01
Spec
Define the business outcome, workflow, risk profile, and success metrics before work expands.
Operating model
A governed delivery loop for AI-ready outcomes.
pSOLV turns enterprise data and AI work into scoped, reviewable, production-oriented delivery loops across strategy, architecture, build, validation, and adoption.
This page explains the overall operating system: how buyer pain becomes a spec, a plan, a reviewed build, a verified outcome, and a durable next step.
What stays explicit in the loop
Spec before sprawl
The workflow, risk profile, and success condition are clarified before the work expands.
Evidence before commitment
AI-assisted work becomes reviewable artifacts, not production assumptions, until validation and approval are visible.
Ownership before release
FDEs, architects, and delivery leads keep the path accountable from diagnostic through sprint, pilot, and production readiness.
Why the method matters
AI can compress analysis and code generation speed. Enterprise delivery still needs proof.
Faster generation alone does not make work safe, reviewable, or production-ready. Enterprises still need control, validation, ownership, and evidence before scope turns into committed outcomes.
That is why pSOLV runs the work as a governed operating system with explicit review, evidence, and ownership before scope turns into committed delivery.
Delivery lifecycle
Spec to ship, with explicit review gates between motion and commitment.
The loop is designed so buyer context stays connected to delivery reality all the way through. AI-assisted work can move quickly inside the loop, but commitments only advance when the artifacts are reviewed and the next move is clear.
02
Plan
Translate the problem into backlog, architecture, scope boundaries, guardrails, and acceptance criteria.
03
Build
Use AI-assisted and human-led implementation inside approved scope with repo discipline and delivery ownership.
04
Verify
Test, evaluate, validate outputs, and inspect data and code quality before commitments move forward.
05
Review
Apply FDE, architecture, security, and business review gates to confirm fitness and reduce delivery risk.
06
Ship
Define the production path, handoff, operating cadence, and adoption next steps for durable use.
Control surfaces
The loop works because control is designed in, not added late.
These are the places where strategy, delivery, and governance stay visible. They are what keep a diagnostic from drifting into vague implementation and keep AI-assisted output attached to real review points.
Strategy / Architecture
Intake + Control Plane
Agent-ready Spec / Execution Contract
Orchestration
Execution
System of Record
Validation / Evidence
Governance + Release
How this supports Databricks + Needletail AI
The operating loop turns diagnostics and acceleration into governed Databricks outcomes.
Diagnostics create scoped work. Needletail AI accelerates discovery, design, quality, lineage, and governance readiness. FDE pods review outputs and convert the work into Databricks outcomes with a clearer production path.
Diagnostic findings
Sprint scope
Architecture notes
Quality rules
Lineage view
Evidence pack
Production path
Human-in-the-loop governance
Automation can assist the work. Humans still approve the decisions that matter.
Automation can help with analysis, drafting, and implementation, but humans approve scope, architecture, customer commitments, production readiness, and merge or release decisions.
The value proposition is not replacing developers or operators. It is making delivery safer, more repeatable, and better evidenced.
Next step