Fixed-scope engagements with a clear start, finish, and takeaway.
Four repeatable ways to start working with PEXIVA — each scoped to a defined timeline, run by industry practitioners, and ending with something concrete your team can act on. Every engagement is tailored to your context; the structure below is the starting point, not a fixed package.
Pick the engagement that matches where you are.
AI Foundry Sprint
A focused sprint to take a high-value AI use case from idea to a working, governed prototype — with a clear path to production. Built around AWS Bedrock and Anthropic Claude, with RAG or agentic patterns as the workload requires.
How it runs
- Frame. Pick one use case, define success metrics, and confirm data access and guardrails.
- Build. Stand up a working prototype — retrieval, prompting, evaluation, and a thin UI or API.
- Hand off. Document the architecture, costs, risks, and a production roadmap your team can own.
What you walk away with
- A working, demoable prototype on your data
- An evaluation harness and prompt/RAG baseline
- A responsible-AI and data-handling review
- A costed production roadmap and next-step options
Best fit: SME and mid-market teams with a concrete AI idea who want proof before committing to a full build.
Scope this engagementCCaaS Modernization Blueprint
A vendor-neutral assessment of your contact-center estate and a right-sized modernization plan — across Amazon Connect, Genesys, Twilio, or Five9 — with AI assist scoped only where the economics work.
How it runs
- Assess. Map current routing, channels, integrations, and cost drivers against your CX goals.
- Design. Define the target architecture, migration approach, and where AI agent-assist pays off.
- Plan. Deliver a phased blueprint with effort, sequencing, and risk for each step.
What you walk away with
- A current-state assessment of your contact-center estate
- A vendor-neutral target architecture
- An AI-assist opportunity map with a cost lens
- A phased migration blueprint with effort and risk
Best fit: Heads of CX or contact-center operations weighing a platform move or AI-assist rollout.
Scope this engagementCloud Cost & Compliance Review
A fast, focused review of your cloud spend and control posture across AWS, Azure, or Google Cloud — surfacing FinOps savings and compliance gaps aligned to the frameworks that apply to you.
How it runs
- Inventory. Pull spend, usage, and configuration data across accounts and environments.
- Analyze. Identify waste, right-sizing opportunities, and control gaps against target frameworks.
- Prioritize. Rank actions by savings and risk reduction, with quick wins called out.
What you walk away with
- A spend breakdown with right-sizing and savings opportunities
- A control-posture review aligned to your target frameworks
- A prioritized, effort-ranked action list
- Quick wins you can action immediately
Best fit: Finance and platform leaders who suspect cloud spend or control drift and want a fast, honest read.
Scope this engagementData Platform Diagnostic
A diagnostic of your data platform and pipelines — across Snowflake, Databricks, BigQuery, or Microsoft Fabric — that identifies what is blocking reliable analytics, governance, and AI readiness.
How it runs
- Map. Trace sources, pipelines, models, and consumers to see how data actually flows.
- Diagnose. Find reliability, cost, governance, and AI-readiness gaps in the current setup.
- Recommend. Lay out a pragmatic modernization path with sequencing and trade-offs.
What you walk away with
- A data-flow map across sources, pipelines, and consumers
- A gap analysis on reliability, cost, and governance
- An AI-readiness assessment of your data foundation
- A sequenced modernization roadmap
Best fit: Data and analytics leaders who need a clear-eyed read on what to fix before scaling AI.
Scope this engagementTimelines are typical ranges and depend on scope, data access, and stakeholder availability. Compliance certifications and BAAs are scoped per engagement and per client.