Talentica > Enterprise > DevX AI Pods - enterprise
DevX AI Pods for Enterprises
Accelerate features, modernization, testing, and new builds with product-aware AI agents. Talentica engineers guide and verify outcomes to reduce rework, simplify reviews, and speed delivery.
Reduction in cycle time
Lower cost per shipped feature
Features meeting requirements
THE PROBLEM
AI can write code. But can it make the right engineering decisions?
AI agents need product context and engineering judgment to avoid complexity, duplication, and verification bottlenecks.
AI agents can introduce complex solutions where simpler approaches would work, increasing costs and long-term maintenance.
Across large codebases, agents can duplicate existing logic, overlook dependencies, and introduce inconsistent implementations.
Large AI-generated changes are harder to verify, increasing review effort and the risk of missed requirements, security issues, and architectural inconsistencies.
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 product
01Tool
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
Unified Product Context
- Specifications
- Existing codebase
- Test Suites
- Architecture document
- CI/CD pipelines
- Logs
What we offer
Four pods. Four ways to accelerate delivery.
DevX AI Pods align AI-powered engineering with your enterprise roadmap. Choose from flexible, fixed-price, or outcome-based models depending on the work.
Ship more of your roadmap. Accelerate feature development with AI agents that understand your codebase, reuse existing functionality, and deliver expert-verified changes.
Complex features, roadmap acceleration, AI capabilities, and ongoing product evolution.
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.
Take new initiatives to production. Build new enterprise products, platforms, and capabilities with AI-accelerated execution and expert-led engineering decisions.
New enterprise products, internal platforms, AI-native applications, and strategic initiatives.
Fixed price for an approved scope, with changes estimated and approved separately.
Modernize without losing what already works. Reconstruct existing business logic, architecture, and dependencies before modernizing in verified increments.
Legacy modernization, framework upgrades, cloud migration, and platform re-engineering.
Fixed price based on code-derived specifications and a verified transformation scope.
Turn quality into an engineering outcome. Expand test coverage, identify quality gaps, and accelerate root-cause analysis with expert-verified results.
Test coverage, code audits, regression reduction, root-cause analysis, and quality improvement.
Outcome-based pricing determined by codebase size (LOC) and agreed quality targets.
CCCR Standards
Engineering quality
verified at every stage
Is it technically correct and does it behave as intended?
Does it align with the existing product, architecture, engineering standards, and prior decisions?
Does it address the requirements, acceptance criteria, dependencies, edge cases, and necessary tests?
Does it solve the intended product or business problem without unnecessary scope?
What Sets Us Apart
Beyond coding agents.
Built for product engineering.
Unified Product Context
Our Archaeology agent connects code, requirements, architecture, dependencies, and tests into a knowledge graph, helping AI agents reuse existing functionality and make informed changes across complex codebases.
Right-Sized Engineering
Experience from building 200+ products guides feature scoping, architecture decisions, and technical trade-offs to prevent over-engineering and unnecessary technical debt.
Product-Aware AI Execution
Specialized agents use shared product context to execute engineering tasks, from feature development and testing to modernization and root-cause analysis.
Expert-Verified Outcomes
Talentica engineers validate specifications, execution plans, and outputs at defined quality gates using CCCR: Correctness, Consistency, Completeness, and Relevance.
LET'S BUILD
Don't add another AI tool. Move the roadmap
DevX AI Pods turn product context into AI-powered engineering outcomes.