VeUP Labs · Frontier Factory · v2.0
The Frontier Factory is how VeUP builds. Engineer-directed agent swarms run AI-DLC from business intent to verified production systems in days. Everything on this page shipped through it.
Method · AI-DLC
AWS's AI-Driven Development Lifecycle makes AI a member of the team, not a tool. Agents propose the plans, the code, and the tests. Engineers hold decision authority at every gate. We run it across our whole delivery organization, in bolts, not sprints: cycles measured in hours and days.
Mob Elaboration turns business intent into units of work in one session, not a quarter of grooming. The whole team validates the AI's questions live. Context is the first deliverable.
Parallel agent swarms take units through architecture, code, and tests. Every unit lands as a reviewed pull request. Adversarial review swarms attack the work before humans see it.
Agents drive infrastructure as code and deployments using the context the build accumulated. Humans watch the gates. Evidence is captured as it happens, not reconstructed later.
For the builders: harness-agnostic by design. Compatible with awslabs/aidlc-workflows adaptive workflows, Kiro steering files, and Amazon Q rules. Ours runs on issue-graph work queues, parallel agent fleets, PR-per-unit discipline, and verification gates that fail loudly.
Exhibit 00 · Internal MSP platform
Our managed-services control plane is AWS-native CDK. Step Functions onboarding, cross-account IAM with unique external IDs, a Security Hub and GuardDuty baseline, Bedrock Guardrails on every AI action, and a SOC 2 evidence pipeline.
One engineer directed the swarm that built it. The team that operates it runs on the factory that produced it. That is the point.
Commit one was the spec. Working infrastructure code followed nine minutes later.
Twenty-eight commits landed in a single hour on night one. All of it from git history, not a press release.
Batch 01 · Media intelligence pipeline
Built for a broadcast sports media platform: frame-level tagging on football footage, automatic clip generation, direct delivery into the customer's media asset manager. The same hardened pipeline now runs content moderation in production for a consumer social platform (the anonymized case study is public). Re-pointed domain layer. Nothing rebuilt.
Frame-accurate tagging at broadcast scale is a throughput problem, a retry problem, and a normalization problem long before it is a model problem. The pipeline absorbs that engineering once. Petabyte-scale archive runs on S3 and Athena behind it.
Batch 02 · Discovery platform
Three workflows in production: environment auto-discovery, live network analysis, and a discovery-call-to-security-checklist generator. Environment discovery that used to take weeks of manual work now finishes before the first call ends.
Scan an AWS account, output a complete architecture diagram. Mapped across every account in a production customer environment with zero manual diagramming.
Pick an account and a time window. The platform maps live traffic, then generates a full security analysis on top of it in one click.
Read the customer's own words from the discovery call. Output a deployable security checklist. Conversation becomes engineering artifact, automatically.
Discovery input is unbounded: accounts, VPCs, flow logs, transcripts. Compressing it into a correct, deployable artifact takes verification gates, not summaries. Every artifact ships with its evidence attached.
Batch 03 · VeUP Mesh
Live at mesh.engineering.veup.com. Demonstrated live at Game Developers Conference 2026. Runs on AWS GPU infrastructure and scales on demand.
Football tagging proved the pipeline. Discovery proved LLM-as-glue. Mesh proves the line runs heavy GPU media workloads end to end on AWS.
Next sockets on the roadmap: broadcast digital twins, site-scan to BIM, and product photography to AR assets. Each is a market with proven demand, not a research project.
Run it · two ways
The factory is not a demo reel. It is a delivery engine you can put to work in two ways, and both come with the same rule: work only counts when it is verified.
We embed and deliver. The same engineers who scope it ship it, at swarm cadence, and we bill by what ships.
We install the engine in your estate: the AI-DLC method, the context plane, the work queues, the verification gates. Your engineers direct it from day one.
Consultants leave documents. We leave installed, verified capability.
Named case studies, reference architectures, and production numbers are public. The line is running. Come watch a unit go through the gates.