — the boring qualities that decide whether a product survives contact with users.
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(04)
Stay until it's stable
Models drift and usage changes. We monitor, evaluate and hand over only when your team can run the system without us.
(writing)
Notes from the build.
What we learn while building, written down plainly, for anyone to enjoy.
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Evaluating retrieval before anyone calls it done
The criteria, the test set, and what "good enough" means in best practices.
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Why most AI features get removed within a year
And the three questions that would have already caught it at the discovery stage.
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Designing interfaces where the model is invisible
Patterns for citations, confidence and graceful failure, this is how it’s done properly.
(start_a_conversation)