Accelerated AI-Native SDLC for Enterprise Engineering

We review the current process, task volume, delays, exceptions, and costs with its business owner. The first pilot should have a clear purpose, accessible data, and measurable acceptance criteria. We prioritize a manageable workflow that can demonstrate useful results without disrupting core operations.

We compare the current cost of completing the work with the full proposed operating cost. This includes model calls, integration, hosting, human review, support, and maintenance. Pilot results test the assumptions. Savings are evaluated from evidence rather than inferred from token prices alone.

No. We choose chat, forms, and dashboards according to the task. Flexible requests can suit conversation, while precise inputs and comparisons often need structured screens. Important actions retain agreed approval steps. Users should be able to correct mistakes, understand status, and reach human support.

We agree on monitoring, support ownership, recovery procedures, and review cycles before release. The operating view tracks task quality, failures, response time, and cost. Agent Swarm capabilities and integrations are scoped and verified for each engagement, with human escalation where the workflow needs it.

It is an evidence-based inspection of an agreed cloud workload. WTA reviews architecture and operating practices against business requirements, then prioritizes improvements across reliability, security, cost, operations, and performance. The output is a practical decision and remediation plan.

Only when implementation is included in the agreed scope. The assessment identifies and prioritizes changes. Remediation, retesting, and ongoing support can be commissioned separately or included as defined deliverables, with ownership and acceptance criteria agreed before work begins.

Yes. We agree on appropriate access and evidence collection with your team. Architecture documents, configuration exports, monitoring, spending records, and stakeholder discussions can support the review. Production changes and disruptive tests require a separately agreed plan.

A cloud architecture review examines how an existing workload is designed and operated. AI readiness examines whether a particular AI initiative has the business case, data, systems, people, and controls needed to proceed. The assessments can inform one another.

It evaluates whether a particular AI initiative has a useful business case and the data, systems, people, and controls needed to work. WTA identifies prerequisites, prioritizes opportunities, and defines a pilot with accountable ownership and measurable acceptance criteria.

No. We can begin with business priorities and current workflows, then compare possible use cases. The assessment helps identify where AI may add value, where conventional automation is sufficient, and where process or data improvements should come first.

No. It identifies relevant governance gaps and tests the business assumptions behind a proposed pilot. Compliance responsibilities remain with the organization and its advisers. Costs and benefits must be validated against actual workflow performance before wider deployment.

Your team receives a prioritized plan and decides whether to proceed. The next step may be resolving data or process gaps, running a scoped pilot, or deferring the initiative. Implementation and ongoing support are agreed separately with clear responsibilities and acceptance criteria.

It is WTA’s service for building coordinated agent workflows around an agreed business process. Jev supplies structured decisions; Microsoft Agent Framework coordinates agents and code that perform the work. The engagement defines integrations, controls, evaluation, and operational ownership.

Jev is TypeSafe AI’s decision model. It returns structured choices, scores, and probability judgments. Other models or specialist agents can generate text and execute permitted tools. A confidence signal does not guarantee correctness, so decisions are evaluated and checked before consequential actions.

We build a custom API integration that calls Jev and validates its response. Workflow code routes work to the permitted executor or human review. Jev is accessed through an external TypeSafe AI API; deployment, data handling, and access requirements are reviewed for your engagement.

Yes. We define approval gates, reviewers, evidence, and escalation routes. Important writes are checked before execution. Where recovery is required, we configure checkpoint storage, bounded retries, and idempotent writes, then test interruption and failure scenarios.

The agreed scope covers a workflow design, agent and decision contracts, working integrations, evaluation results, and an operating runbook. Pilot measures can include completion quality, exception rates, review effort, latency, and cost per completed task. Wider rollout follows the evidence rather than a promised savings percentage.