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 senior specialists, 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 engagement →CCaaS 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 engagement →Cloud 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 engagement →Data 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 engagement →Timelines are typical ranges and depend on scope, data access, and stakeholder availability. Compliance certifications and BAAs are scoped per engagement and per client.