For Systems Integrators

Industrial AI
perception modules
you can ship and
support.

Keplore delivers Vision Inspection, Robot Guidance, and Identification & Verification — each packaged with defined acceptance criteria, a validation harness, FAT/SAT documentation, and a governed update process with rollback.

Vision Inspection
Robot Guidance
Identification & Verification
Talk to our team See the modules
Output contracts — every module
Vision Inspection
Output contract Structured pass/fail · defect class · location · measurement · explicit unknown handling below threshold
Robot Guidance
Output contract Target position + pose · confidence score · defined low-confidence behaviors: re-acquire / retry / hold / divert
Identification & Verification
Output contract ID + verify outputs · confidence score · NO_READ signaling · re-read triggering · exception hooks for routing logic
Every output is typed, bounded, and testable. No black boxes.
Days
Pilot to validatednot months — powered by SRES autonomous build engine
100%
Acceptance criteriadefined before build starts on every engagement
FAT/SAT
Same documentfactory and site acceptance tested against identical criteria
0
Silent failuresevery output has defined handling for unknowns and edge cases
Governed
Every updateyou approve every change — nothing deploys without your sign-off
The Problem

Your customers need AI
perception. Standard tools
won't hold up.

Three failure modes keep appearing on industrial lines — and they're getting more common as AI adoption accelerates.

01
Rules-based vision breaks when conditions change
Lighting shifts, new product variants, fixture tolerances — traditional machine vision requires manual recalibration every time. The line stops. The integrator gets the call.
02
AI models work in demos, fall apart in production
Most AI inspection tools deliver a model and walk away. No acceptance criteria, no validation evidence, no plan for when accuracy degrades six months after go-live. The integrator inherits the support problem.
03
Building AI in-house is slow, expensive, and risky
Manual ML engineering takes one engineer two to three months per deployment with no self-adaptation. Teams without deep AI expertise can't maintain it after handoff. Projects stall or regress.
"We need AI inspection that stays accurate when conditions change — not just something that passes a demo."
— Head of Engineering, Tier 1 automotive supplier
Traditional integrators: custom builds, brittle over time, no AI update lifecycle
AI vision platforms: software-only, no commissioning package, no post-go-live model
Rules-based vision: accurate at install, breaks when the environment drifts
Keplore: production-ready modules, validated, governed, maintainable
Production Modules

Three modules.
One commissioning
standard.

Productized across cells and lines. Each module ships with the same package — scope-bounded, output-defined, validation-ready.

Module 01
Vision
Inspection
Scope
  • Defect detection & measurement
  • Surface, dimensional & cosmetic checks
  • Variable lighting & mixed SKUs
  • Constrained viewpoints
Output contract
Structured pass/fail with defect class, location, and measurement — plus explicit unknown handling below threshold. Not advisory. Actionable.
Module 02
Robot
Guidance
Scope
  • Pick, place, insertion & alignment
  • Variable fixtures & lighting conditions
  • No hand-tuning per configuration
  • High-repeatability workcells
Output contract
Target position + pose with confidence score — plus defined low-confidence behaviors: re-acquire / retry / hold / divert. The controller always knows what to do.
Module 03
ID &
Verification
Scope
  • Label, logo, mark & count verification
  • Character ambiguity & partial occlusion
  • Industrial packaging & logistics
  • Exception routing for automation
Output contract
ID + verify outputs with confidence score — NO_READ signaling, re-read triggering, and exception hooks for routing logic. Designed for downstream automation.
Every module includes
Acceptance criteria + evidence pack
Interface spec + validation harness
Update-ready regression suite
Versioned releases + rollback
Runbook + rollback procedure
FAT / SAT documentation
Scope Boundary

We handle the AI.
You keep everything else.

The boundary between Keplore's scope and yours is explicit, respected, and designed to protect your business model and your customer relationship.

Keplore handles
The AI perception problem.
  • AI model design, training, and validation
  • Acceptance criteria definition and evidence
  • Validation harness and FAT/SAT documentation
  • Post-deployment drift detection and updates
  • Governed release process with rollback
  • Regression testing on every change
  • Versioned artifacts with full audit trail
Keplore is the AI component. We do not touch cell architecture, robot programming, or safety systems. That's intentional.
Stays with you
Your business, intact.
  • PLC and robot programming
  • Safety systems and cell architecture
  • Commissioning process and timeline
  • Customer relationship and project ownership
  • System integration and site support
  • Mechanical design and hardware selection
Keplore does not compete with your integration business. You retain the customer, the contract, and the ongoing support relationship. We are the AI component you commission, deliver, and hand off.
Delivery Playbook

Same five steps.
Same outputs.
Every engagement.

A repeatable structure with defined artifacts at every gate. No milestone closes without sign-off.

01
Define acceptance
Success criteria, thresholds, failure modes, unknown rules. Nothing begins without this. The acceptance criteria document is reviewed and approved before build starts.
acceptance criteria doc
02
Data + edge plan
Coverage for production scenarios and the edge cases that matter. Agreed upfront, not discovered at go-live. Every failure mode with a name is a failure mode with a plan.
capture plan + labeling guide
03
Baseline + risks
Early performance report plus top failure modes identified. First opportunity to surface scope issues before they become schedule problems. Shared before any integration work begins.
baseline report + risk list
04
Integrate + validate early
Interface spec and validation harness delivered so your team can integrate and validate while Keplore tunes. Parallel workstreams. You're not waiting on us to start your work.
interface spec + validation harness
05
Ship the package
Acceptance pack for FAT/SAT plus an update-ready validation pipeline — regression, evidence, rollback. Deployment-ready. Commissionable. Supportable after go-live.
deployment-ready module package
After Go-Live

Maintained under control.
Not handed off
and forgotten.

When conditions change — new SKU, lighting shift, fixture tolerance drift — the system surfaces it, proposes a candidate update, then verifies it before release.

Update-ready validation pipeline
01
Monitor
Continuous drift detection across production performance indicators.
02
Capture
Automated capture of edge cases and distribution shifts as they occur.
03
Propose
System proposes a candidate update with supporting evidence. You review it.
04
You approve
Nothing changes in production without your authorization. Every engagement.
05
Validate
Update verified against regression coverage, packaged with evidence before release.
06
Deploy / Rollback
Staged deployment. Full rollback available at every stage. Audit trail maintained throughout.
This is not autonomous.
Every change is governed, documented, and reversible. Your customer can ask what changed and why — and you will have the answer. The update system is automated in its detection and proposal; it is human-authorized in its deployment. These are not the same thing.
What this means for you
Your customer's line adapts to new SKUs without a site visit
You have an answer when the customer asks "what changed?"
No surprise regressions — every update is regression-tested first
Rollback is available if a version underperforms in production
Your support burden stays bounded, even as the environment evolves
Validation Evidence

Real metrics.
Not marketing claims.

Manufacturing buyers need proof. Here's what a Keplore deployment actually produces.

deployment_log — automotive cosmetic inspection
Module typeVision Inspection
Accuracy at commissioning98.1%
Accuracy at 90-day mark97.4%
False positive rate0.8%
Product variants handled14
Model updates deployed3
Validation incidents0
Line stoppages (vision)0
All 3 updates validated against regression suite before deployment · Performance monitoring active throughout · FAT and SAT ran against same acceptance criteria document
"What sold us wasn't the accuracy numbers. It was that they could tell us exactly what the system would and wouldn't handle, and prove it."
— Automation Engineering Lead, precision parts manufacturer
Acceptance criteria agreed and documented before any build work begins
Test results and validation evidence provided before go-live — not after
Model updates go through the same validation process as initial deployments
All failure modes documented and operationalized in the delivery package
Your customer can audit the system — every decision has a traceable rationale
Request validation white paper →
The Engine

Days, not months.

Keplore modules are built and validated by SRES — an autonomous AI system builder that runs experiments, compares candidates, and validates performance without manual ML engineering at every step.

Autonomous experimentation
SRES runs multiple model candidates, compares results, tunes hyperparameters, and identifies failure modes — without requiring a human ML engineer at every step.
Multi-layer validation
Nothing is declared done until it has passed mathematical reasoning checks, OS-level process verification, and semantic validation — simultaneously. This is what makes SRES production-safe.
Self-adapting in production
The same engine that builds the module also maintains it. When the production environment drifts, SRES detects it, proposes an update, and prepares the regression evidence — before any human reviews it.
What this means for the schedule
A traditional ML engineering engagement takes one engineer 2–3 months per deployment. Keplore delivers in days. That speed advantage compounds across every project in your pipeline.
sres_build.log — vision_inspection_v1
00:01problem scope parsed · acceptance criteria loaded
00:04experiment set initialized · 12 candidates queued
00:31candidate 01–04 evaluated · 3 eliminated on latency
01:12top 3 candidates advancing · hyperparameter sweep running
02:48failure mode analysis · 2 edge cases flagged
03:10edge cases captured · labeling guide updated
05:22validation pass 1 · accuracy 96.8% · threshold: 95% ✓
06:01false positive rate 1.2% · threshold: 2.0% ✓
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 than manual ML
847
test cases generated
Why Not The Alternatives

Where every other option
leaves you exposed.

Compared on the things that matter after go-live, not just at demo day.

Traditional Integrators AI Vision Platforms Rules-based Vision Keplore
Defined acceptance criteria upfront Sometimes — Basic ✓ Always
Validation evidence at handoff Varies — Partial ✓ Every module
Handles variable environments + drift Limited ✓ — ✓ Designed for it
Governed update process post-go-live — — — ✓ Standard
FAT/SAT documentation package Varies — Partial ✓ Every engagement
Defined output contracts (no black box) — — ✓ ✓ Every module
Rollback on every update — — — ✓ Built in
Pilot to deployment in days — Sometimes — ✓ SRES-powered
Traction

Deployed. Validated.
In production.

Real engagements using the same modules and playbook described on this page.

Paying customer
Global Magnetics Supplier
EV and smart home OEM supplier. Vision Inspection module deployed across 2 machines. Deployment scale target: 1,000–5,000 machines per year.
Before: 1 engineer · 2–3 months · no self-adaptation
After: 3–7 days · continuous self-adaptation · 90% faster
Paying customer
Robot Co. — Idaho
Design-for-manufacturing application. Robot Guidance module. $5K–$20K per machine installation, $1K–$10K annual subscription. 30–50 machine deployment target.
Module: Robot Guidance · Status: Active deployment
Strategic partner
Emerson Electric
$18B global automation company. Partnership to provide Keplore AI modules to Emerson's industrial customers — a channel that reaches deep into the exact integrator and manufacturer base Keplore serves.
Revenue: $18B (2025) · Type: Channel partnership
Get Started

Have a perception
problem your current
tools can't solve?

Tell us what you're building. We'll scope the problem, define acceptance criteria, and tell you honestly whether Keplore is the right fit.

Talk to our team Review the playbook first
→ No commitment required → 30-min scoping call → Real engineers on the call