Genelith AI Solutions

Turn past designs into the strength of your next design.

Drawings, specifications, past trouble records — and the judgment of veteran engineers. We turn scattered design knowledge into a form ready for decision-making, with the evidence attached.

We deliver configurations in which your drawings and design data are not used to train AI. On-premises and closed-network deployments are supported.

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Drawing Check Support AI

3 findings

  • Clearance 2.0mm — Internal standard 3.0mm or more
  • Material not specified — SUS304
  • Chamfer note missing — C0.5
EvidenceInternal standards DS-102Similar drawings

DESIGN FLOOR CHALLENGES

Design delays don't come from drafting — they come from searching, judging, and verifying.

From concept studies to drawing checks, every phase chains inefficient information hunting with dependence on senior engineers, stretching lead times.

Up to 340 minutes

spent cross-checking a drawing against its specifications (per case, measured before AI adoption)

Source: Panasonic Connect announcement, February 2026

About 90%

of design engineers recognize that design work depends on specific individuals

Source: New Innovations survey (about 200 design engineers)

  1. 01

    Concept design

    Data from past projects is scattered, so organizing requirements and searching for similar specifications takes time. Early studies depend on veteran engineers' experience.

  2. 02

    Basic design

    Tracing the impact of specification changes depends on individuals. Drawings, BOMs, and CAE results are not carried over to the next project.

  3. 03

    Detailed design

    Dimensions, tolerances, and notes are entered repeatedly, with transcription errors. How they are applied varies from person to person.

  4. 04

    Drawing check & approval

    The quality of review comments depends on the reviewer's experience, and waiting for checks becomes a bottleneck. Findings are not fed back into the next design.

BARRIERS TO AI ON THE DESIGN FLOOR

Reading engineering drawings is still hard for general-purpose AI.

71.7%

Practising experts: 94.9%

Drawing-reading accuracy of the best-performing general-purpose AI (structural drawing benchmark)

Source DrawingVQA (arXiv 2607.15418, July 2026)

38.7%

Under 10% for general-purpose AI

Share of users of specialized AI (AI built for a specific task) who felt that understanding of drawings had been achieved

Source CADDi survey, January 2026 (n=300)

97%

Up to 340 minutes → 10 minutes

Reduction in drawing and specification cross-checking time (specialized AI, design and development division of a major electronics manufacturer)

Source Panasonic Connect announcement, February 2026

OUR APPROACH

An AI that keeps adapting to your floor, driven by two wheels: understanding context and learning tacit knowledge.

The AI supports engineers' decision-making, and the final judgment stays with people. Fully automated design is not the goal.

  • Drawings
  • Specifications
  • Past records

Real-time understanding of context

Proposal

Human final judgment

Automatic learning of tacit knowledge

Real-time understanding of context

Drawings, specifications, past records. The AI reads these disconnected data sources together, understanding the engineer's intent as context.

Automatic learning of tacit knowledge

Engineers' corrections and adoptions of the AI's proposals are logged, accumulating veteran engineers' thought processes so the AI keeps learning.

Every proposal cites its evidence, with reference links to the internal standards and similar past drawings it drew on.

Deployment is not the end: the AI keeps getting better on feedback from the floor.

HOW TO CHOOSE

There are four ways to use AI. Compare where each one fits and choose.

We have laid out the four approaches so you can compare where each one fits.

  • Use general-purpose AI as it is

    Where it fits

    Drafting and summarizing text, general knowledge support

    Where it does not fit

    Reading engineering drawings, and judgments based on internal standards (see the statistics under barriers to AI on the design floor)

  • Packaged products (drawing check, drawing search, and so on)

    Where it fits

    Work where standard check items are enough. Drawing management is already in order and you want to start right away

    Where it does not fit

    Work that needs judgment based on internal design standards and unwritten rules

  • Build it in-house

    Where it fits

    You have a dedicated AI team and the capacity to keep improving what you build

    Where it does not fit

    You cannot assign dedicated staff (after the build, maintenance and improvement still need someone to own them)

  • Custom development with hands-on support (Genelith)

    Where it fits

    Work where a packaged product did not reach the accuracy you need. Many internal rules, and you want to confirm results step by step

    Where it does not fit

    Work where standard features are enough (in that case we recommend a packaged product)

Five questions to guide the choice

  1. 01

    Are your design standards and check items documented?

  2. 02

    In a packaged-product trial, was the accuracy enough for the work?

  3. 03

    Can you assign dedicated staff to build and improve the AI?

  4. 04

    How far outside the company can your drawings and design data go?

  5. 05

    Do you want to confirm the effect in stages before deciding on investment?

Start with 30 minutes — tell us about the challenges on your design floor.

Book a free 30-minute consultation

GETTING STARTED

You can start even before your data is in order.

We offer two entry points, matched to how much data you have accumulated.

01

If you have accumulated drawings and documents

Plan A | Free prototype validation

Under NDA, we build a lightweight AI prototype with your data at no cost. You can verify answer accuracy on your own data, the feel of the UI, and the estimated cost and timeline of a production rollout. The scope and duration of the free validation, and the criteria for moving to a paid engagement, are set out in writing before we start.

02

If your data foundation is still ahead of you

Plan B | Data creation and requirements partnership

We work alongside you from the design of what to collect, at what granularity, and how. Using AI as you go, we build a system where design data and knowledge accumulate naturally.

PROCESS

Start small, and advance as the results are confirmed.

An initial diagnosis measures your current state, and we confirm the effect at each stage before moving on. That is why we don't promise reduction rates upfront.

  1. 01

    30 min

    Free consultation

    We hear about your current challenges and align on an approach by sharing relevant case studies from our work.

  2. 02

    1–2 weeks

    Initial assessment

    We identify the target business process and run a quick assessment of the volume, quality, and format of your existing data — a first read on feasibility.

  3. 03

    4–8 weeks

    Effectiveness validation

    We build a simple prototype using a sample of your data. Key people on the floor try it directly, and we test both the technical and operational feasibility of whether AI contributes to real work.

  4. 04

    3–6 months

    Floor integration and field validation

    We limit scope to a specific department or line and begin beta operation within real data and real processes, gathering floor feedback and surfacing issues.

  5. 05

    6 months and beyond

    Ongoing accuracy improvement and sustained adoption

    Under a continuing engagement, we tune accuracy on real data from the floor, sustain adoption on the floor, and guard against the AI model's accuracy degrading over time.

  6. 06

    Progressively

    Expansion

    We strengthen data links with other processes and prepare for scaling company-wide.

Deployment is not the end. We keep improving accuracy and usability based on feedback from the floor.

TEAM

An expert AI team, building for the design floor.

Major automotive manufacturer / AI & DX Promotion Office

A stalled project is moving again

An AI project that had stalled internally is back on track, and we can now clearly see it becoming real.

Building a multimodal foundation model for the factory floor

Major automotive manufacturer / AI & DX Promotion Office

The real problem surfaces through hands-on support

As the engagement continued, the real problems we needed to solve, sitting just outside our original request, became clear one after another.

Automated 3D model design

Major automotive manufacturer / Quality control department

Development where you can see why, not just what

It's not just the quality of what's delivered — we can see the reasoning behind each result, and that let us move forward with confidence.

PoC toward visual inspection and a digital twin

An AI team with a Kaggle Grandmaster on staff, building for the design floor.

We have built AI for several companies, centered on the automotive industry. In design support AI, on-site integration with a major automotive manufacturer is now underway.

Partners & Programs

  • NVIDIA Omniverse Enterprise
  • NVIDIA Inception Program
  • Microsoft for Startups
  • Google for Startups

Partners & Programs

Start with 30 minutes — tell us about the challenges on your design floor.

Book a free 30-minute consultation

FAQ

Start with 30 minutes — tell us about the challenges on your design floor.

We'll work out together where to start so the effect can be confirmed.

Plan A | Free prototype validation

If you have accumulated drawings and documents

Plan B | Data creation and requirements partnership

If your data foundation is still ahead of you

Book a free 30-minute consultation

What we do in the 30 minutes

  1. We'll ask about your current challenges.
  2. We'll share relevant case studies.
  3. We'll align on an approach together.