Map
Model the real system
Codebases, APIs, documents, operators, hardware, tests, and production rules become the working map.
Autonomous engineering for mission-critical systems
AI systems for mission-critical work.
Zerg works with teams whose products depend on hard technical systems: hardware, simulations, data and control planes, vendor integrations, migrations, and production workflows. We model the environment, build the software, validate against real signals, and keep it operating as the system changes.
Operating loopLive
Build
Generate and modify software
Zerg writes integrations, internal tools, migration code, test harnesses, and operational applications.
Prove
Validate against the environment
Unit tests are only the start. We validate against live services, simulators, devices, and production signals.
Run
Keep watching after delivery
When requirements, APIs, or dependencies drift, Zerg detects the change and creates the follow-on fix.
24/7
continuous runtime
signals
system validation
owned
code and operations
Mission
Build where normal software teams stall.
01
Integrations that do not stay simple
Data connectors, vendor APIs, brittle workflows, and glue code that need ongoing attention after they ship.
02
Internal tools with real operational stakes
Dashboards, review queues, command surfaces, and workflows for teams whose work cannot live in spreadsheets.
03
Migrations and remediation at production scale
Framework migrations, dependency changes, CVE remediation, and code modernization with verification loops.
04
Hardware and field systems
Agents that work against devices, simulators, bench rigs, logs, and signal chains instead of screenshots.
Runtime
The product is the operating loop.
We do not stop at prompt-to-code. Zerg maps the real environment, builds the software, validates against real signals, and keeps watching after delivery so drift becomes a fix path, not a fire drill.
01
Specify
We capture the job, constraints, interfaces, failure modes, and the definition of done.
02
Instrument
Zerg gets the tools it needs: repos, services, documents, test fixtures, connectors, and runtime access.
03
Generate
The system plans, edits, tests, and produces artifacts that can be reviewed, deployed, or kept running.
04
Operate
Monitoring, agent loops, and human review keep the software useful as requirements and dependencies move.
Connectors
AI with hands on your actual systems.
Connectors are first-class work, not afterthoughts.
Email, files, calendars, ticketing systems, CRMs, warehouses, vendor APIs, cloud infrastructure, and custom hardware can all become part of the operating surface.
Agents need permissions, memory, and verification.
The hard part is not generating code once. It is giving the system safe access, context, tests, and the ability to recover when the outside world changes.
Your process becomes software.
Zerg turns repeated operational work into tools, automations, agent workflows, and review loops that your team can own.
Proof
Ship the work that used to be uneconomic.
The useful AI frontier is not another chat box. It is software that can touch fragmented APIs, inspect messy repos, test against real infrastructure, and keep improving when the world changes underneath it.
2-3/week
Production integrations
A platform team can ship and maintain integrations at a cadence that used to require a much larger team.
24 days
Large migration
Production JavaScript to TypeScript migration with tests and reviewable checkpoints.
signals
Signal validation
Hardware-in-the-loop workflows for systems where correctness depends on real signals.
Use cases
Where we are strongest.
Zerg works best when the problem has real interfaces, live state, and expensive correctness requirements. The more operational context there is, the more leverage the system has.
Data connectors
Build and maintain integrations across vendor APIs, warehouses, documents, and internal systems.
Operations apps
Create internal tools that combine workflow, state, permissions, and AI assistance.
Code modernization
Migrate frameworks, remediate vulnerabilities, and keep legacy systems moving.
Data pipelines
Generate and operate ETL, validation, reconciliation, and monitoring loops around changing data sources.
Build with Zerg