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Production da Vinci Inc.
CAPABILITIES

What we do

From framing the problem through choosing the technology, building it and testing it.

CAPABILITIES

What we take on

  • AI systems

    01

    We build generative AI and machine learning into real work and real products, not demonstrations. Where the machine decides and where a person does is part of the design, not an afterthought.

    What this covers

    • Agents that carry a task through
    • Document search that shows its sources
    • Text, image and audio in one pipeline
    • Workflow automation
    • Decision support
    • AI-native applications

    Where you can see it

  • Models: building and measuring

    02

    We do more than call a model that already exists: where the problem needs it, we build, tune and evaluate the model itself. We do not put a model in charge of anything before there is a way to tell whether it is right.

    What this covers

    • Machine learning
    • Domain-specific models
    • Fine-tuning
    • Evaluation harnesses and regression suites
    • Inference optimization
    • Training and evaluation pipelines

    Where you can see it

  • Simulation and digital twins

    03

    Reproducing what happens in the physical world on a computer, and using it for design, verification and decisions. It is what you reach for when trying the real thing is expensive or dangerous.

    What this covers

    • Physical simulation
    • Digital twins
    • 3D environments
    • Synthetic data
    • Optimization

    Nothing public yet

    We have nothing public to show in this area yet. We are glad to walk through it directly.

  • Product engineering

    04

    We take things past the prototype and build them as products people actually use. Shipping is where the work starts: releases, billing and monitoring stay with the same team.

    What this covers

    • Web applications
    • iOS and Android applications
    • Cloud infrastructure
    • Backends and APIs
    • Realtime systems

    Where you can see it

  • Data and knowledge

    05

    Turning a company's data and expertise into something an AI or a product can use. The material nobody wrote for a machine to read is very often the asset nobody else has.

    What this covers

    • Data engineering
    • Knowledge architecture
    • Enterprise search
    • Analytics
    • Data products

    Where you can see it

  • Business and technology design

    06

    When what to build has not been decided yet, we start from framing the problem. We think it through with you before implementation — and then carry on into building it.

    What this covers

    • Problem definition
    • Product strategy
    • Technology strategy
    • Business design
    • Technical feasibility
    • Prototype planning

    Where you can see it

PROCESS

From working it out to putting it in service.

The shape of an engagement varies. The spine of it does not.

  1. 01

    Step 1: Understand

    Understand the problem

    Who uses it, what is going wrong, and what would have to change for it to be worth anything. We do not move on while that is vague.

  2. 02

    Step 2: Design

    Design the system

    The product, the working practice and the shape of the business. Which technology to use follows from that.

  3. 03

    Step 3: Build

    Build it

    We build the assumption that matters most. A document is not the deliverable.

  4. 04

    Step 4: Validate

    Validate it

    Tested against real data in the real environment. A result that goes against us reaches you as it is.

  5. 05

    Step 5: Deploy

    Put it into service

    Not stopping at a proof of concept. Built so it keeps running once it is in service.

TERMS

Settled before we start

Ownership of what we build, how your data is handled, and how the work stays legible. The same terms whatever the shape of the engagement.

You own it
The code, the models, the evaluation sets and the documentation are yours. We do not keep a licence back, and we do not build you something that only we can operate.
Your data stays where you put it
We can build so that your data never leaves your environment, including running models locally where that is what the situation requires. Where a hosted model is used, we say which, and what it is sent.
No vendor captured us
The model is a configuration value, chosen per task and replaceable. We have shipped systems with a fully local fallback path specifically so that independence can be exercised rather than asserted.
Security agreed per engagement
Compliance standards, data residency and access policies are settled at the start of a project against your requirements, not assumed from ours.

You do not need to know what to build yet.

A problem, some data or an idea is enough. We start from deciding what to build.

Talk to us →