BuildOps™ — Engineer what's next. Engineering infrastructure for builders.

The problem

Technology is moving faster.

AI models are changing. Agents can now perform increasingly complex tasks. Robotics is becoming more intelligent. Autonomous systems are becoming more capable. Cybersecurity is becoming more complex. Software systems are becoming more distributed.

But engineering the systems around these technologies is becoming harder.

Teams work across

RequirementsDocumentsCodeRepositoriesAI ModelsDataCloudAPIsSimulatorsSecurity ToolsTestingDeploymentMonitoringEvaluation

The tools are powerful.

The engineering context is fragmented.

BuildOps is designed to connect that context.

What is BuildOps™?

Engineering infrastructure for builders.

An engineering environment designed to connect the work required to turn a technical problem into a working, tested, evaluated and deployable system.

Not simply another coding platform.

Not just a repository.

Not just an AI coding tool.

Not just a simulator.

Not just a cybersecurity scanner.

From

“I want to build this.”

“Here is the system I built, tested, evaluated, deployed and proved.”

The platform

Everything you need to engineer real systems.

Projects

Build and ship real systems with tests, deployment and evaluation.

API, data and ML services
Full repository with CI and Kubernetes
Scored on 10 engineering dimensions

Open Projects

Labs

Run controlled experiments on models, agents and systems.

Frontier and agent evaluation
Reproducible runs and traces
Failures recorded as findings

Open Labs

Engineering Problems

Take on real problems with requirements, constraints and context.

Beginner to advanced
Starter repo and test suite
Structured evaluation report

Open Engineering Problems

Assessments

Submit your work and receive a structured evaluation.

Architecture to documentation
Strengths and recommendations
Version history of scores

Open Assessments

AI & Agents

Evaluate models and agents for reliability, safety and cost.

Tool-use and permission tests
Latency, cost and robustness
Sandboxed execution

Open AI & Agents

Research

Run experiments and publish reproducible results.

Benchmarks and datasets
Methodology notes
Open discussion

Open Research

Systems

Design for scale, caching, queues and fault tolerance.

Architecture diagrams
Failure and load scenarios
Observability setup

Open Systems

Robotics

Take navigation and perception from simulation to the edge.

ROS 2 planners and nodes
Simulation test runs
Fleet coordination

Open Robotics

Cloud

Containerize, deploy, monitor and operate on any cloud.

Docker, Kubernetes, CI/CD
Infrastructure as code
Autoscaling and rollbacks

Open Cloud

Security

Threat model, harden and test applications and agents.

Authentication and secrets
Dependency and API security
Agent permission boundaries

Open Security

Complex problems. Simplified engineering.

Your company has a complex problem. BuildOps engineers the solution.

Describe the problem in plain language. BuildOps turns it into requirements, architecture, working code, tests, simulations and evidence in one connected workspace.

Example: a flash-sale launch

A retailer is launching a flash sale for a new iPhone. Traffic spikes in seconds, inventory must stay accurate and payments cannot fail. In BuildOps the team states the goal, and the workspace produces the architecture, the services, the load tests and the failure simulations, then records how each result was validated.

Engineering is complex. Building isn't.

BuildOps keeps requirements, design, code, tests and evidence in one place, so the team works on the problem instead of the tooling.

AI Engineering Studio

BuildOps turns a problem statement into a working engineering plan and implementation. Seven capabilities run together:

Build and execute

Code is written, run and checked inside the workspace. Every run keeps its logs, inputs and results.

Requirement analysisArchitecture designCode generationTest generationFailure analysisPerformance tuningDocumentation

Test, simulate and optimize

Load testing, failure injection, security checks, cost estimates, performance profiling and optimization suggestions run on the same project.

Deploy and operate

Ship to your cloud, watch the system run and feed real behaviour back into the engineering context.

Engineering evidence

Every decision, test and evaluation is recorded, so a result can be reviewed and trusted, not just claimed.

Built for companies

Faster solutions for complex problems, with a record of how each one was designed, tested and approved.

Built for developers

A workspace where you understand, architect, implement, validate, evaluate and prove your work, and keep the proof.

Engineering is complex. Building shouldn't be.

The workspace

Code, test, deploy and monitor — in one workspace.

Build real systems, explore emerging technology, solve engineering problems, collaborate with builders, and demonstrate what you can actually engineer. Don’t just say you can engineer. Prove it.

Home

Explore

Build

Projects

Labs

Problems

Research

Companies

Teams

Your Work

Workspace

Real-Time Fraud Detection System In Progress

OverviewCodeDataModelsTestsDeploymentsMonitoring

▸ api

▸ models

▸ services

▸ tests

app.py

requirements.txt

README.md

app.py

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI(title="Fraud Detection")

class Transaction(BaseModel):
    amount: float
    user_id: str

@app.post("/predict")
async def predict(t: Transaction):
    return {"risk": "low", "score": 0.12}

Deployment

StatusLive

System Status

APIHealthy

DatabaseHealthy

ModelHealthy

MonitoringActive

Traffic

Illustrative workspace. Projects, scores and profiles shown are examples.

Engineering Problems

Real problems. Requirements, constraints and context.

Take on problems from beginner to advanced, with a starter repo, a test suite and a structured evaluation report.

LiDAR Point Cloud Processing System

PythonNumPyROS 2Open3DAdvanced

Detect and track objects in a 360° LiDAR scan: ground removal, clustering, 3D boxes.

Start Building Open problems Evidence

Problem

A spinning sensor returns hundreds of thousands of 3D points every second. Nothing in that stream says “car” or “person”. Your system must find the ground, separate objects and report their positions fast enough for a vehicle to react.

CropVoxel downsampleRANSAC groundClustering3D boxesROS 2 topic

You will deliver

A pipeline that turns one scan into a list of 3D boxes
Unit tests on synthetic scenes with known answers
A latency profile per stage and a budget under 50 ms
A ROS 2 node that publishes boxes for RViz

Explore the scene in 3DDrag to rotate 360°

Inside the problem

OverviewPoint cloud basicsPipeline codeTestsPerformanceDeploymentEvaluation

pipeline.pyPython · Open3D

import numpy as np
import open3d as o3d

def detect_objects(pcd):
    # 1. Crop to the area the vehicle cares about
    box = o3d.geometry.AxisAlignedBoundingBox((-40, -40, -3), (40, 40, 3))
    pcd = pcd.crop(box)

    # 2. Voxel downsample to stay inside the latency budget
    pcd = pcd.voxel_down_sample(voxel_size=0.1)

    # 3. RANSAC: find the ground plane and remove it
    _, ground = pcd.segment_plane(distance_threshold=0.2,
                                  ransac_n=3, num_iterations=200)
    objects = pcd.select_by_index(ground, invert=True)

    # 4. Cluster what is left into candidate objects
    labels = np.array(objects.cluster_dbscan(eps=0.5, min_points=10))

    # 5. One 3D box per cluster, published on a ROS 2 topic
    boxes = []
    for k in range(labels.max() + 1):
        idx = np.where(labels == k)[0]
        boxes.append(objects.select_by_index(idx).get_axis_aligned_bounding_box())
    return boxes

Humanoid Robot: Parts, Neural Architecture and Control

PythonControlKinematicsRLSafetyFrontierBuildOps Labs

Design a humanoid from actuators to neural policy: balance, reach, learning, and a safety layer that overrides everything.

Start Building Create Submission Evidence

Problem

Design a humanoid robot that can understand a request, see the world, walk without falling, use its hands, and stop safely when something goes wrong. A humanoid is a stack: mechanics, sensors, fast control loops, learned skills and a slow planner on top.

Design example (not a product spec)

Item	Example value
Height / mass	about 1.7 m / 55-70 kg
Degrees of freedom	30 or more across legs, arms, hands, waist and neck
Compute	onboard GPU for perception and policies, real-time controller for joints
Loops	1 kHz joint control, 50 Hz skills, 1-5 Hz planning

Illustrative walking cycleHip, knee, ankle and shoulder joints

PartsNeural schemaHow it worksControl codeLearningSafetyTestsDeploy

Inside the problem

OverviewAnatomy & partsNeural schemaHow it worksControl codeLearningSafetyTests & simulationDeploymentEvaluation

safety.pyPython · illustrative

def control_step(state, policy, limits, safety):
    cmd = policy(state)                 # learned skill, about 50 Hz
    cmd = limits.clamp(cmd)             # joint, torque and speed limits
    if safety.violated(state):          # tilt, torque, e-stop, lost sensor
        return safety.safe_stop(state)  # the safety layer overrides everything
    return cmd                          # sent to the 1 kHz joint controllers

Ride Booking Platform: Request, Match, Track, Pay

PythonFastAPIRedisPostgreSQLWebSocketsVery hardBuildOps Engineering

Match riders to the nearest driver in real time without double-assigning, price with surge, and charge exactly once, through crashes and retries.

Start Building Create Submission View Evaluation

Problem

Build the backend of a ride-booking service like Uber or Ola. A rider requests a ride; the system finds the nearest free driver in seconds, tracks the trip live, prices it with surge, and charges exactly once. It must stay correct while thousands of requests, location pings, cancellations and payment calls overlap.

Why this is very hard

Real-time geo search: find the nearest drivers among 100,000 that move every few seconds
Concurrency: two riders must never get the same driver
Many states: a ride moves through eight states and can be cancelled from several
Money: charge once, even if the network fails mid-payment
Scale: tens of thousands of location writes per second per city

Design targets for one city

Number	Value	Source
Rides per day	1,000,000	design target
Drivers online	100,000	design target
Location ping interval	4 s	=> 25,000 writes/s
Peak ride requests	about 150/s	10 × average

RequirementsArchitectureData modelMatchingPricing & statesPaymentsAPIsSimulationDeployScale

Inside the problem

ProblemRequirementsArchitectureData modelMatchingPricing & trip statesPayments & failuresAPIsSimulation & testsDeploymentScalingDecisionsEvaluation

matching.pyPython · Redis

import redis

r = redis.Redis()

def claim_nearest_driver(rider_id, lng, lat):
    # nearest free drivers first, from a geo index of live locations
    candidates = r.geosearch("drivers:online", longitude=lng, latitude=lat,
                             radius=3, unit="km", sort="ASC", count=10)
    for driver in candidates:
        # atomic claim: only one rider can win this driver
        if r.set(f"driver:{driver}:claimed", rider_id, nx=True, ex=30):
            return driver
    return None  # widen the radius or queue the request

def charge_once(trip_id, amount, gateway):
    # same key on every retry, so the rider is charged exactly once
    return gateway.charge(amount=amount, idempotency_key=f"trip-{trip_id}")

Every company has a different problem

Your problem defines your workspace.

BuildOps is designed around the problem — not around one fixed industry. One platform. Different engineering worlds.

Mobility

Building an autonomous delivery system.

Fintech

Solving real-time fraud detection.

Healthcare

Engineering medical AI.

Manufacturing

Automating a factory.

Cybersecurity

Developing a new security system.

AI

Building autonomous agents.

Startup

Turning a new idea into a product.

Research

Exploring technology that does not yet have a standard engineering workflow.

Don’t start with a template. Start with the problem.

A company enters BuildOps and says: “This is what we need to solve.” BuildOps then organizes the engineering environment around:

Problem+Requirements+Technology+Data+Architecture+Team+Risk+Infrastructure+Evaluation criteria

The platform remains standardized. The engineering workflow can adapt to the work. A robotics team does not need to work like a SaaS team. A cybersecurity team does not need to work like an autonomous-vehicle team. A research team does not need to work like a startup.

Different problems.Different workflows.One engineering infrastructure.

How BuildOps simplifies engineering

Complex engineering. Clearer workflow.

Engineering can involve dozens of tools and hundreds of decisions. BuildOps gives the work a connected engineering context.

Where do I define the requirements?Where do I design the architecture?Where do I build?Where do I run it?Where do I test it?Where do I simulate it?How do I evaluate it?How do I prove the result?

BuildOps organizes the engineering journey around one connected flow:

Define

What are we solving?

Understand

What technology, data, constraints and systems are involved?

Design

What architecture should we build?

Build

Create the software, model, system or prototype.

Run

Execute the system and experiments.

Test

Verify functionality, reliability and performance.

Secure

Identify and address security risks.

Simulate

Test scenarios before exposing the system to the real world where appropriate.

Evaluate

Measure whether the system actually works.

Improve

Learn from failures and optimize.

Deploy

Move the system into its intended environment.

Operate

Keep the system running.

Monitor

Understand what is happening.

Prove

Create evidence of what was built, tested and achieved.

What can you build?

If it is a technical system, engineer it.

Software systems

APIs
Distributed systems
Cloud applications
Enterprise platforms
Real-time systems
Data platforms
Edge applications

Artificial intelligence

Machine learning systems
AI applications
RAG systems
Multimodal systems
AI agents
Agentic workflows
AI evaluation systems

Robotics

Industrial robotics
Mobile robots
Autonomous robots
Perception systems
Navigation
Control systems
Robot simulation

Autonomous systems

Autonomous vehicles
Delivery systems
Drones
Sensor-fusion systems
Navigation systems
Decision-making systems

Cybersecurity

Security platforms
Threat detection
Security automation
Application security
AI security
Attack simulation
Incident-response systems

Cloud & distributed systems

Microservices
Infrastructure
Real-time platforms
Scalable APIs
Distributed computing
Observability

Frontier technology

Quantum computing
Physical AI
Advanced simulation
Future computing systems
Emerging technologies

A real company example

“I want to build an autonomous electric delivery vehicle.”

That is not just a coding project. It is an engineering system. BuildOps can organize the digital engineering workflow around:

Requirements

Payload
Range
Speed
Operating environment
Safety requirements
Energy constraints
Communication requirements

System architecture

Vehicle system
Compute
Sensors
Software
AI
Networking
Cloud / edge infrastructure

Perception

Camera
LiDAR
Radar
Object detection
Segmentation
Sensor fusion

Localization

GPS
IMU
LiDAR / visual localization
Mapping

Planning

Route planning
Path planning
Obstacle avoidance
Decision making

Control

Steering
Braking
Acceleration
Trajectory tracking

Simulation

Virtual environment
Road scenarios
Weather conditions
Traffic scenarios
Edge cases

Testing

Functional testing
Performance testing
Failure testing
Scenario testing
Safety validation

Cybersecurity

Vehicle communication
APIs
Identity
Access control
Network security
Secure updates

Evaluation

Accuracy
Latency
Reliability
Safety metrics
Performance
Failure rate

Deployment

Edge systems
Cloud services
Vehicle software
Monitoring

Operations

Telemetry
Alerts
Failures
Performance
Updates
Continuous improvement

Evidence

Architecture
Experiments
Test results
Evaluation results
Decisions
System versions
Engineering history

BuildOps does not claim to replace every specialized vehicle, simulation, manufacturing or certification system. It provides the engineering infrastructure and connected workflow around the digital engineering work.

Every company can start differently

Same platform. Different engineering problem.

“We need to build a new security system for our company.”

BuildOps can organize

AssetsThreat ModelRequirementsArchitectureSecurity ControlsBuildAttack SimulationTestingMonitoringDetectionIncident ResponseEvaluationImprovement

Security is not added at the end. Security becomes part of the engineering lifecycle.

“We are building an autonomous AI agent.”

BuildOps can organize

ModelAgentToolsPermissionsDataEnvironmentMemoryExecutionMonitoringEvaluationSecurityHuman approvalDeploymentContinuous evaluation

The question is no longer simply “Does the model generate a good answer?” It becomes: can the entire agent system perform its task reliably, safely and within its permissions?

“We need real-time fraud detection.”

BuildOps can organize

Business ProblemRequirementsDataFeature EngineeringML ModelReal-Time APIDatabaseAuthenticationPerformance TestingFraud ScenariosEvaluationDeploymentMonitoringContinuous Improvement

The same platform adapts to a completely different engineering problem.

“We want to automate our factory.”

BuildOps can organize

ProcessRequirementsSensorsIndustrial SystemsRoboticsComputer VisionEdge ComputingControlSimulationSafetyTestingDeploymentMonitoringOptimization

The engineering workflow changes because the problem changes.

“I want to explore a new technology.”

BuildOps can organize

QuestionHypothesisExperimentArchitecturePrototypeDataset / EnvironmentBenchmarkEvaluationReproductionComparisonImprovementEvidencePublication / Productization

Research does not have to stop at an experiment. The experiment can become an engineered system.

Built for every builder

Built for every builder.

Whether you’re a student, fresher or experienced engineer, BuildOps helps you grow, build and create demonstrable work.

Students

Build your skills. Create real proof.

Go beyond academic projects. Build systems, solve real problems, deploy and receive structured evaluations.

Explore for Students

Freshers

Prove your engineering ability.

Don't wait for your first job to become an engineer. Showcase real projects and engineering decisions.

Explore for Freshers

Experienced

Build advanced solutions. Create more value.

Work on complex systems, evaluate architecture, experiment with new technology and demonstrate depth.

Explore Engineers

AI & Research Teams

Experiment with models. Evaluate agents.

Benchmark systems. Test safety. Measure reliability. Publish findings.

Explore AI & Research

Organizations

Create engineering workspaces. Post real problems.

Evaluate solutions. Benchmark teams. Discover engineering capability.

Explore Enterprise

Role by role

Student

Build your first real engineering system.

Move beyond Course → Assignment → Certificate → College Project toward Problem → Requirements → Architecture → Build → Test → Deploy → Evaluate. Learn how engineering actually works.

Fresher

Don't just show what you studied.

Show what you can engineer. Build evidence of architecture, debugging, testing, security, performance, deployment and real-world decision making. Become production-oriented.

Developer

Build beyond the code.

Connect requirements, architecture, code, infrastructure, testing, security, performance, deployment and observability. Build systems, not isolated features.

Senior engineer

Engineer at system scale.

Architect complex systems. Evaluate trade-offs. Compare approaches. Improve reliability. Analyze performance. Lead engineering decisions. Create evidence that teams can understand and act on.

AI engineer

Engineer the AI system — not just the model.

Models, agents, tools, data, evaluation, security, reliability, latency, cost, deployment and monitoring. AI is the technology. Engineering makes it usable.

Robotics engineer

Build. Simulate. Test. Evaluate.

Sensors, perception, localization, planning, control, simulation, scenarios, safety and performance. Connect digital engineering with the physical system.

Researcher

Turn experiments into engineering evidence.

Explore. Experiment. Benchmark. Reproduce. Compare. Improve. Publish. Build the evidence behind the result.

Startup

Turn the idea into a system.

Problem → Prototype → Architecture → Product → Testing → Security → Deployment → Operations → Improvement. Move from idea to engineered product.

Enterprise

One engineering infrastructure. Many problems.

Different teams, technologies, projects, security requirements and workflows. BuildOps gives the organization a common engineering foundation while individual projects adapt to their own requirements.

Engineering

Engineering is bigger than coding.

Coding is one part. Engineering also includes requirements, architecture, system design, testing, security, performance, scalability, reliability, deployment, monitoring, documentation, trade-offs, communication, ownership and decision-making.

DISCOVERUNDERSTANDDESIGNBUILDTESTDEPLOYOPERATEEVALUATEDOCUMENTPROVE

What engineering includes

Requirements

Write down what must be true before building.

Open guide

Architecture

Pick components and boundaries.

Open guide

System Design

Model load, data and failure.

Open guide

Implementation

Readable, reviewable code.

Open guide

Testing

Unit, integration, load.

Open guide

Security

Threat model, secrets, access.

Open guide

Performance

Budgets, profiling, p95.

Open guide

Scalability

What breaks at 10x.

Open guide

Reliability

Retries, timeouts, recovery.

Open guide

Deployment

Repeatable, reversible releases.

Open guide

Monitoring

Metrics, logs, alerts.

Open guide

Documentation

So others can run it.

Open guide

Trade-offs

Say what you gave up.

Open guide

Communication

Explain decisions clearly.

Open guide

Ownership

Operate what you ship.

Open guide

Decision-making

Record why, not just what.

Open guide

Engineering Problems

Practice the full lifecycle.

Systems

Design trade-offs.

Security

Threat model.

AI & Agents

AI can generate. Engineering must evaluate.

Section

AI Models

Compare models on your task.

TrainingServing

Section

AI Agents

Build agents with bounded permissions.

ToolsPlanning

Section

Agent Evaluation

Repeatable agent test runs.

RunsFailures

Section

Model Evaluation

Accuracy, latency, cost.

Benchmarks

Section

AI Safety

Test refusal and misuse.

Red-team

Section

AI Security

Sandbox and permission tests.

Injection

Section

Inference

Throughput vs cost.

BatchingQuantization

Section

Multimodal AI

Evaluate per modality.

VisionAudio

Section

Agentic Systems

Reliability under failure.

Orchestration

Section

AI Infrastructure

Operate model services.

GPUsServing

How work moves through AI & Agents

Pick a problemDesignBuildTestEvaluatePublish evidence

Evaluated on

ArchitectureCode QualityTestingSecurityPerformanceScalabilityReliabilityDeploymentDocumentationEngineering Decisions

Recent activity (sample)

New submission opened for review
Benchmark rerun with a fixed seed
Architecture decision recorded
Discussion thread marked answered

Good to know

All scores here are demo data, assessed against a sample rubric.
Failures are logged as evidence, not hidden.
Eligible evidence can later connect to Splexus Redume.

AI & Frontier Labs — Continue in AI & Frontier Labs
Evaluations — Continue in evaluations
Security — Continue in Security

For students and freshers

The world doesn’t need more certificates alone.

It needs people who can contribute to engineering systems. BuildOps helps students move from:

LearningBuildingTestingDeployingEvaluatingProving

Fresher ≠ “no experience”

A fresher can still demonstrate:

ArchitectureDebuggingTestingSecurityDeploymentPerformanceEngineering decisionsReal system thinking

Build the evidence before the job title.

Production-oriented engineering

AI can write code. Engineers still have to engineer the system.

Modern coding agents can already plan, edit code, execute commands and verify software tasks. That makes engineering judgment even more important. The engineer must understand:

What should be built?
Why should it be built?
What architecture makes sense?
What could fail?
How should it be tested?
How should it be secured?
How should it be evaluated?
How should it operate in the real world?

AI is the tool. Engineering is the skill.

BuildOps + AI

Don’t fight AI. Engineer with it.

AI can help generate code, analyze data, design experiments, explore solutions and automate repetitive work. BuildOps provides the engineering context around that work.

Human

Defines objectives, constraints, decisions and accountability.

AI

Assists with analysis, implementation, experimentation and automation.

BuildOps

Connects the engineering workflow.

Evaluation

Determines whether the result actually works.

Evidence

Shows what happened.

Students & Freshers

Worked examples: engineering from problem to deployment.

Starter to hard

iPhone Flash Sale

10,000 iPhones, 50,000 users: never oversell. Concurrency, idempotency, payments, load test.

Open the full example Very hard

Ride Booking Platform

Request, match, track and pay. Geo index, atomic driver matching, surge pricing, payment saga, failure recovery.

Open the full example Frontier

Humanoid Robot

Parts, neural architecture, balance control, learning and safety, with tested code.

Open the full example Every engineer

Secure Development Playbook

Where to start, where to run each check and what done looks like, from threat model to incident response.

Open the full example

Students

Build your skills. Create real proof.

You don’t need years of experience to start building real engineering capability. Go beyond academic projects.

Explore Problems Freshers

A simple path

Pick a beginner challengeBuild itAdd testsDeployGet evaluatedKeep the evidence

Week 1

Read requirements and sketch the architecture.

Week 2

Implement the core API and database.

Week 3

Add tests, security checks and deployment.

Week 4

Write up decisions and submit for evaluation.

What you can demonstrate

ProgrammingAPIsDatabasesAITestingSecurityDeploymentArchitectureDocumentation

BuildOps for freshers

Don’t wait for your first job to become an engineer.

Build production-oriented engineering capability before you enter the workforce.

Why traditional fresher profiles are not enough

Traditional

Degree
Certificates
Academic projects
Course completion
Interview preparation
Coding scores

BuildOps

Real systems and architecture
APIs and databases
Testing and security
Deployment, performance, monitoring
Documentation and decisions
Evaluation evidence

From college project to production-oriented engineering

ProblemRequirementsArchitectureImplementationTestingSecurityDeploymentMonitoringEvaluationEvidence

Starter projects

Every starter project includes: requirements (functional + non-functional targets), an architecture diagram and trade-offs, tasks (implement, document, review), tests (unit, integration, load), containerized staging deployment, and an evaluation across Architecture, Code Quality, Testing, Security, Performance and more.