Work That Speaks for Itself

Challenge

Build a production-grade AI assistant accessible entirely via SMS — no app, no login — that turns unstructured customer input (text descriptions, photos, video walkthroughs, voice notes) into structured business artifacts (estimates, bills of materials, PDF deliverables). Required end-to-end engineering across telecom compliance, multimodal AI, and document generation.

Solution

Atlas Tech Group designed and built a multi-channel AI assistant platform using AI-accelerated engineering. The system combines Twilio A2P SMS infrastructure with LLM-driven content understanding, vision models for photo and video scene analysis, structured estimate generation with bill-of-materials extraction, and automated PDF document creation. Built on AWS with full Twilio Campaign Registry compliance (consent flows, opt-in/opt-out handling, 10DLC registration).

Challenge

A large enterprise operating on a legacy ERP (1990s-era technology stack, SQL Server system of record) needed to modernize without disrupting day-to-day operations. Manual data reconciliation between vendor catalogs, internal systems, and the legacy ERP was consuming hundreds of hours of staff time. Prior modernization attempts using traditional methods had stalled. Requirements: maintain pricing accuracy parity with the legacy system, preserve SQL Server as system of record, deliver progressive replacement without big-bang cutover risk, and enable partner integrations on a modern API surface.

Solution

Atlas Tech Group's principal designed and led implementation of an API-first strangler-fig modernization program. Multiple microservices were designed, built, and deployed across roughly nine months, progressively replacing legacy ERP modules. Key elements included OpenAPI 3.1 specifications with self-documenting endpoints and two-tier service-token authentication (admin and partner-scoped) with CI-enforced auth lint to prevent regression; pricing-engine fidelity validated against legacy stored procedures with automated regression suites (all worksheet scenarios within 1% accuracy); a multi-agent AI engineering fleet collaborating on different codebases with typed memory, enforced safety rules, branch protection, and human approval on every merge; an AI-assisted catalog reconciliation pipeline combining deterministic pre-filtering with targeted LLM ranking; and quantified per-operation AI cost engineering for predictable scaling.

Challenge

A technology organization needed to determine which AI-assisted development tools (Cursor, Cline, GitHub Copilot, v0) would deliver the most value for their engineering team — with a structured framework for measuring impact, not just running demos.

Solution

Atlas Tech Group designed and executed a structured evaluation framework across real engineering tasks: code generation quality, architectural reasoning, test coverage generation, refactoring capability, and developer experience. Each tool was assessed against the same task set with consistent scoring criteria.

Challenge

A mid-sized enterprise needed to migrate a legacy on-premises application stack to AWS while maintaining 99.9% uptime requirements, meeting compliance obligations, and enabling a team with limited cloud-native experience.

Solution

Atlas Tech Group led architecture design, infrastructure-as-code development (Terraform), migration sequencing, and knowledge transfer. The engagement included a cloud readiness assessment, data migration strategy, blue-green deployment implementation, and runbook development for the operations team.