01
Guided project intake
Captures project identity, business goal, build type, current system context, stage, priority, and buyer role.
Clear project signal before review
AI Software Factory for Production Software Delivery
Estimate with AI, validate with senior architects, and deliver through a controlled engineering system that connects agents, admin workflows, client visibility, QA, release, and documentation.
Enterprise intake, not loose forms
Capture scope, risks, integrations, budget signal, and delivery assumptions in one controlled flow.
Delivery system after estimate
Qualified work moves into admin review, agent-assisted planning, client visibility, and controlled execution.
Delivery controlLive project
Active engagement
Architecture sprintAI commerce operations platform
Estimate approved Week 2 of 3
Agreed value$82k On budget
Scope locked92%Approved baseline
Delivery healthOn planNo blocked work
Risk levelLow2 flags resolved
Delivery planArchitecture is the active approval gate
Updated liveScopeApproved100%
ArchitectureIn progress68%
BuildReady next18%
Quality assuranceQueued--
LaunchScheduled--
8 agents connectedScope, PM, QA and DevOps relayLive
Client workspace syncedBudget, phases and requestsReady
Enterprise delivery controls from estimate to handoff
Governed AI deliverySenior architecture reviewAdmin + client operating layerAgent efficiency telemetryRelease and handoff controls
13+
Years of software delivery
200+
Engineers across delivery teams
60 sec
First AI scope signal
8
Specialized delivery agents
Estimate Features
What /estimate gives before delivery starts.
The estimate flow is the product front door. It turns a visitor's idea into project context, scope signal, delivery ranges, implementation shape, and an execution-ready handoff.
Inputs captured
Profile, stage, scope, features, integrations, devices, cloud
Outputs generated
Cost, timeline, tiers, team, phases, risk, assumptions
Next action
Architect review, commercial alignment, onboarding, execution
02
Adaptive discovery
Adjusts questions around profile, project stage, pain points, market signals, business impact, and technical depth.
Less generic, more relevant scope
03
Feature and integration mapping
Uses catalog search, AI recommendations, platforms, integrations, devices, design needs, and infrastructure choices.
Feature scope connected to real systems
04
Cost and timeline ranges
Generates delivery tiers with cost ranges, timeline ranges, team size, hours breakdown, and recurring infrastructure signals.
MVP-first, standard, fast-track, priority
05
Delivery plan shape
Shows team structure, delivery phases, assumptions, exclusions, risk score, confidence level, and architecture signals.
Implementation path, not a loose quote
06
Execution-ready handoff
After contact validation, creates the executive summary, risk analysis, technology recommendation, delivery roadmap, and review package.
Ready for review and delivery planning
Unified StackLift Platform
One front door. One delivery operating system.
StackLift turns a broad delivery platform into one product path: capture the estimate, govern the scope, validate architecture, and move qualified work into controlled execution.
Front-door intelligence
Estimate Engine
Capture project intent, integrations, timeline pressure, budget signal, and scope complexity before commercial commitment.
Adaptive intake
Cost and timeline range
Risk and scope map
Delivery infrastructure
AI Software Factory
Turn approved scope into engineering phases, tickets, documentation, QA paths, deployment steps, and client-ready delivery updates.
Architecture-first delivery
Agent-assisted execution
Release controls
Controlled automation
Agent Command System
Specialized agents support scoping, architecture, code, QA, DevOps, docs, review, and PM workflows under human ownership.
Defined agent roles
Efficiency tracking
Human approval gates
Transparent delivery
Client Command Center
Clients can see budget, timeline, phases, requests, invoices, releases, and updates without waiting for manual status reports.
Live project state
Actionable updates
Change request control
Enterprise Control Plane
Operational control from estimate to release.
The stronger product-company presentation is not more decoration. It is visible governance: intake quality, agent accountability, delivery telemetry, and client-ready state across the platform.
Intake
Structured project signal
Review
Architecture and budget validation
Execute
Agent-assisted delivery workflow
Operate
Client, admin, release, and handoff control
Governance
Controlled intake and review
Every estimate routes through structured scope signals, risk checks, budget assumptions, and senior architecture validation.
Estimate -> architect review -> approved delivery path
Agent Ops
Agent hierarchy with human ownership
Master, leader, and specialist agents can support delivery tasks while approvals, outputs, and efficiency stay visible to admin.
Defined roles, review gates, efficiency tracking
Telemetry
Delivery health in one operating layer
Budget, timeline, scope, phase progress, requests, releases, invoices, and notifications are treated as connected product data.
Shared source of truth across admin and client
Client Layer
Actionable client visibility
Client surfaces are designed to reflect admin decisions instantly and send users to the exact project context that needs action.
Live project state, relevant redirects, clear next steps
How StackLift Works
From first estimate to production operation.
Projects enter through the estimate, but the journey continues through review, commercial approval, onboarding, execution, QA, release, client visibility, and ongoing operation.
Start Estimate01
Estimate
The visitor submits project context through `/estimate`; StackLift turns it into scope, risk, cost, and timeline signals.
02
Architect review
A senior architect reviews the AI output, adjusts assumptions, and confirms the technical path before commitment.
03
Commercial alignment
The estimate becomes an agreed scope, proposal, contract, budget, and delivery commitment with clear approval gates.
04
Plan and onboard
StackLift prepares phases, ownership, access, dependencies, kickoff readiness, and the client delivery workspace.
05
Build and validate
Senior engineers and specialized agents execute work items with architecture, QA, documentation, and release controls.
06
Release and operate
Releases, approvals, invoices, updates, support, and project health remain visible across admin and client surfaces.
Platform Capabilities
A productized operating layer for software delivery.
StackLift supports broad delivery needs, but each module maps back to a visible operating outcome: estimate, build, automate, modernize, integrate, and operate.
Explore Solutions01
Intelligent Scoping
AI-powered project intelligence that converts any requirement format into structured epics, risk maps, and delivery roadmaps in seconds.
60-second scopeAI risk analysisRoadmap generation
02
Orchestrated Delivery
A multi-layer AI engineering pipeline where senior engineers and specialized AI agents collaborate across every phase of delivery.
8 AI agentsSenior oversightParallel execution
03
Continuous Quality
Automated QA generation, security review, performance analysis, and release validation — built into every delivery cycle.
AI test generationSecurity reviewRelease gates
04
Operational Excellence
CI/CD automation, observability, monitoring, infrastructure as code, and scalable DevOps systems for every project.
CI/CD automationObservabilityIaC
AI Software Factory
Agents are useful only when the workflow is controlled.
StackLift presents agents as a managed delivery layer, not a loose collection of bots. Each agent has a job, output, and review boundary.
01
AI Intake & Scoping
Requirements, PRDs, and ideas are parsed into structured epics, modules, risk maps, and delivery scope in seconds.
Scope AgentPM Agent
02
Architecture Design
System design, API boundaries, data models, cloud topology, security architecture, and scalability planning.
Architecture Agent
03
Engineering & Build
Senior engineers with AI-assisted code generation, automated code review, and implementation acceleration.
Code Agent
04
Quality Assurance
AI-generated test coverage, edge case analysis, regression testing, security scanning, and release validation.
QA AgentReview Agent
05
DevOps & Deployment
CI/CD pipelines, environment configuration, observability setup, infrastructure as code, and deployment automation.
DevOps Agent
06
Documentation & Handoff
Technical documentation, API references, user guides, release notes, and complete engineering handoff materials.
Documentation Agent
Trust, Security, Control
Enterprise controls around every AI-assisted workflow.
Serious projects need visible delivery control, human review, source ownership, project transparency, and release discipline before budget is committed.
Security-first delivery
Access control, audit-friendly workflows, source ownership, handoff discipline, and release gates are part of the operating model.
Human-owned AI
Agents speed up analysis and execution, but senior engineers own architecture, production changes, client commitments, and risk decisions.
Transparent project control
Budget, timeline, phases, change requests, invoices, and delivery updates are visible across admin and client surfaces.
Use Cases
Use cases mapped to delivery outcomes.
Each use case helps a visitor recognize their project type, understand the operating outcome, and move into the estimate flow with context.
AI Transformation
Architect and implement AI-native capabilities into existing systems — copilots, intelligent agents, RAG pipelines, and workflow intelligence.
AI copilot deployment
LLM integration
Intelligent workflows
Platform Modernization
Migrate legacy systems to modern cloud-native architectures without disrupting current operations.
Zero-downtime migration
Architecture re-platforming
Tech debt elimination
Enterprise SaaS Build
Architect and deliver large-scale SaaS platforms with multi-tenant architecture, enterprise security, and infinite scalability.
Multi-tenant architecture
Subscription billing
Enterprise security
Workflow Automation
Replace manual operations with intelligent automation — approval flows, ERP integrations, and operational dashboards.
Process automation
ERP integration
Operational dashboards
Data Engineering
Build scalable data pipelines, analytics platforms, and AI-powered intelligence systems for data-driven decision making.
Data pipelines
Analytics platform
AI-powered reporting
Cloud-Native Engineering
Design and implement distributed, cloud-native architectures with DevOps, CI/CD pipelines, and infrastructure automation.
Cloud migration
Microservices
Infrastructure automation
Delivery Proof
Proof that reads like operating data.
Every proof card shows what matters: project type, scope size, timeline, and the operational result delivered.
4
Outcome snapshots3-8w
Typical delivery bands29-64
Story-point rangeAI document workflow
64
ptsRFP intelligence system
Moved proposal review from ad hoc reading to structured qualification.
Legacy business system
51
ptsERP approval modernization
Converted an email-driven process into a trackable internal module.
AI assistant
47
ptsCustomer support AI copilot
Gave support teams a controlled AI workflow with human review and confidence scoring.
Production hardening
29
ptsAI-coded founder prototype rescue
Converted a promising prototype into a release-ready engineering backlog.
Technology Ecosystem
The stack behind the delivery system.
The ecosystem demonstrates that StackLift can connect delivery work to the systems teams already use.
Cloud Platforms
AWSGoogle CloudAzureVercelCloudflare
AI & Machine Learning
OpenAIAnthropicLangChainPineconeHugging Face
Databases & Storage
PostgreSQLMongoDBRedisSupabaseNeon
Frameworks & Runtimes
Next.jsReactNode.jsPythonFastAPI
Commerce & Payments
ShopifyStripePayPalSquareBraintree
DevOps & Observability
GitHub ActionsDockerKubernetesTerraformDatadog
Agent Ecosystem
Specialized agents, not a generic AI promise.
Each agent supports a real task and improves a measurable delivery workflow, from scoping and QA to DevOps, documentation, review, and project reporting.
Explore Agent EcosystemScope Agent
Turns ideas, PRDs, and backlogs into epics, user stories, dependencies, and acceptance criteria.
Architecture Agent
Drafts system design options, data models, API boundaries, cloud topology, and security concerns.
Code Agent
Assists senior engineers with implementation patterns, boilerplate, refactors, and code checks.
QA Agent
Generates test cases, edge cases, regression checklists, and release validation paths.
DevOps Agent
Prepares CI/CD pipelines, deployment environments, observability setup, and rollback checklists.
Documentation Agent
Creates technical notes, release notes, user guides, and implementation handoff material.
Review Agent
Flags security vulnerabilities, performance regressions, maintainability issues, and quality risks before release.
PM Agent
Summarizes sprint progress, blockers, next steps, and produces client-ready delivery updates.
Common Questions
Enterprise delivery questions, answered.
Clear answers reduce friction before the estimate and help qualified projects move forward with the right expectations.
What types of companies does StackLift AI work with?
We work with startups, SMBs, mid-market companies, and enterprise organizations. Our delivery model scales from early-stage MVP builds to complex enterprise platform modernization and AI transformation initiatives.
How does StackLift AI differ from a traditional development agency?
Traditional agencies start with months of discovery, bill open-ended hours, and lack delivery predictability. StackLift AI operates as an AI-native engineering infrastructure — providing AI-powered scoping, structured delivery pipelines, outcome-based pricing, and senior engineering accountability.
Can StackLift AI handle enterprise-scale and complex architecture?
Yes. We architect and deliver large-scale SaaS platforms, AI-native applications, ERP integrations, cloud-native systems, distributed architectures, data engineering platforms, and long-term enterprise modernization programs.
What is the AI Software Factory model?
The AI Software Factory is our delivery infrastructure: a structured pipeline where senior engineers and 8 specialized AI agents collaborate across scoping, architecture, engineering, QA, DevOps, and documentation — creating repeatable, high-quality delivery at scale.
Do you offer long-term engineering partnerships?
Yes. Our AI Software Pod model provides ongoing engineering capacity — a dedicated team of senior engineers and AI agents working as an embedded delivery arm within your organization, with sprint-based delivery and roadmap execution.
How does pricing work for enterprise and complex projects?
All projects are priced around deliverable complexity using our story-point model — not open-ended developer hours. This means you get a predictable budget, timeline, and delivery path before any engineering begins.
Estimate-to-delivery operating system
Move from estimate to governed delivery.
Use guided intake to capture project context, review scope in admin, and move qualified work into StackLift's delivery operating system.
Structured intake. Architect review. Delivery control before commitment.