Lane 01
Data Platform Execution
Modernize Databricks delivery with Needletail AI acceleration, FDE-led scoping, and governed lakehouse execution.
Recommended starting point
Databricks + Needletail AI Readiness Diagnostic
Packaged offers
Choose the right pSOLV entry point.
pSOLV helps enterprises modernize data platforms, adopt AI professionally, and execute governed delivery through AI-native FDE pods. Each lane starts narrow, stays human-reviewed, and expands only when the evidence supports it.
Offer posture
AI-native services is the delivery model. Databricks + Needletail AI remains a primary data-platform wedge, with AI Pro Adoption and FDE-led workflow execution as complementary commercial paths.
Three service lanes
Balanced entry points across platform, adoption, and execution.
Lane 02
AI Pro Adoption
Bring professional operating discipline to AI tools, coding agents, TokenOps, and review-gated enterprise adoption.
Recommended starting point
AI Usage & Credit Burn Diagnostic
Lane 03
FDE-Led Workflow Execution
Use AI-native FDE pods to turn manual, exception-heavy workflows into governed delivery patterns and reusable execution assets.
Recommended starting point
Workflow-to-Agent Pilot Factory
Where to begin
Choose by the problem in front of you.
If your pain is
Databricks backlog, migration, governance debt, or AI-ready data foundations
Start with
Databricks + Needletail AI Readiness Diagnostic
Data Platform Execution
If your pain is
AI tool sprawl, credit burn, coding-agent risk, or inconsistent review discipline
Start with
AI Usage & Credit Burn Diagnostic
AI Pro Adoption
If your pain is
Manual workflows, exception-heavy operations, or delivery work that needs an FDE-led pod
Start with
Workflow-to-Agent Pilot Factory
FDE-Led Workflow Execution
Offer design principles
Built to narrow risk before expanding scope.
Start with the clearest commercial wedge
Keep AI-assisted outputs human-reviewed
Use diagnostics before broad scope
Convert evidence into the next operating move
Data Platform Execution
Data Platform Execution offers
Offer 1
Databricks + Needletail AI Readiness Diagnostic
Turn a blurry lakehouse or AI-readiness problem into a scoped delivery path.
Trigger
Databricks backlog, migration uncertainty, governance debt, or unclear first slice.
Outputs
Current-state readout, blocker map, scoped recommendation, and next-step delivery path.
Offer 2
Needletail AI Pipeline Factory Sprint
Move one clear pipeline, ingestion, or migration slice into repeatable execution.
Trigger
Known pipeline bottleneck, manual build effort, brittle orchestration, or migration throughput pressure.
Outputs
Scoped design package, reviewed implementation artifacts, quality checks, and delivery checkpoints.
Offer 3
Unity Catalog + AI-Ready Governance Sprint
Tighten governance readiness so data-platform work can scale with trust.
Trigger
Lineage gaps, unclear ownership, access friction, observability gaps, or AI-ready control needs.
Outputs
Governance readiness assessment, control priorities, lineage recommendations, and execution sequence.
Offer 4
AI-Ready Lakehouse Data Product Pilot
Prove one governed data-product path tied to a concrete business workflow.
Trigger
A KPI, analytics workflow, or AI-ready data product needs proof before broader expansion.
Outputs
Pilot scope, delivery design, reviewed build artifacts, and expansion recommendation.
Offer 5
Managed LakehouseOps with Needletail AI
Sustain delivery throughput, review rigor, and governed planning after first proof.
Trigger
Ongoing backlog pressure, operational inconsistency, review fatigue, or need for a steadier cadence.
Outputs
Operating cadence, prioritized workflow backlog, review rituals, and next-wave planning.
AI Pro Adoption
AI Pro Adoption offers
Offer 1
AI Usage & Credit Burn Diagnostic
Create visibility into AI usage, credit burn, workflow exposure, and operating risk.
Trigger
AI tools are spreading faster than governance, cost visibility, and review discipline.
Outputs
Usage readout, cost and risk map, operating-model gaps, and prioritized next-step path.
Offer 2
AI Coding Agent Adoption Sprint
Move coding-agent usage from individual experimentation into reviewed team execution.
Trigger
Developers are using AI coding tools unevenly and leaders need team-level quality gates.
Outputs
Adoption playbook, team guardrails, workflow patterns, and review rituals.
Offer 3
Agentic SDLC Governance Blueprint
Define review gates, decision rights, and accountability for AI-assisted delivery.
Trigger
Coding agents or assistants are entering delivery workflows without a clear control model.
Outputs
Governance blueprint, workflow control map, review-gate model, and rollout sequence.
Offer 4
TokenOps / AI FinOps Control Tower
Create operating visibility for AI usage, credit burn, budget accountability, and review cadence.
Trigger
AI spend is growing, credits are consumed without clear ownership, or reporting is fragmented.
Outputs
TokenOps operating model, AI FinOps signal map, control cadence, and review packet structure.
FDE-Led Workflow Execution
FDE-Led Workflow Execution offers
Offer 1
Workflow-to-Agent Pilot Factory
Pilot one high-value workflow where AI assistance can be governed, reviewed, and measured.
Trigger
A manual or exception-heavy workflow is ready for a controlled agent-assisted pilot.
Outputs
Workflow anatomy, pilot scope, review-gated operating design, evidence packet, and scale recommendation.
Offer 2
FDE Delivery Pod
Put forward-deployed leadership around one governed execution priority.
Trigger
The business outcome is clear, but execution needs tighter workflow ownership and delivery rhythm.
Outputs
Pod structure, delivery cadence, stakeholder operating rhythm, reviewed artifacts, and escalation path.
Offer 3
AI Delivery Operating Model Design
Design the operating model that connects AI-assisted work, human review, governance, and delivery outcomes.
Trigger
Teams need a durable way to govern AI-native delivery across multiple workflows or pods.
Outputs
Operating model design, role map, review gates, delivery rituals, and rollout sequence.
Next step