WTA began as a small crew of builders obsessed with craft and outcomes. In our first year, we sat beside operators, support agents, and analysts, mapping friction in their day‑to‑day work. We shipped tiny, testable improvements, instrumented everything, and learned to write guardrails before writing features. That discipline—profound discovery, short iterations, measurable impact, and human‑in‑the‑loop decisions—became the DNA of our pods and still shapes how we plan, prototype, and bring AI to production today.
As customers’ ambitions grew, we shifted from one-off builds to platform thinking. We standardized agentic orchestration to coordinate services, added RAG over governed data for trustworthy answers, and moved monoliths toward event-driven microservices. CI/CD, MLOps, evaluation suites, and golden datasets keep models and prompts under version control with clear rollback paths. We invest in tools that remove toil—code generation, automated tests, and observability—so velocity climbs while risk goes down.
Today, we partner with Fortune 500 and high-growth leaders to modernize core systems and launch AI-native experiences at scale. We build copilots that deflect tickets and speed decisions, autonomous runbooks that self-heal services, and decision-intelligence layers that move revenue and cost KPIs. Every engagement uses the same principles—security by design, auditability, and CMMI-aligned governance—so launches are predictable and teams can own the platform long after go-live.
Seasoned leaders who pair enterprise governance with product‑speed execution across AI, platforms, and delivery.
A simple set of principles that guide how we build: outcomes, craft, integrity, collaboration, learning, and lasting impact.
Start with business-unparalleled outcomes; design backward from user value for greater deliveries.
Prototype fast, learn faster, and scale what works—with user testing, guardrails‑first design, and evidence‑based decisions in every sprint.
Ship with standards, security, accessibility, and clear audits, so every release is reliable, traceable, and compliant across environments.
Cross‑functional pods solve end‑to‑end, product, design, data, and engineering aligned on outcomes, ownership, and clear communication.
Upskill constantly; share playbooks; improve every sprint via retros, peer reviews, and labs that turn lessons into reusable accelerators.
Build systems that last—resilient, cost‑aware, and responsible, with observability, performance tuning, and ethical and environmental guardrails.
We are local where it matters and global where it helps. Distributed pods collaborate across time zones while anchoring work close to your teams and data, offering speed, coverage, and context, without compromising governance or support.