Talentica > Startups > DevX AI Pods
DevX AI Pods
DevX pods bring senior developers and product-aware proprietary agents together to ship features twice as fast, with outcome-based pricing and guaranteed quality across every deliverable.
Reduction in cycle time
Lower cost per shipped feature
Features meeting the acceptance criteria
THE PROBLEM
AI speeds up coding, but moves the challenges elsewhere
DevX AI Pods work across SDLC, not just code generation
AI generates code without fully understanding the product or existing codebase.
More AI generated code means more PRs, reviews, tests and validations to handle.
Scope creep, over-engineering and security issues become harder to detect.
Selecting, adopting, and standardizing the right AI tools across SDLC becomes a new challenge.
THE CORE IDEAS
Three layers, one shared context
All three layers share one foundation: your product context. Tools turn it into capabilities, agents execute against it, experts verify the work — the outcome is a verified product.
Verified Products
01Tools
A platform layer that embeds our product engineering experience into reusable capabilities.
Dependency mapping, semantic indexing, impact analysis
Epic decomposition, tech spec generation, PR reviews, RCA
Preflight filtering, ADRs, design by contract, shift-left testing, traceability, NFRs
Security checks, audit trails, CI/CD gates
02Agents
Lightweight task-execution agents that orchestrate engineering workflows using the platform tools.
Invoke the tools using the product context
End to end: epic grooming, decomposition, writing code and tests
PRs, unit tests, epic specs, tech specs
Application logs, code instrumentation
03Expert verification
Human experts carry the mental model of what’s being built. They set direction, steer execution and stand behind every output
Product and technical specs
Architectural decisions, workflow refinement
Agent execution, code, PRs, tests
Edge cases, judgment calls
Product Context
- Specifications
- Existing codebase
- Test Suites
- Architecture document
- CI/CD pipelines
- Logs
What we offer
Pods designed around your work
Build complete product features without having to define every detail upfront. We first get familiar with your product by connecting to your codebase, documentation, and work management tools. Each feature is then sized before development starts.
Product-aware setup, feature estimates, end-to-end feature delivery
Fixed price for clearly defined features, or purchase DevXUnits for work that is still evolving. Units are used based on the size of each feature.
Turn your product idea to a working MVP with clear scope, cost, and delivery timelines. We use AI-assisted discovery to finalize the PRD and estimate the agreed scope before development begins.
Product-aware setup, feature estimates, end-to-end feature delivery
Fixed price for clearly defined features, or purchase DevXUnits for work that is still evolving. Units are used based on the size of each feature.
Increase test coverage, review code quality, or get to the root of recurring production issues with a pod focused on clear engineering outcomes.
Improved test coverage, code audit reports, and root-cause analysis.
Outcome-based pricing, based on your codebase size and the quality target you want to achieve.
Modernize, migrate or transform an existing product with a scope grounded in your current codebase. We derive and validate the PRD from the code, estimate the transformation, and execute against an agreed outcome.
Code-derived PRD, effort estimate, transformed product
Fixed price for the verified scope.
CCCR Standards
Four dimensions.
Verified outcomes.
Is the architecture, code, and output technically sound?
Does the AI deliver stable, repeatable quality across runs and iterations?
Are all requirements and acceptance criteria covered?
Does the final output solve the intended user or business need?