Know What You're Getting From Your Vendor — Before It's Too Late
Independent, AI-accelerated software analysis and verification to confirm deliverables match your requirements, KPIs, quality standards, and security expectations—with expert validation and clear, actionable findings.
Misalignment Risk
PROBLEM
Deliverables can drift from business goals, acceptance criteria, and iteration KPIs.
IMPACT
You discover gaps late, after timeline, budget, and stakeholder trust are already impacted.
Progress Visibility
PROBLEM
Effort reporting, estimations, and "% complete" can be hard to validate objectively.
IMPACT
Delivery risk accumulates quietly until deadlines slip or scope expands.
Quality & Security
PROBLEM
Code quality, test coverage, and security are often reported inconsistently.
IMPACT
Technical debt grows, defects escape, and security issues surface after release, when fixes cost more.
AI + Human Expertise
NexDevTech provides an independent verification layer for third-party software development. AI accelerates deep analysis across requirements, backlog, code, tests, and security practices—while senior experts validate results and translate findings into clear corrective actions.
Independent Assurance
● Verification performed from the client's perspective (requirements, KPIs, acceptance criteria).
● Objective "expected vs actual" delta reporting you can use in steering, governance, and vendor conversations.
● Can operate as an advisory proxy-PO when requirements are incomplete or inconsistent.
Speed With Depth
● Rapid, parallelized analysis across multiple dimensions (functional alignment, code quality, QA, security).
● Earlier detection of drift—so corrections are cheaper and less disruptive.
● Faster decision-making for acceptance, release readiness, or remediation planning.
Actionable Outputs
● Clear, prioritized gap list with evidence and traceability.
● Risk and cost implications tied to delivery timeline and rework probability.
● Ready-to-execute remediation backlog (tickets/user stories) and quality/security recommendations.
Stage #1:
Intake, Data Prep & Context Mapping
What We Do
● Review requirements/specs, iteration or release goals, KPIs, backlog items, and key stakeholder expectations.
● Ingest repository access (or code drops), test artifacts, CI/CD outputs (optional), and relevant documentation.
● Normalize inputs and structure them for analysis (including traceability mapping foundations).
What You Get
● Intake summary + analysis plan (scope, systems, artifacts, constraints).
● Data readiness report (what's available, what's missing, what limits certainty).
Stage #2:
AI Orchestration & Multi-Dimensional Analysis
What We Do
● Build a searchable knowledge layer over requirements, backlog, and code (e.g., embeddings + vector search).
● Use an orchestrated set of specialized AI agents (e.g., PO/BA alignment agent, QA agent, cyber security agent, architecture/code-quality agent).
● Evaluate: functional coverage, spec compliance, code quality patterns, test strategy/coverage signals, and security findings.
What You Get
● Early findings with traceability anchors (where each conclusion comes from).
● A structured list of candidate deltas and risk indicators.
Stage #3:
Expert Review, Verification & "No-Hallucination" Controls
What We Do
● Senior experts verify AI-flagged deltas against repository evidence, requirements, and stakeholder intent.
● Confirm severity, reproduce key issues (when applicable), and remove low-confidence claims.
● Convert insights into practical recommendations aligned with delivery reality.
What You Get
● Verified delta set with confidence levels and supporting evidence references.
● Clear prioritization (blockers vs important vs opportunistic improvements).
Stage #4:
Reporting, Remediation Backlog & Executive Readout
What We Do
● Produce a boardroom-ready summary and a delivery-team-ready action pack.
● Create remediation user stories / tasks and a recommended verification cadence (ongoing governance option).
● Optional: run a follow-up verification after vendor fixes to confirm closure.
What You Get
● Verification report + release readiness recommendation (accept / conditional accept / reject / remediate).
● Remediation backlog (tickets/user stories) and governance checkpoints.
Expected vs Actual Delta Report
(Traceable)
● Gaps between requirements/KPIs and delivered functionality or behavior.
● Evidence-based references (requirements/backlog → code/tests).
● Severity and recommended resolution path.
Quality Engineering Assessment
● Code maintainability and technical debt signals (hotspots, complexity, duplication patterns).
● Test strategy review and coverage indicators (unit/integration/e2e posture, critical path coverage).
● CI/CD and release readiness signals (optional if artifacts available).
Security & OWASP-Aligned Review (As Applicable)
● High-impact security findings and insecure patterns.
● Verification of security practices (dependency hygiene, secrets handling, authn/authz considerations).
● Recommended fixes and prioritization aligned to risk.
Estimation &
Progress Credibility Review
● Alignment between reported progress and observable outputs.
● Risk flags for timeline/cost overruns (scope drift, rework likelihood, missing acceptance coverage).
● Practical recommendations for better predictability.
Remediation Backlog
(Ready for Jira/Azure DevOps)
● Prioritized user stories/tasks for closing gaps.
● Acceptance criteria and suggested tests.
● Suggested owners (vendor vs internal) and sequencing.
01
Reduced Vendor Risk
● Catch misalignment earlier—before it becomes expensive rework.
● Increase acceptance confidence and reduce surprise defects.
02
Higher Predictability
● Clear visibility into what's truly done vs what's claimed.
● Better control over release readiness decisions and timeline confidence.
03
Improved Quality & Security Posture
● Practical, prioritized improvements tied to real risk and delivery impact.
● Lower long-term maintenance costs by preventing hidden debt.
04
Stronger Governance Without Micromanagement
● Objective verification layer that supports governance, procurement, and delivery leadership.
● Enables constructive vendor conversations with evidence instead of opinions.
Data Isolation
● Scoped repo/doc access (least privilege).
● Segmented environments per engagement.
● Minimal ingestion (only required artifacts)
Flexible Deployment
BUSINESS: API-based (faster, cost-efficient, security managed by provider)
PRIVATE: Self-hosted (maximum control, dedicated infrastructure)
No Training On Your Code
● Not used to train public models.
● Client-controlled retention and deletion.
● No sharing beyond approved parties