KeploreAI · Santa Clara, CA

AI systems that hold up
in production.

Keplore builds production-ready AI systems for hard, real-world problems — in days, not months. Powered by PhysMind (formerly SRES), our autonomous AI engineering engine.

90% faster than manual ML engineering
Paying customers in production
System · Online 00:00.00
throughput2,847 u/h uptime99.97% cells03 / 03
In the Field

AI systems where they actually run.

From electronic component inspection to robot guidance to automotive lines — built, validated, and adapting in production. Not demos. Not pilots. Real deployments, under real conditions.

Active sites03 / 03
01/03
Vision Inspection · Active

Magnetic component
defect detection.

Surface flaws, flux-pattern anomalies, dimensional drift — caught across 14 SKUs under variable lighting. Self-adapting when the factory floor changes.

Variants14
False pos.0.8%
Cycle62 fps
Robot Guidance · FANUC integrator

Robot vision for
pick, insert, align.

Target position + pose with confidence scoring. Variable fixtures, shifting parts — with typed outputs and defined retry/hold/divert logic wired into the controller.

Axes6-DOF
Δ outputΔX, ΔY
Fallbacktyped
Manufacturing Automation · OEM engagement

Adaptive AI for
the assembly line.

Production lines shift — tooling wear, part variation, fixture drift. Rule-based vision can't keep up. PhysMind builds the perception layer, validates it, and adapts as the line evolves.

ScopeLine process
AdaptationContinuous
HandoffTyped
What KeploreAI Does

AI coding tools build code.
We build systems that learn.

There's a fundamental difference between writing software and building an AI system. Software connects components — once the logic is right, it generalizes. AI systems require experimentation under constraints, continuous validation, and adaptation as data shifts. Keplore automates the hard part.

Software engineering — connect components, logic generalizes, rarely retrained
AI systems engineering — experiment under constraints, validate multiple candidates, continuously adapt as data shifts
Keplore — automates the experimentation, validation, and adaptation loop. Delivers the system, not just the model.

The gap between "model that works in a notebook" and "AI system that holds up in production" is enormous. No existing tool — coding assistants, off-the-shelf models, or manual ML engineering — closes it reliably. That gap is what KeploreAI is built to solve.

PhysMind — Autonomous AI System Builder
Plans, experiments, evaluates, validates, and deploys AI systems without manual ML engineering at every step. What used to take one engineer 2–3 months, PhysMind delivers in days.
Industrial AI Solutions
Production-ready perception modules for manufacturers, OEMs, and systems integrators — packaged with acceptance criteria, validation evidence, and a governed update process that survives go-live.
Self-Service Agent
Technical users describe their AI problem directly. PhysMind autonomously builds, validates, and deploys — accessible via agent interface for exploratory and applied AI work across industries.
Use Cases

Real problems.
Real deployments.

Keplore works where standard models break down — variable environments, mission-critical accuracy, and production conditions that change over time.

Live · A-04
Industrial Inspection · Active Customer
Magnet Defect Detection
Global MoE supplier · EV & smart home OEMs
A global magnetics supplier serving EV and smart home OEMs needed defect detection that could self-adapt as product variants and factory conditions changed — without an ML engineer on-site for every update. Traditional Halcon-based vision required 2–3 months per model rebuild with no self-adaptation.
Before1 engineer · 2–3 months per model · no adaptation · $1,000+ per deploy
After3–7 days · self-adapting · $500/deploy · 90% faster time-to-deployment
Live · B-11
Robot Guidance · Active Customer
Robotic Insertion & Alignment
Design for Making · Idaho Falls, ID · FANUC certified integrator
A FANUC-certified robotics integrator needed an AI perception system to locate bucket opening centers and compute precise displacement coordinates (ΔX, ΔY in cm) for robotic arm insertion — reliably, with retry and fallback logic that native iRVision couldn't provide for this application.
ProblemVariable bucket positioning exceeded native vision capabilities
OutputTyped coordinate output · confidence score · retry/hold/divert logic · local edge deployment
Eval · C-02
Manufacturing Automation · Technical Evaluation
Automotive Manufacturing Automation
Performance automotive OEM · technical discussions
A performance automotive OEM initiated technical discussions around AI-driven perception for production-line automation — where rule-based vision and static ML models can't keep up with tooling wear, part variation, and shifting fixture conditions on the assembly floor.
ChallengeLine variation and drift exceed rule-based vision and pre-trained models
FitPhysMind: self-builds, validates, and adapts without engineers in the loop
Why Keplore

Not a consulting shop.
Not a generic platform.

Keplore is an AI technology company that delivers real solutions. The distinction matters when you're putting AI into production and someone has to be accountable for whether it holds up.

"AI coding tools build code. We build systems that learn — and keep learning after go-live."
Validation evidence, not demos
Every deployment includes acceptance criteria agreed before build starts, test results, and a validation package — not a demo that passes once and never gets checked again.
vs. AI startups who deliver a model and move on
Systems that adapt, not stale models
PhysMind continuously monitors production performance. When data shifts, it proposes a governed update — you review and approve. Nothing changes without sign-off. Instant rollback available.
vs. ML platforms that require your engineers to manage drift
Days, not months
Traditional ML engineering: one engineer, 2–3 months per deployment, no self-adaptation. PhysMind automates the experimentation, comparison, and validation loop — delivering in days at a fraction of the cost.
vs. custom ML shops and manual integration work
Typed outputs, not black boxes
Every module has a defined output contract: structured results, confidence scores, and defined handling for edge cases and unknowns. The downstream system always knows exactly what it will receive.
vs. AI platforms that hand off opaque model outputs
Manual MLAI PlatformsKeplore
Delivers in daysSometimes
Acceptance criteria upfrontVaries✓ Always
Validation evidence at handoffVaries✓ Every build
Governed post-deploy updates✓ Standard
Typed output contracts✓ Every module
Self-adapts to data driftPartial✓ PhysMind
Works without your ML team✓ That's the point
Two Ways to Work With Keplore

One engine.
Two entry points.

Whether you need a deployed solution for your production line, or you want to work directly with the AI system builder — Keplore has a path for you.

The Engine

PhysMind: Autonomous AI
system builder.

Beyond coding tools and model wrappers — PhysMind plans, experiments, validates, and deploys AI systems that adapt to real-world conditions without manual ML engineering at every step.

Agentic decision-making
Tracks performance at every stage — training, testing, deployment. Adapts like a senior ML engineer: tunes hyperparameters, selects architectures, identifies failure modes — without being asked.
Autonomous experimentation
Automatically compares and analyzes multiple model candidates. Selects the best approach for the specific constraints of the problem — not a pre-selected template or generic foundation model.
Multi-layer validation
Multiple LLM roles (actor, critic, validator) operating simultaneously. OS-level checks across processes, files, and metrics. Mathematical reasoning combining semantic and logic tests. Nothing declared done until all layers pass.
Self-correcting in production
The same engine that builds the system also maintains it. Detects performance drift, proposes governed updates, validates changes before deployment. The system improves without requiring your engineers to manage the loop.
sres_build.log — vision_inspection_v1
00:01problem scope parsed · acceptance criteria loaded
00:04experiment set initialized · 12 candidates queued
00:31candidates 01–04 evaluated · 3 eliminated on latency
01:12top 3 advancing · hyperparameter sweep running
02:48failure mode analysis · 2 edge cases flagged
03:10edge cases captured · labeling guide updated
05:22validation pass · accuracy 96.8% · threshold 95% ✓
06:44regression suite compiled · 847 test cases
07:30acceptance criteria met · evidence pack generated ✓
07:31module ready for integration handoff ✓
~7h
build to evidence pack
90%
faster vs manual ML
847
test cases generated
Get Started

Have an AI problem
that won't stay solved?

Tell us what you're building. We'll tell you whether Keplore is the right fit — and what it would take to get there.

→ No commitment required → 30-min scoping call → Real engineers on the call

Products

PhysMind CLI PhysMind VSCode Extension PhysMindAct PhysMind Online Lab

Solutions

For Integrators