Aatvi helps teams rescue AI-built software, modernize legacy systems, and deliver dependable applications. Start with the problem you need to solve: an application approaching launch, a codebase that needs review, or a workflow that needs to change.
We agree the scope and acceptance criteria before starting. An assessment delivers findings and a plan; an implementation engagement delivers the agreed software changes, verification and handover.
Start with your project
Tell us what you are building or changing, who uses it and what is getting in the way. We will discuss the goal, constraints and a useful first scope.
One agreed application and repository, covering up to three agreed critical user flows. Codebase size, stack, integrations and access determine the final review boundaries.
Paid, fixed-scope assessment. We agree a fixed fee and schedule after a free fit and scope discussion, before work starts. Implementation is quoted separately.
Implementation, active penetration testing and certification are outside this audit. A review does not guarantee that every defect has been found.
We agree what will be reviewed or built, how progress will be checked and who owns the next step. Findings from an assessment inform a separately agreed implementation scope.
We review workflows, code, data, risks, and constraints before proposing a build plan.
The roadmap prioritizes the few use cases, fixes, or product slices that can create measurable value.
Implementation includes tests, deployment shape, access boundaries, monitoring, and handoff notes.
We use usage data, incidents, evals, and feedback to decide what should be hardened next.
Production-first engineering across Next.js, TypeScript, Cloudflare, data systems, and AI workflows.
Security, dependency hygiene, evals, observability, and human review built into the delivery path.
Written scope, explicit limitations and agreed acceptance checks for each engagement.
Founders with AI-built prototypes that need to become real products.
Product and engineering teams adding LLM, RAG, agent, or automation features.
Leaders who need an implementation roadmap grounded in their data and workflows.
Operational teams modernizing ERP, analytics, or internal software without a risky rewrite.
Teams looking for a generic AI trend report without implementation ownership.
Projects that require invented proof, fake urgency, or unrealistic launch claims.
One-off chatbot builds with no data, workflow, security, or adoption plan.
Aatvi provides software engineering services, including AI Code Rescue, software and product development, legacy modernization, AI consulting, AI agents, and data engineering. We scope assessment and implementation work to the needs of each project.
We build. Strategy work is useful only when it leads to an implementation path, a working prototype, or a production-ready system your team can maintain.
Yes. Several services start with an existing product, legacy system, or AI-generated prototype. We review what is there, identify what is risky, and decide whether to stabilize, extend, or rebuild a narrow slice.
Start with a free fit and scope discussion. We agree the review or implementation boundaries, access requirements, fee and schedule before paid work begins. An audit and any subsequent implementation are scoped separately.
The service pages are backed by practical checklists, sample audit structure, and buyer guides so searchers and AI answer systems can inspect how Aatvi thinks about production risk.
How replay, idempotency and explicit control of uncertainty help teams investigate production failures.
Read guideOperations perspectiveA discussion of the gap between recording transactions and connecting operational signals across departments.
Read guideFramework and worksheetAn evidence-led review framework and blank worksheet for AI-built apps: auth, secrets, dependencies, data, tests, deployment, and ownership.
Read guideIllustrative exampleAn illustrative audit report showing launch blockers, important fixes, and later cleanup. Not a client result.
Read guideGuideA practical sequence for founders and teams turning a fast AI-built demo into production software.
Read guideExplainerA plain-English definition of AI slop, why it appears, and when the right fix is audit, rescue, or rewrite.
Read guideBring the codebase, workflow, or product idea that is closest to real business pressure. We will help decide whether it needs an audit, roadmap, build sprint, or a narrower first step.
