Application Development & Maintenance

AI-Native SDLC

A Development Lifecycle Built for the Age of AI

Traditional software development practices were designed for a pre-AI world. We've rethought every phase of the SDLC to leverage AI-assisted tools — while maintaining the rigor that enterprise applications demand.

Phase 1

AI-Augmented Requirements Engineering

Use LLMs to synthesize requirements from stakeholder interviews, existing documentation, and domain knowledge. Auto-generate user stories, acceptance criteria, and edge case identification. Eliminate ambiguity before a line of code is written.

Phase 2

Architecture & Prompt Design

Design the AI application architecture: backend APIs (FastAPI/Python), frontend (React/Next.js), data pipelines, vector stores, and the prompt engineering layer. Prompts are treated as first-class code assets — versioned, tested, and reviewed.

Phase 3

Claude Code-Assisted Development

Leverage Claude Code for accelerated implementation: code generation, refactoring, test writing, code review, and documentation. Our engineers pair with AI tools to deliver 2–3x faster than traditional development while maintaining code quality standards.

Phase 4

AI-Augmented Testing

Automated unit and integration tests generated alongside code. LLM-based prompt regression testing to detect model output drift. Adversarial testing for AI-specific vulnerabilities. Performance benchmarking for inference latency and throughput.

Phase 5

Deployment & Operations

Container-based deployment via CI/CD pipelines. Blue-green deployments for zero-downtime model updates. Automated rollback on quality metric degradation. Ongoing monitoring of model drift, cost per request, and business KPIs.

Technology Stack

Modern, Battle-Tested Technologies

Backend

Python with FastAPI or Django REST Framework. Async-first architecture for high-throughput LLM workloads. Celery for background task queues. Redis for caching and session management.

Frontend

React or Next.js for interactive AI interfaces. TypeScript for type safety. TailwindCSS for rapid UI development. Streaming response rendering for real-time LLM output display.

Infrastructure

Docker containers on ECS, Cloud Run, or Azure Container Apps. Terraform or Pulumi for infrastructure as code. GitHub Actions or AWS CodePipeline for CI/CD. Datadog or CloudWatch for observability.

Best Practices

Maintenance & Operations FAQ

How do you handle model version upgrades?

We maintain a model version registry and run automated regression tests against your golden test set before any model upgrade. Upgrades are staged: first to a canary slice of traffic, then gradually rolled out while monitoring quality metrics. Rollback is automated if degradation is detected.

How do you detect and respond to model drift?

We monitor output quality using automated LLM-as-judge evaluation on a representative sample of production queries. Statistical control charts detect drift from established baselines. Alerts trigger on quality degradation, enabling rapid investigation and prompt adjustment.

How are prompts versioned and managed?

Prompts are stored in a prompt registry (LangSmith, Weights & Biases, or a custom solution) with full version history, A/B testing support, and rollback capability. Changes to production prompts go through a peer review process and automated regression testing before deployment.

What SLAs do you offer for production AI applications?

Our standard maintenance engagement includes 99.9% uptime SLA (excluding underlying LLM provider outages), P1 incident response within 1 hour, P2 within 4 hours, and P3 within 1 business day. We provide monthly reliability and cost reports.