AIDLC-Based App Development Services
AI applications and agents designed, built, validated and deployed through one governed AI development lifecycle, on a trusted data foundation and a knowledge layer that grounds every answer, with measurable outcomes at every gate.
Quick answerAIDLC-based apps are AI applications and agents built through an AI development lifecycle (AIDLC) — a governed path from design to deployment. They rest on a trusted operational data layer and a knowledge layer that provides grounded enterprise context, reuse agents and engineering patterns across use cases, and clear governance gates so every solution conforms to your data, security and architecture principles. AIDLC supports two complementary approaches: a lifecycle for building AI applications and agents, and AI-assisted software delivery. We run them as separate tracks. See how they fit together.
Six building blocks of an AIDLC-based app
Each block closes a gap that stalls AI projects between pilot and production.
Governed, consistent data
AI applications need data that is consistent, quality-checked and access-controlled, with one definition of each business entity and clear lineage.
Operational data layer
A trusted data foundation over your systems of record, so apps and agents read the same governed data instead of building one-off pipelines.
AI SDLC
The lifecycle that controls how applications and agents are designed, built, validated and deployed, with entry and exit criteria at every stage.
Knowledge layer
Grounded enterprise context: documents, policies and domain knowledge indexed with permissions, so answers cite your sources.
Reusable agents & patterns
Agents, tools and engineering patterns packaged once and reused, so the second AI use case ships faster than the first.
Governance gates
Checkpoints that confirm each AI solution conforms to your data, security and architecture principles before it advances.
One lifecycle, with a gate at every handoff
Frame
Use case, success metric and risk tier, agreed before design starts.
Design
Architecture, data and knowledge needs, and the patterns and agents to reuse.
Build
Agents, retrieval and integrations, assembled on the operational data layer.
Validate
Evaluation for accuracy, groundedness and safety against agreed thresholds.
Deploy & evolve
Release through governance gates, then monitor and feed telemetry back in.
What changes when the app is AI
| Dimension | Traditional SDLC | AIDLC-based apps |
|---|---|---|
| Scope of control | Code and releases | Code, data, knowledge, prompts, agents and models |
| How quality is proven | Functional and regression tests | Evaluation sets for accuracy, groundedness and safety |
| Role of data | An input to the app | A governed foundation the app depends on |
| Reuse | Shared libraries and services | Reusable agents, tools and engineering patterns |
| Release control | Change approval | Governance gates tied to data, security and architecture |
Two complementary approaches, kept separate
AIDLC supports two complementary approaches. One governs what you build: AI applications and agents. The other changes how you build: AI-assisted software delivery, which AWS describes as the AI-driven development life cycle. We run them as separate tracks so neither dilutes the other.
AIDLC for AI apps and agents
Governs the AI application itself: its data, knowledge, prompts, models, agents and policies.
- Scope
- AI applications and agents only.
- Stages
- Frame, design, build, validate, and deploy and evolve.
- Gates
- Data, security, architecture and evaluation gates.
- Proves
- Accuracy, groundedness, safety and reliability in production.
AIDLC for AI-assisted delivery
AI plans and drafts the engineering work, and humans validate it. AWS introduced this approach in 2025 as the AI-driven development life cycle.
- Scope
- The delivery process for any application, AI or not.
- Phases
- Inception, construction and operations.
- Gates
- Humans validate the AI’s plans and output at each step.
- Proves
- Delivery speed, engineering quality and rework.
Complementary, never mixed
Use AI-assisted engineering to build faster. Use the AIDLC gates to decide whether what was built is fit to ship. Adopt either track on its own, or both together. Read AWS’s original article on the AI-driven development life cycle, or see how we apply AI-assisted delivery in AI Pods and GenAI Software Factories.
Separate scope
The apps track applies to AI applications and agents. The delivery track can apply to any software you build, AI or not.
Separate gates
AI-assisted delivery never bypasses a governance gate. Speed does not replace evaluation, data checks or sign-off.
Separate measures
Delivery metrics, such as cycle time and rework, are tracked apart from AI quality metrics, such as groundedness, safety and cost.
What sets an AIDLC-based approach apart
Many AI engineering offerings are strong at one layer, such as models, tooling or delivery speed. An AIDLC-based approach is built to hold up across all of them.
| Dimension | Typical AI engineering offering | AIDLC-based approach |
|---|---|---|
| Starting point | A model, a tool or a proof of concept | A business use case, a risk tier and the data it depends on |
| Data and knowledge | Left to the client, or rebuilt for each project | A governed operational data layer and knowledge layer shared across use cases |
| Proof of quality | Demos and spot checks | Versioned evaluation sets for accuracy, groundedness and safety, scored at every gate |
| Governance | A review near the end, or a separate policy document | Gates built into each stage and sized to risk |
| Reuse | Each use case is a new build | Reusable agents, tools and patterns, so later use cases start ahead |
| Ownership and lock-in | Often tied to one model, platform or vendor | Model- and cloud-agnostic, delivered as code you own |
| Delivery speed | Gains come from tools alone | AI-assisted engineering inside the gates, not instead of them |
What stands behind the approach
Data engineering included
Most AI projects need it. We treat data quality as part of model quality.
Platforms behind the team
RLCatalyst and Spectra accelerate build and run.
AWS-native delivery
Bedrock, SageMaker and GPU infrastructure within your account and controls.
Vendor-neutral, you own it
Model- and cloud-agnostic, delivered as code, models and infrastructure-as-code that belong to you.
Field-tested accelerators
Reusable accelerators for agentic automation across cloud, data and operations.
AI-assisted delivery
AI Pods bring AI-augmented engineering to the build, inside the gates and not instead of them.
Evidence you can hold us to
Two kinds of proof: the delivery results Relevance Lab already publishes, and the scorecard every AIDLC-based app is measured against, shown below.
Figures as published by Relevance Lab at relevancelab.com. Results vary by engagement, data readiness and scope.
What every AIDLC-based app is measured on
We agree target values with you at the Frame stage and report against them at every gate, so success is defined before the build starts.
| Outcome | How it is measured | Where it is checked |
|---|---|---|
| Answer quality | Accuracy and groundedness scores on a versioned evaluation set | Validate gate |
| Safety and compliance | Pass rate on policy, prompt-injection and data-leakage tests | Validate and deploy gates |
| Time to value | Days from approved use case to first governed release | Each release |
| Reuse | Share of each new app built from existing agents, tools and patterns | Design gate |
| Cost control | Cost per request and per user, against the agreed budget | Deploy and evolve |
| Human effort | How often people must intervene or correct the output | Production monitoring |
Adopt AIDLC at your own pace
Lifecycle assessment
We review your current SDLC, data and governance against the six building blocks and pick the first use case.
Gated pilot
One use case end to end: a slice of the data layer, the knowledge layer, an agent, evaluation and governance gates.
Reusable platform
Turn the pilot into agents, patterns and gates that every later use case inherits.
- AWS
- DataStax
- Salesforce
- Snowflake
Need the product built, too?
AI Application Development Services design and ship the AI-powered product that runs on an AIDLC foundation.
Building AI on a governed foundation
Introducing AI Pods and GenAI Software Factories
How AI-augmented delivery squads and software factories add elastic capacity while humans stay in control of scope, risk and accountability.
Read More BlogBuilding the GenAI DNA of tomorrow's enterprise — the right way
A seven-dimension maturity framework for measuring GenAI readiness, finding gaps and adopting GenAI with scalability and governance.
Read More BlogRethinking enterprise analytics with GraphRAG and context-aware AI
Why data engineering stalls on missing context — and how GraphRAG makes data relationships explicit for AI.
Read MoreAIDLC-Based App Development Services: frequently asked questions
Scaling this across the enterprise?
Our Enterprise AI Services practice brings generative AI, agents and LLMs to production at scale — with the governance, platform and operating model to keep them reliable, compliant and cost-controlled.
Ready to build AI on a governed lifecycle?
Tell us the first use case and we will come back with a lifecycle assessment, the data and knowledge foundation it needs, and a gated pilot plan.
Talk to an AI specialist
Tell us where you are on your AI journey — a use case, a proof of concept, or a platform decision — and an AI consultant will come back with next steps and a rough shape for the engagement.