Move approved work into a bounded software candidate without losing the context and controls that make the change understandable.
An AI engineering team that develops software end-to-end, under governance.
From an approved business requirement, IncrementPilot’s specialized agents plan, architect, design, build, test, independently review, remediate, and qualify real software changes against your repositories and environments.
Your team retains authority over material scope and release.
Agents plan and designRequirements, architecture, experience design, and delivery boundaries
Agents build and challengeFrontend and backend code, testing, independent review, and remediation
Humans govern deliveryA qualified software candidate with material scope and release authority retained by your team
AI AGENT TEAM RUN / IP-042GOVERNED
Approved business requirementAdd customer request managementOne agent-driven path from requirement to qualified candidate
01
Plan & designNova / Orion / Iris · requirements, architecture, experience
READY02
Build the softwareForge / Pixel · backend and frontend implementation
DONE03
Independently challengeRex / Shield / Veil / Integration · review and test
RUNNING04
Remediate findingsForge / Pixel · evidence-driven corrective changes
AS NEEDED05
Qualify & prepare deliveryDrift / Gate · governed evidence and candidate handoff
GATEDCandidateBound
Review roles4
ReleaseHuman
Exact repository identity
Policy-bound tools and paths
Independent review
Fail-closed delivery gates
What it delivers
Expand engineering capacity with an AI team that can actually execute the lifecycle.
IncrementPilot is designed for product and engineering leaders who want AI to do meaningful delivery work while keeping the software process inspectable, bounded, and reviewable.
Separate implementation from QA, integration, code, and security review so generated work cannot approve itself.
Keep candidate identity, test and review evidence, qualification state, and the human release decision connected.
Delivery capabilities
How IncrementPilot supports those outcomes across the software change lifecycle.
Move from intent to implementation
Connect business outcomes, stories, acceptance criteria, architecture constraints, and delivery plans so implementation starts from governed context.
Produce real software changes
Work against actual repositories, branches, environments, codebases, and delivery rules rather than isolated code-generation prompts.
Challenge the change independently
Separate implementation from code review, QA, integration, and security review so generated work cannot approve itself.
Remediate with evidence
Use reviewer findings, validation evidence, and bounded corrective loops to improve a candidate without turning remediation into uncontrolled regeneration.
Deliver with proof attached
Bind the candidate, test evidence, reviewer verdicts, qualification results, and release decision to the exact software change being delivered.
Refuse unsafe or ambiguous work
Stop when scope, identity, evidence, policy, cost, or review requirements are not satisfied instead of pushing a low-confidence change forward.
How it works
A governed delivery loop from approved work to a reviewable release candidate.
IncrementPilot’s agents execute the work while the platform governs evidence and decision gates — rather than treating AI generation as the end of the process.
Understand the work
Resolve repository identity, product context, story intent, acceptance criteria, dependencies, policies, and execution boundaries.
Generate a bounded candidate
Create the change inside the approved repository, workspace, file scope, tool permissions, and delivery plan.
Evaluate and review
Check the diff and run separate code, QA, security, and integration review against the exact candidate.
Repair only what failed
Carry unresolved findings forward, make controlled corrections, and re-run real evaluation until the candidate passes or the bounded loop stops.
Qualify and prepare delivery
Require the right execution and application evidence before creating a governed pull request or handing the release decision back to people.
Specialists within the workflow
The five-stage lifecycle stays simple. The right agents join where their expertise is needed.
IncrementPilot does not run 17 agents in a line. It activates the appropriate specialists for the work, keeps implementation and independent challenge separate, and preserves human authority over material scope and release.
Specialists do the workPlanning, design, build, quality, security, and delivery each have focused responsibilities.
Implementation does not approve itselfIndependent reviewers challenge the exact candidate and its evidence.
Humans retain consequential authorityAgents prepare work, evidence, and recommendations; people control material decisions and release.
UNDERSTAND & PLAN
Clarify intent and set boundaries
Atlas, Petra, Nova, Sage, Orion, Iris
BUILD
Create the bounded candidate
Forge, Pixel
EVALUATE & CHALLENGE
Test and independently scrutinize
Veil, Rex, Shield, Integration
REMEDIATE
Repair evidence-backed findings
Forge or Pixel, with the relevant reviewers
QUALIFY & PREPARE DELIVERY
Prepare a governed handoff
Drift, Gate
Control which reviewed skills and qualified tools the delivery team is allowed to use.
Connects support needs and production signals back into evidence and the next delivery cycle.
Meet the named specialists
Stable identities make responsibility, evidence, handoffs, and separation of duties visible.
ORCHESTRATION
Atlas
Technical delivery orchestrator
Coordinates runtime execution, governed handoffs, evidence, and lifecycle gates for approved work.
PRODUCT
Petra
Product ownership assistant
Supports business prioritization, scope, and acceptance recommendations while product authority remains human.
PRODUCT
Nova
Business analyst
Turns business needs into clear stories, acceptance criteria, and questions that need resolution.
PLANNING
Sage
Planning and flow facilitator
Maintains sequencing, progress, dependencies, and blockers across the approved delivery plan.
DESIGN
Orion
Architecture specialist
Defines the technical shape and boundaries that keep a change coherent with the wider system.
DESIGN
Iris
UI design specialist
Shapes user-facing journeys and design intent before implementation turns them into an interface.
BUILD
Pixel
Frontend developer
Builds the customer-facing interface within the approved user experience and delivery boundaries.
BUILD
Forge
Backend developer
Builds the services, data, and integration work required for a complete software change.
FUNCTIONAL QA
Veil
Story and acceptance QA
Tests intended behavior against the story and acceptance criteria before delivery can proceed.
INDEPENDENT REVIEW
Rex
Code reviewer
Challenges code quality, requirement alignment, and maintainability without reviewing its own work.
INDEPENDENT REVIEW
Shield
Security reviewer
Examines security, authorization, dependencies, and sensitive-data risks before promotion.
SYSTEM QA
Integration
Integration test specialist
Verifies cross-component and system behavior beyond an isolated story or component.
DELIVERY
Drift
Deployment specialist
Prepares controlled environments and deployment evidence for an approved release path.
DELIVERY
Gate
Release-readiness gatekeeper
Assesses release evidence and readiness while consequential production authorization remains human.
FEEDBACK LOOP
Echo
Support specialist
Connects production signals, incidents, and support needs back to the delivery process.
CAPABILITY GOVERNANCE
Lumen
Skill curator
Governs reusable delivery skills so learned practices are reviewed before they are reused.
CAPABILITY GOVERNANCE
Volt
Tool curator
Governs the tools agents may use so delivery remains within approved capabilities and policies.
Where it fits
Useful when delivery is too important for a code-generation shortcut.
Feature delivery
Carry a well-defined product story from approved scope through implementation, review, remediation, and preparation of a governed pull request when evidence passes.
Modernization work
Change existing codebases while preserving repository boundaries, architecture constraints, tests, and release evidence.
Cross-layer changes
Coordinate UI, API, data, configuration, and integration work where a change must remain coherent across multiple parts of the product.
Enterprise AI delivery
Use AI inside a controlled software lifecycle with explicit identity, permissions, review independence, auditability, and human release authority.
Enterprise assurance
The control layer is part of the product, not an afterthought.
IncrementPilot is built around evidence-bound delivery. The same workflow that creates software also records why the work was allowed, what changed, what passed, what failed, and who still owns the release decision.
ControlWhat it protects
Exact identityRepository, branch, candidate, actor, and evidence binding
Bounded executionApproved tools, paths, budgets, workspaces, and runtime authority
Independent reviewImplementation cannot silently approve its own result
Evidence-backed gatesDelivery requires current validation and qualification evidence
Human release authorityAI can prepare a release candidate; people retain consequential promotion control
Deployment model
Designed to work with enterprise repositories, runners, policies, and environments.
The delivery path is intended to integrate with existing engineering controls rather than replace them with an opaque autonomous runtime.
Work against real source control
Bind work to exact repository and commit identities, preserve branch policy, and deliver through governed Git workflows.
Use controlled customer runners
Execute approved commands and validations inside bounded workspaces with explicit leases, tool policy, and evidence receipts.
Use the right model for the phase
Route generation, review, and remediation independently so higher-cost reasoning can be applied only where it adds value.
Emporia IT product portfolio
Extend governed delivery with investigation and product intelligence.
IncrementPilot is designed for the delivery path. The companion products help teams investigate what changed and maintain a trustworthy view of the product estate.
INVESTIGATEExplore CauseSignal →
UNDERSTANDExplore FoundryTrace →
CauseSignal
Reconstruct incidents, defects, regressions, and risky changes with evidence, competing hypotheses, confidence, and accountable next actions.
FoundryTrace
Establish a current product record across requirements, implementation, releases, deployed reality, evidence gaps, and conflicts.
Enterprise briefing
See how governed AI delivery fits your engineering environment.
Discuss repository boundaries, delivery controls, review policy, deployment architecture, and qualification approach.
info@emporiait.com